@unpublished{GruebelHuberHuembsetal.2021, author = {Gr{\"u}bel, Julia and Huber, Olivier and H{\"u}mbs, Lukas and Klimm, Max and Schmidt, Martin and Schwartz, Alexandra}, title = {Nonconvex Equilibrium Models for Energy Markets: Exploiting Price Information to Determine the Existence of an Equilibrium}, pages = {29}, year = {2021}, abstract = {Motivated by examples from the energy sector, we consider market equilibrium problems (MEPs) involving players with nonconvex strategy spaces or objective functions, where the latter are assumed to be linear in market prices. We propose an algorithm that determines if an equilibrium of such an MEP exists and that computes an equilibrium in case of existence. Three key prerequisites have to be met. First, appropriate bounds on market prices have to be derived from necessary optimality conditions of some players. Second, a technical assumption is required for those prices that are not uniquely determined by the derived bounds. Third, nonconvex optimization problems have to be solved to global optimality. We test the algorithm on well-known instances from the power and gas literature that meet these three prerequisites. There, nonconvexities arise from considering the transmission system operator as an additional player besides producers and consumers who, e.g., switches lines or faces nonlinear physical laws. Our numerical results indicate that equilibria often exist, especially for the case of continuous nonconvexities in the context of gas market problems.}, language = {en} } @phdthesis{Plein2021, author = {Plein, Fr{\"a}nk}, title = {When Bilevel Optimization Meets Gas Networks: Feasibility of Bookings in the European Entry-Exit Gas Market. Computational Complexity Results and Bilevel Optimization Approaches}, year = {2021}, abstract = {Transport and trade of gas are decoupled after the liberalization of the European gas markets, which are now organized as so-called entry-exit systems. At the core of this market system are bookings and nominations, two special capacity-right contracts that grant traders access to the gas network. The latter is operated by a separate entity, known as the transmission system operator (TSO), who is in charge of the transport of gas from entry to exit nodes. In the mid to long term, traders sign a booking contract with the TSO to obtain injection and withdrawal capacities at entry and exit nodes, respectively. On a day-ahead basis, they then nominate within these booked capacities a balanced load flow of the planned amounts of gas to be injected into and withdrawn from the network the next day. The key property is that by signing a booking contract, the TSO is obliged to guarantee transportability for all balanced load flows in compliance with the booked capacities. To assess the feasibility of a booking, it is therefore necessary to check the feasibility of infinitely many nominations. As a result, deciding if a booking is feasible is a challenging mathematical problem, which we investigate in this dissertation. Our results range from passive networks, consisting of pipes only, to active networks, containing controllable elements to influence gas flows. Since the study of the latter naturally leads to a bilevel framework, we first consider some more general properties of bilevel optimization. For the case of linear bilevel optimization, we consider the hardness of validating the correctness of big-Ms often used in solving these problems via a single-level reformulation. We also derive a family of valid inequalities to be used in a bilevel-tailored branch-and-cut algorithm as a big-M-free alternative. We then turn to the study of feasible bookings. First, we present our results on passive networks, for which bilevel approaches are not required. A characterization of feasible bookings on passive networks is derived in terms of a finite set of nominations. While computing these nominations is a difficult task in general, we present polynomial complexity results for the special cases of tree-shaped or single-cycle passive networks. Finally, we consider networks with linearly modeled active elements. After obtaining a bilevel optimization model that allows us to determine the feasibility of a booking in this case, we derive various single-level reformulations to solve the problem. In addition, we obtain novel characterizations of feasible bookings on active networks, which generalize our characterization in the passive case. The performance of these various approaches is compared in a case study on two networks from the literature, one of which is a simplified version of the Greek gas network.}, language = {en} } @unpublished{DeSantisdeVriesSchmidtetal.2021, author = {De Santis, Marianna and de Vries, Sven and Schmidt, Martin and Winkel, Lukas}, title = {A Penalty Branch-and-Bound Method for Mixed-Binary Linear Complementarity Problems}, pages = {24}, year = {2021}, abstract = {Linear complementarity problems (LCPs) are an important modeling tool for many practically relevant situations but also have many important applications in mathematics itself. Although the continuous version of the problem is extremely well studied, much less is known about mixed-integer LCPs (MILCPs) in which some variables have to be integer-valued in a solution. In particular, almost no tailored algorithms are known besides reformulations of the problem that allow to apply general-purpose mixed-integer linear programming solvers. In this paper, we present, theoretically analyze, enhance, and test a novel branch-and-bound method for MILCPs. The main property of this method is that we do not ``branch'' on constraints as usual but by adding suitably chosen penalty terms to the objective function. By doing so, we can either provably compute an MILCP solution if one exists or compute an approximate solution that minimizes an infeasibility measure combining integrality and complementarity conditions. We enhance the method by MILCP-tailored valid inequalities, node selection strategies, branching rules, and warmstarting techniques. The resulting algorithm is shown to clearly outperform two benchmark approaches from the literature.}, language = {en} } @article{KrugLeugeringMartinetal.2021, author = {Krug, Richard and Leugering, G{\"u}nter and Martin, Alexander and Schmidt, Martin and Weninger, Dieter}, title = {Time-Domain Decomposition for Optimal Control Problems Governed by Semilinear Hyperbolic Systems with Mixed Two-Point Boundary Conditions}, series = {Control and Cybernetics}, journal = {Control and Cybernetics}, pages = {20}, year = {2021}, abstract = {In this article, we continue our work (Krug et al., 2021) on time-domain decomposition of optimal control problems for systems of semilinear hyperbolic equations in that we now consider mixed two-point boundary value problems and provide an in-depth well-posedness analysis. The more general boundary conditions significantly enlarge the scope of applications, e.g., to hyperbolic problems on metric graphs with cycles. We design an iterative method based on the optimality systems that can be interpreted as a decomposition method for the original optimal control problem into virtual control problems on smaller time domains.}, language = {en} } @article{KleinertMannsSchmidtetal.2021, author = {Kleinert, Thomas and Manns, Julian and Schmidt, Martin and Weninger, Dieter}, title = {Presolving Linear Bilevel Optimization Problems}, series = {EURO Journal on Computational Optimization}, journal = {EURO Journal on Computational Optimization}, number = {9}, doi = {10.1016/j.ejco.2021.100020}, year = {2021}, abstract = {Linear bilevel optimization problems are known to be strongly NP-hard and the computational techniques to solve these problems are often motivated by techniques from single-level mixed-integer optimization. Thus, during the last years and decades many branch-and-bound methods, cutting planes, or heuristics have been proposed. On the other hand, there is almost no literature on presolving linear bilevel problems although presolve is a very important ingredient in state-of-the-art mixed-integer optimization solvers. In this paper, we carry over standard presolve techniques from single-level optimization to bilevel problems and show that this needs to be done with great caution since a naive application of well-known techniques does often not lead to correctly presolved bilevel models. Our numerical study shows that presolve can also be very beneficial for bilevel problems but also highlights that these methods have a more heterogeneous effect on the solution process compared to what is known from single-level optimization. As a side result, our numerical experiments reveal that there is an urgent need for better and more heterogeneous test instance libraries to further propel the field of computational bilevel optimization.}, language = {en} } @article{KrugLeugeringMartinetal.2020, author = {Krug, Richard and Leugering, G{\"u}nter and Martin, Alexander and Schmidt, Martin and Weninger, Dieter}, title = {Time-Domain Decomposition for Optimal Control Problems Governed by Semilinear Hyperbolic Systems}, series = {SIAM Journal on Control and Optimization}, journal = {SIAM Journal on Control and Optimization}, pages = {28}, year = {2020}, abstract = {In this article, we extend the time-domain decomposition method described by Lagnese and Leugering (2003) to semilinear optimal control problems for hyperbolic balance laws with spatio-temporal varying coefficients. We provide the design of the iterative method applied to the global first-order optimality system, prove its convergence, and derive an a posteriori error estimate. The analysis is done entirely on the continuous level. A distinguishing feature of the method is that the decomposed optimality system can be interpreted as an optimality system of a local "virtual" optimal control problem. Thus, the iterative time-domain decomposition of the optimality system can be interpreted as an iterative parallel scheme for virtual optimal control problems on the subintervals. A typical example and further comments are given to show the range of potential applications. Moreover, we provide some numerical experiments to give a first interpretation of the role of the parameters involved in the iterative process.}, language = {en} } @article{KleinertLabbeLjubićetal.2021, author = {Kleinert, Thomas and Labb{\´e}, Martine and Ljubić, Ivana and Schmidt, Martin}, title = {A Survey on Mixed-Integer Programming Techniques in Bilevel Optimization}, series = {EURO Journal on Computational Optimization}, journal = {EURO Journal on Computational Optimization}, pages = {47}, year = {2021}, abstract = {Bilevel optimization is a field of mathematical programming in which some variables are constrained to be the solution of another optimization problem. As a consequence, bilevel optimization is able to model hierarchical decision processes. This is appealing for modeling real-world problems, but it also makes the resulting optimization models hard to solve in theory and practice. The scientific interest in computational bilevel optimization increased a lot over the last decade and is still growing. Independent of whether the bilevel problem itself contains integer variables or not, many state-of-the-art solution approaches for bilevel optimization make use of techniques that originate from mixed-integer programming. These techniques include branch-and-bound methods, cutting planes and, thus, branch-and-cut approaches, or problem-specific decomposition methods. In this survey article, we review bilevel-tailored approaches that exploit these mixed-integer programming techniques to solve bilevel optimization problems. To this end, we first consider bilevel problems with convex or, in particular, linear lower-level problems. The discussed solution methods in this field stem from original works from the 1980's but, on the other hand, are still actively researched today. Second, we review modern algorithmic approaches to solve mixed-integer bilevel problems that contain integrality constraints in the lower level. Moreover, we also briefly discuss the area of mixed-integer nonlinear bilevel problems. Third, we devote some attention to more specific fields such as pricing or interdiction models that genuinely contain bilinear and thus nonconvex aspects. Finally, we sketch a list of open questions from the areas of algorithmic and computational bilevel optimization, which may lead to interesting future research that will further propel this fascinating and active field of research.}, language = {en} } @article{KleinertLabbeSchmidtetal.2020, author = {Kleinert, Thomas and Labb{\´e}, Martine and Schmidt, Martin and Plein, Fr{\"a}nk}, title = {Closing the Gap in Linear Bilevel Optimization: A New Valid Primal-Dual Inequality}, series = {Optimization Letters}, journal = {Optimization Letters}, number = {15}, pages = {1027 -- 1040}, year = {2020}, abstract = {Linear bilevel optimization problems are often tackled by replacing the linear lower-level problem with its Karush-Kuhn-Tucker (KKT) conditions. The resulting single-level problem can be solved in a branch-and-bound fashion by branching on the complementarity constraints of the lower-level problem's optimality conditions. While in mixed-integer single-level optimization branch-and-cut has proven to be a powerful extension of branch-and-bound, in linear bilevel optimization not too many bilevel-tailored valid inequalities exist. In this paper, we briefly review existing cuts for linear bilevel problems and introduce a new valid inequality that exploits the strong duality condition of the lower level. We further discuss strengthened variants of the inequality that can be derived from McCormick envelopes. In a computational study, we show that the new valid inequalities can help to close the optimality gap very effectively on a large test set of linear bilevel instances.}, language = {de} } @article{BoettgerGrimmKleinertetal.2020, author = {B{\"o}ttger, Tom and Grimm, Veronika and Kleinert, Thomas and Schmidt, Martin}, title = {The Cost of Decoupling Trade and Transport in the European Entry-Exit Gas Market with Linear Physics Modeling}, series = {European Journal of Operational Research}, journal = {European Journal of Operational Research}, pages = {27}, year = {2020}, abstract = {Liberalized gas markets in Europe are organized as entry-exit regimes so that gas trade and transport are decoupled. The decoupling is achieved via the announcement of technical capacities by the transmission system operator (TSO) at all entry and exit points of the network. These capacities can be booked by gas suppliers and customers in long-term contracts. Only traders who have booked capacities up-front can "nominate" quantities for injection or withdrawal of gas via a day-ahead market. To ensure feasibility of the nominations for the physical network, the TSO must only announce technical capacities for which all possibly nominated quantities are transportable. In this paper, we use a four-level model of the entry-exit gas market to analyze possible welfare losses associated with the decoupling of gas trade and transport. In addition to the multilevel structure, the model contains robust aspects to cover the conservative nature of the European entry-exit system. We provide several reformulations to obtain a single-level mixed-integer quadratic problem. The overall model of the considered market regime is extremely challenging and we thus have to make the main assumption that gas flows are modeled as potential-based linear flows. Using the derived single-level reformulation of the problem, we show that the feasibility requirements for technical capacities imply significant welfare losses due to unused network capacity. Furthermore, we find that the specific structure of the network has a considerable influence on the optimal choice of technical capacities. Our results thus show that trade and transport are not decoupled in the long term. As a further source of welfare losses and discrimination against individual actors, we identify the minimum prices for booking capacity at the individual nodes.}, language = {en} } @unpublished{GrimmNowakScheweetal.2020, author = {Grimm, Veronika and Nowak, Daniel and Schewe, Lars and Schmidt, Martin and Schwartz, Alexandra and Z{\"o}ttl, Gregor}, title = {A Tractable Multi-Leader Multi-Follower Peak-Load-Pricing Model with Strategic Interaction}, doi = {10.1007/s10107-021-01708-0}, pages = {35}, year = {2020}, abstract = {While single-level Nash equilibrium problems are quite well understood nowadays, less is known about multi-leader multi-follower games. However, these have important applications, e.g., in the analysis of electricity and gas markets, where often a limited number of firms interacts on various subsequent markets. In this paper, we consider a special class of two-level multi-leader multi-follower games that can be applied, e.g., to model strategic booking decisions in the European entry-exit gas market. For this nontrivial class of games, we develop a solution algorithm that is able to compute the complete set of Nash equilibria instead of just individual solutions or a bigger set of stationary points. Additionally, we prove that for this class of games, the solution set is finite and provide examples for instances without any Nash equilibria in pure strategies. We apply the algorithm to a case study in which we compute strategic booking and nomination decisions in a model of the European entry-exit gas market system. Finally, we use our algorithm to provide a publicly available test library for the considered class of multi-leader multi-follower games. This library contains problem instances with different economic and mathematical properties so that other researchers in the field can test and benchmark newly developed methods for this challenging class of problems.}, language = {en} } @unpublished{BiefelLiersRolfesetal.2020, author = {Biefel, Christian and Liers, Frauke and Rolfes, Jan and Schmidt, Martin}, title = {Affinely Adjustable Robust Linear Complementarity Problems}, pages = {20}, year = {2020}, abstract = {Linear complementarity problems are a powerful tool for modeling many practically relevant situations such as market equilibria. They also connect many sub-areas of mathematics like game theory, optimization, and matrix theory. Despite their close relation to optimization, the protection of LCPs against uncertainties - especially in the sense of robust optimization - is still in its infancy. During the last years, robust LCPs have only been studied using the notions of strict and Γ-robustness. Unfortunately, both concepts lead to the problem that the existence of robust solutions cannot be guaranteed. In this paper, we consider affinely adjustable robust LCPs. In the latter, a part of the LCP solution is allowed to adjust via a function that is affine in the uncertainty. We show that this notion of robustness allows to establish strong characterizations of solutions for the cases of uncertain matrix and vector, separately, from which existence results can be derived. Our main results are valid for the case of an uncertain LCP vector. Here, we additionally provide sufficient conditions on the LCP matrix for the uniqueness of a solution. Moreover, based on characterizations of the affinely adjustable robust solutions, we derive a mixed-integer programming formulation that allows to solve the corresponding robust counterpart. If, in addition, the certain LCP matrix is positive semidefinite, we prove polynomial-time solvability and uniqueness of robust solutions. If the LCP matrix is uncertain, characterizations of solutions are developed for every nominal matrix, i.e., these characterizations are, in particular, independent of the definiteness of the nominal matrix. Robust solutions are also shown to be unique for positive definite LCP matrix but both uniqueness and mixed-integer programming formulations still remain open problems if the nominal LCP matrix is not positive definite.}, language = {en} } @article{ScheweSchmidtThuerauf, author = {Schewe, Lars and Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {Global Optimization for the Multilevel European Gas Market System with Nonlinear Flow Models on Trees}, series = {Journal of Global Optimization}, journal = {Journal of Global Optimization}, doi = {10.1007/s10898-021-01099-8}, abstract = {The European gas market is implemented as an entry-exit system, which aims to decouple transport and trading of gas. It has been modeled in the literature as a multilevel problem, which contains a nonlinear flow model of gas physics. Besides the multilevel structure and the nonlinear flow model, the computation of so-called technical capacities is another major challenge. These lead to nonlinear adjustable robust constraints that are computationally intractable in general. We provide techniques to equivalently reformulate these nonlinear adjustable constraints as finitely many convex constraints including integer variables in the case that the underlying network is tree-shaped. We further derive additional combinatorial constraints that significantly speed up the solution process. Using our results, we can recast the multilevel model as a single-level nonconvex mixed-integer nonlinear problem, which we then solve on a real-world network, namely the Greek gas network, to global optimality. Overall, this is the first time that the considered multilevel entry-exit system can be solved for a real-world sized network and a nonlinear flow model.}, language = {en} } @unpublished{KleinertSchmidt2020, author = {Kleinert, Thomas and Schmidt, Martin}, title = {Why there is no need to use a big-M in linear bilevel optimization: A computational study of two ready-to-use approaches}, pages = {8}, year = {2020}, abstract = {Linear bilevel optimization problems have gained increasing attention both in theory as well as in practical applications of Operations Research (OR) during the last years and decades. The latter is mainly due to the ability of this class of problems to model hierarchical decision processes. However, this ability makes bilevel problems also very hard to solve. Since no general-purpose solvers are available, a "best-practice" has developed in the applied OR community, in which not all people want to develop tailored algorithms but "just use" bilevel optimization as a modeling tool for practice. This best-practice is the big-M reformulation of the Karush-Kuhn-Tucker (KKT) conditions of the lower-level problem - an approach that has been shown to be highly problematic by Pineda and Morales (2019). Choosing invalid values for M yields solutions that may be arbitrarily bad. Checking the validity of the big-Ms is however shown to be as hard as solving the original bilevel problem in Kleinert et al. (2019). Nevertheless, due to its appealing simplicity, especially w.r.t. the required implementation effort, this ready-to-use approach still is the most popular method. Until now, there has been a lack of approaches that are competitive both in terms of implementation effort and computational cost. In this note we demonstrate that there is indeed another competitive ready-to-use approach: If the SOS-1 technique is applied to the KKT complementarity conditions, adding the simple additional root-node inequality developed by Kleinert et al. (2020) leads to a competitive performance - without having all the possible theoretical disadvantages of the big-M approach.}, language = {en} } @unpublished{GabrielLealSchmidt2020, author = {Gabriel, Steven A. and Leal, Marina and Schmidt, Martin}, title = {On Linear Bilevel Optimization Problems with Complementarity-Constrained Lower Levels}, pages = {16}, year = {2020}, abstract = {We consider a novel class of linear bilevel optimization models with a lower level that is a linear program with complementarity constraints (LPCC). We present different single-level reformulations depending on whether the linear complementarity problem (LCP) as part of the lower-level constraint set depends on the upper-level decisions or not as well as on whether the LCP matrix is positive definite or positive semidefinite. Moreover, we illustrate the connection to linear trilevel models that can be reduced to bilevel problems with LPCC lower levels having positive (semi)definite matrices. Finally, we provide two generic and illustrative bilevel models from the fields of transportation and energy to show the practical relevance of the newly introduced class of bilevel problems and show related theoretical results.}, language = {en} } @article{BeckSchmidt2021, author = {Beck, Yasmine and Schmidt, Martin}, title = {A Robust Approach for Modeling Limited Observability in Bilevel Optimization}, series = {Operations Research Letters}, journal = {Operations Research Letters}, number = {49(5)}, pages = {752 -- 758}, year = {2021}, abstract = {Many applications of bilevel optimization contain a leader facing a follower whose reaction deviates from the one expected by the leader due to some kind of bounded rationality. We consider bilinear bilevel problems with follower's response uncertainty due to limited observability regarding the leader's decision and exploit robust optimization to model the decision making of the follower. We show that the robust counterpart of the lower level allows to tackle the problem via the lower level's KKT conditions.}, language = {en} } @article{PleinThueraufLabbeetal.2021, author = {Plein, Fr{\"a}nk and Th{\"u}rauf, Johannes and Labb{\´e}, Martine and Schmidt, Martin}, title = {A Bilevel Optimization Approach to Decide the Feasibility of Bookings in the European Gas Market}, series = {Mathematical Methods of Operations Research}, journal = {Mathematical Methods of Operations Research}, doi = {10.1007/s00186-021-00752-y}, pages = {37}, year = {2021}, abstract = {The European gas market is organized as a so-called entry-exit system with the main goal to decouple transport and trading. To this end, gas traders and the transmission system operator (TSO) sign so-called booking contracts that grant capacity rights to traders to inject or withdraw gas at certain nodes up to this capacity. On a day-ahead basis, traders then nominate the actual amount of gas within the previously booked capacities. By signing a booking contract, the TSO guarantees that all nominations within the booking bounds can be transported through the network. This results in a highly challenging mathematical problem. Using potential-based flows to model stationary gas physics, feasible bookings on passive networks, i.e., networks without controllable elements, have been characterized in the recent literature. In this paper, we consider networks with linearly modeled active elements such as compressors or control valves. Since these active elements allow the TSO to control the gas flow, the single-level approaches for passive networks from the literature are no longer applicable. We thus present a bilevel model to decide the feasibility of bookings in networks with active elements. While this model is well-defined for general active networks, we focus on the class of networks for which active elements do not lie on cycles. This assumption allows us to reformulate the original bilevel model such that the lower-level problem is linear for every given upper-level decision. Consequently, we derive several single-level reformulations for this case. Besides the classic Karush-Kuhn-Tucker reformulation, we obtain three problem-specific optimal-value-function reformulations. The latter also lead to novel characterizations of feasible bookings in networks with active elements that do not lie on cycles. We compare the performance of our methods by a case study based on data from the GasLib.}, language = {en} } @unpublished{HeitschHenrionKleinertetal.2021, author = {Heitsch, Holger and Henrion, Ren{\´e} and Kleinert, Thomas and Schmidt, Martin}, title = {On Convex Lower-Level Black-Box Constraints in Bilevel Optimization with an Application to Gas Market Models with Chance Constraints}, pages = {34}, year = {2021}, abstract = {Bilevel optimization is an increasingly important tool to model hierarchical decision making. However, the ability of modeling such settings makes bilevel problems hard to solve in theory and practice. In this paper, we add on the general difficulty of this class of problems by further incorporating convex black-box constraints in the lower level. For this setup, we develop a cutting-plane algorithm that computes approximate bilevel-feasible points. We apply this method to a bilevel model of the European gas market in which we use a joint chance constraint to model uncertain loads. Since the chance constraint is not available in closed form, this fits into the black-box setting studied before. For the applied model, we use further problem-specific insights to derive bounds on the objective value of the bilevel problem. By doing so, we are able to show that we solve the application problem to approximate global optimality. In our numerical case study we are thus able to evaluate the welfare sensitivity in dependence of the achieved safety level of uncertain load coverage.}, language = {en} } @unpublished{BeckLjubicSchmidt2022, author = {Beck, Yasmine and Ljubic, Ivana and Schmidt, Martin}, title = {A Survey on Bilevel Optimization Under Uncertainty}, pages = {57}, year = {2022}, abstract = {Bilevel optimization is a very active field of applied mathematics. The main reason is that bilevel optimization problems can serve as a powerful tool for modeling hierarchical decision making processes. This ability, however, also makes the resulting problems challenging to solve - both in theory and practice. Fortunately, there have been significant algorithmic advances in the field of bilevel optimization so that we can solve much larger and also more complicated problems today compared to what was possible to solve two decades ago. This results in more and more challenging bilevel problems that researchers try to solve today. This survey gives a detailed overview of one of these more challenging classes of bilevel problems: bilevel optimization under uncertainty. We review the classic ways of addressing uncertainties in bilevel optimization using stochastic or robust techniques. Moreover, we highlight that the sources of uncertainty in bilevel optimization are much richer than for usual, i.e., single-level, problems since not only the problem's data can be uncertain but also the (observation of the) decisions of the two players can be subject to uncertainty. We thus also review the field of bilevel optimization under limited observability, the area of problems considering only near-optimal decisions, and discuss intermediate solution concepts between the optimistic and pessimistic cases. Finally, we also review the rich literature on applications studied using uncertain bilevel problems such as in energy, for interdiction games and security applications, in management sciences, and networks.}, language = {en} } @unpublished{MolanSchmidt2022, author = {Molan, Ioana and Schmidt, Martin}, title = {Using Neural Networks to Solve Linear Bilevel Problems with Unknown Lower Level}, pages = {16}, year = {2022}, abstract = {Bilevel problems are used to model the interaction between two decision makers in which the lower-level problem, the so-called follower's problem, appears as a constraint in the upper-level problem of the so-called leader. One issue in many practical situations is that the follower's problem is not explicitly known by the leader. For such bilevel problems with unknown lower-level model we propose the use of neural networks to learn the follower's optimal response for given decisions of the leader based on available historical data of pairs of leader and follower decisions. Integrating the resulting neural network in a single-level reformulation of the bilevel problem leads to a challenging model with a black-box constraint. We exploit Lipschitz optimization techniques from the literature to solve this reformulation and illustrate the applicability of the proposed method with some preliminary case studies using academic and linear bilevel instances.}, language = {en} } @unpublished{HorlaenderSchmidt2022, author = {Horl{\"a}nder, Andreas and Schmidt, Martin}, title = {A Penalty Branch-and-Bound Method for Mixed-Integer Quadratic Bilevel Problems}, pages = {9}, year = {2022}, abstract = {We propose an algorithm for solving bilevel problems with mixed-integer convex-quadratic upper level as well as convex-quadratic and continuous lower level. The method is based on a classic branch-and-bound procedure, where branching is performed on the integer constraints and on the complementarity constraints resulting from the KKT reformulation of the lower-level problem. However, instead of branching on constraints as usual, suitably chosen penalty terms are added to the objective function in order to create new subproblems in the tree. We prove the correctness of the method and present its applicability by some first numerical results.}, language = {en} } @unpublished{HanteKrugSchmidt2021, author = {Hante, Falk and Krug, Richard and Schmidt, Martin}, title = {Time-Domain Decomposition for Mixed-Integer Optimal Control Problems}, pages = {32}, year = {2021}, abstract = {We consider mixed-integer optimal control problems, whose optimality conditions involve global combinatorial optimization aspects for the corresponding Hamiltonian pointwise in time. We propose a time-domain decomposition, which makes this problem class accessible for mixed-integer programming using parallel-in-time direct discretizations. The approach is based on a decomposition of the optimality system and the interpretation of the resulting subproblems as suitably chosen mixed-integer optimal control problems on subintervals in time. An iterative procedure then ensures continuity of the states at the boundaries of the subintervals via co-state information encoded in virtual controls. We prove convergence of this iterative scheme for discrete-continuous linear-quadratic problems and present numerical results both for linear-quadratic as well as nonlinear problems.}, language = {en} } @article{KleinertGrimmSchmidt2019, author = {Kleinert, Thomas and Grimm, Veronika and Schmidt, Martin}, title = {Outer Approximation for Global Optimization of Mixed-Integer Quadratic Bilevel Problems}, series = {Mathematical Programming (Series B)}, journal = {Mathematical Programming (Series B)}, pages = {28}, year = {2019}, abstract = {Bilevel optimization problems have received a lot of attention in the last years and decades. Besides numerous theoretical developments there also evolved novel solution algorithms for mixed-integer linear bilevel problems and the most recent algorithms use branch-and-cut techniques from mixed-integer programming that are especially tailored for the bilevel context. In this paper, we consider MIQP-QP bilevel problems, i.e., models with a mixed-integer convex-quadratic upper level and a continuous convex-quadratic lower level. This setting allows for a strong-duality-based transformation of the lower level which yields, in general, an equivalent nonconvex single-level reformulation of the original bilevel problem. Under reasonable assumptions, we can derive both a multi- and a single-tree outer-approximation-based cutting-plane algorithm. We show finite termination and correctness of both methods and present extensive numerical results that illustrate the applicability of the approaches. It turns out that the proposed methods are capable of solving bilevel instances with several thousand variables and constraints and significantly outperform classical solution approaches.}, language = {en} } @article{KramerKrebsSchmidt2018, author = {Kramer, Anja and Krebs, Vanessa and Schmidt, Martin}, title = {Strictly and Γ-Robust Counterparts of Electricity Market Models: Perfect Competition and Nash-Cournot Equilibria}, series = {Operations Research Perspectives}, journal = {Operations Research Perspectives}, number = {89(2)}, pages = {100197}, year = {2018}, abstract = {This paper mainly studies two topics: linear complementarity problems for modeling electricity market equilibria and optimization under uncertainty. We consider both perfectly competitive and Nash-Cournot models of electricity markets and study their robustifications using strict robustness and the Γ-approach. For three out of the four combinations of economic competition and robustification, we derive algorithmically tractable convex optimization counterparts that have a clear-cut economic interpretation. In the case of perfect competition, this result corresponds to the two classical welfare theorems, which also apply in both considered robust cases that again yield convex robustified problems. Using the mentioned counterparts, we can also prove the existence and, in some cases, uniqueness of robust equilibria. Surprisingly, it turns out that there is no such economic sensible counterpart for the case of Γ-robustifications of Nash-Cournot models. Thus, an analogue of the welfare theorems does not hold in this case. Finally, we provide a computational case study that illustrates the different effects of the combination of economic competition and uncertainty modeling.}, language = {en} } @article{ScheweSchmidtThuerauf2018, author = {Schewe, Lars and Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {Structural Properties of Feasible Bookings in the European Entry-Exit Gas Market System}, series = {4OR}, journal = {4OR}, number = {18}, doi = {10.1007/s10288-019-00411-3}, pages = {197 -- 218}, year = {2018}, abstract = {In this work we analyze the structural properties of the set of feasible bookings in the European entry-exit gas market system. We present formal definitions of feasible bookings and then analyze properties that are important if one wants to optimize over them. Thus, we study whether the sets of feasible nominations and bookings are bounded, convex, connected, conic, and star-shaped. The results depend on the specific model of gas flow in a network. Here, we discuss a simple linear flow model with arc capacities as well as nonlinear and mixed-integer nonlinear models of passive and active networks, respectively. It turns out that the set of feasible bookings has some unintuitive properties. For instance, we show that the set is nonconvex even though only a simple linear flow model is used.}, language = {en} } @article{KrebsSchmidt2019, author = {Krebs, Vanessa and Schmidt, Martin}, title = {Γ-Robust Linear Complementarity Problems}, series = {Optimization Methods and Software}, journal = {Optimization Methods and Software}, year = {2019}, abstract = {Complementarity problems are often used to compute equilibria made up of specifically coordinated solutions of different optimization problems. Specific examples are game-theoretic settings like the bimatrix game or energy market models like for electricity or natural gas. While optimization under uncertainties is rather well-developed, the field of equilibrium models represented by complementarity problems under uncertainty - especially using the concepts of robust optimization - is still in its infancy. In this paper, we extend the theory of strictly robust linear complementarity problems (LCPs) to Γ-robust settings, where existence of worst-case-hedged equilibria cannot be guaranteed. Thus, we study the minimization of the worst-case gap function of Γ-robust counterparts of LCPs. For box and l1-norm uncertainty sets we derive tractable convex counterparts for monotone LCPs and study their feasibility as well as the existence and uniqueness of solutions. To this end, we consider uncertainties in the vector and in the matrix defining the LCP. We additionally study so-called ρ-robust solutions, i.e., solutions of relaxed uncertain LCPs. Finally, we illustrate the Γ-robust concept applied to LCPs in the light of the above mentioned classical examples of bimatrix games and market equilibrium modeling.}, language = {en} } @article{KrugMehrmannSchmidt2019, author = {Krug, Richard and Mehrmann, Volker and Schmidt, Martin}, title = {Nonlinear Optimization of District Heating Networks}, series = {Optimization and Engineering}, journal = {Optimization and Engineering}, number = {22(2)}, pages = {783 -- 819}, year = {2019}, abstract = {We develop a complementarity-constrained nonlinear optimization model for the time-dependent control of district heating networks. The main physical aspects of water and heat flow in these networks are governed by nonlinear and hyperbolic 1d partial differential equations. In addition, a pooling-type mixing model is required at the nodes of the network to treat the mixing of different water temperatures. This mixing model can be recast using suitable complementarity constraints. The resulting problem is a mathematical program with complementarity constraints subject to nonlinear partial differential equations describing the physics. In order to obtain a tractable problem, we apply suitable discretizations in space and time, resulting in a finite-dimensional optimization problem with complementarity constraints for which we develop a suitable reformulation with improved constraint regularity. Moreover, we propose an instantaneous control approach for the discretized problem, discuss practically relevant penalty formulations, and present preprocessing techniques that are used to simplify the mixing model at the nodes of the network. Finally, we use all these techniques to solve realistic instances. Our numerical results show the applicability of our techniques in practice.}, language = {en} } @unpublished{KrebsMuellerSchmidt2019, author = {Krebs, Vanessa and M{\"u}ller, Michael and Schmidt, Martin}, title = {Γ-Robust Linear Complementarity Problems with Ellipsoidal Uncertainty Sets}, series = {International Transactions in Operational Research}, journal = {International Transactions in Operational Research}, number = {29(1)}, pages = {417 -- 441}, year = {2019}, abstract = {We study uncertain linear complementarity problems (LCPs), i.e., problems in which the LCP vector q or the LCP matrix M may contain uncertain parameters. To this end, we use the concept of Γ-robust optimization applied to the gap function formulation of the LCP. Thus, this work builds upon [16]. There, we studied Γ-robustified LCPs for l1- and box-uncertainty sets, whereas we now focus on ellipsoidal uncertainty set. For uncertainty in q or M, we derive conditions for the tractability of the robust counterparts. For these counterparts, we also give conditions for the existence and uniqueness of their solutions. Finally, a case study for the uncertain traffic equilibrium problem is considered, which illustrates the effects of the values of Γ on the feasibility and quality of the respective robustified solutions.}, language = {en} } @unpublished{SchmidtHillerKochetal.2019, author = {Schmidt, Martin and Hiller, Benjamin and Koch, Thorsten and Pfetsch, Marc and Geißler, Bj{\"o}rn and Henrion, Ren{\´e} and Joormann, Imke and Martin, Alexander and Morsi, Antonio and R{\"o}misch, Werner and Schewe, Lars and Schultz, R{\"u}diger and Steinbach, Marc C.}, title = {Capacity Evaluation for Large-Scale Gas Networks}, pages = {8}, year = {2019}, abstract = {Natural gas is important for the energy turnaround in many countries like in Germany, where it serves as a "bridging energy" towards a fossil-free energy supply in the future. About 20\% of the total German energy demand is provided by natural gas, which is transported through a complex pipeline network with a total length of about 30000 km and the efficient use of the given transport infrastructure for natural gas is of political, economic, and societal importance. As a consequence of the liberalization of the European gas market in the last decades, gas trading and transport have been decoupled. This has led to new challenges for gas transport companies, and mathematical optimization is perfectly suited for tackling many of these challenges. However, the underlying mathematical problems are by far too hard to be solved by today's general-purpose software so that novel mathematical theory and algorithms are needed. The industrial research project "ForNe: Research Cooperation Network Optimization" has been initiated and funded by Open Grid Europe in 2009 and brought together experts in mathematical optimization from seven German universities and research institutes, which cover almost the entire range of mathematical optimization: integer and nonlinear optimization as well as optimization under uncertainty. The mathematical research results have been put together in a software package that has been delivered to Open Grid Europe at the end of the project. Moreover, the research is still continuing - e.g., in the Collaborative Research Center/Transregio 154 "Mathematical Modelling, Simulation and Optimization using the Example of Gas Networks" funded by the German Research Foundation.}, language = {en} } @article{LabbePleinSchmidtetal.2019, author = {Labb{\´e}, Martine and Plein, Fr{\"a}nk and Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {Deciding Feasibility of a Booking in the European Gas Market on a Cycle is in P for the Case of Passive Networks}, series = {Networks}, volume = {78}, journal = {Networks}, number = {2}, doi = {10.1007/s00186-021-00752-y}, pages = {128 -- 152}, year = {2019}, abstract = {We show that the feasibility of a booking in the European entry-exit gas market can be decided in polynomial time on single-cycle networks that are passive, i.e., do not contain controllable elements. The feasibility of a booking can be characterized by solving polynomially many nonlinear potential-based flow models for computing so-called potential-difference maximizing load flow scenarios. We thus analyze the structure of these models and exploit both the cyclic graph structure as well as specific properties of potential-based flows. This enables us to solve the decision variant of the nonlinear potential-difference maximization by reducing it to a system of polynomials of constant dimension that is independent of the cycle's size. This system of fixed dimension can be handled with tools from real algebraic geometry to derive a polynomial-time algorithm. The characterization in terms of potential-difference maximizing load flow scenarios then leads to a polynomial-time algorithm for deciding the feasibility of a booking. Our theoretical results extend the existing knowledge about the complexity of deciding the feasibility of bookings from trees to single-cycle networks.}, language = {en} } @article{EgererGrimmKleinertetal.2019, author = {Egerer, Jonas and Grimm, Veronika and Kleinert, Thomas and Schmidt, Martin and Z{\"o}ttl, Gregor}, title = {The Impact of Neighboring Markets on Renewable Locations, Transmission Expansion, and Generation Investment}, series = {European Journal of Operational Research}, journal = {European Journal of Operational Research}, pages = {35}, year = {2019}, abstract = {Many long-term investment planning models for liberalized electricity markets either optimize for the entire electricity system or focus on confined jurisdictions, abstracting from adjacent markets. In this paper, we provide models for analyzing the impact of the interdependencies between a core electricity market and its neighboring markets on key long-run decisions. This we do both for zonal and nodal pricing schemes. The identification of welfare optimal investments in transmission lines and renewable capacity within a core electricity market requires a spatially restricted objective function, which also accounts for benefits from cross-border electricity trading. This leads to mixed-integer nonlinear multilevel optimization problems with bilinear nonconvexities for which we adapt a Benders-like decomposition approach from the literature. In a case study, we use a stylized six-node network to disentangle different effects of optimal regional (as compared to supra-regional) investment planning. Regional planning alters investment in transmission and renewable capacity in the core region, which affects private investment in generation capacity also in adjacent regions and increases welfare in the core region at the cost of system welfare. Depending on the congestion-pricing scheme, the regulator of the core region follows different strategies to increase welfare causing distributional effects among stakeholders.}, language = {en} } @article{ScheweSchmidtThuerauf2020, author = {Schewe, Lars and Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {Computing Technical Capacities in the European Entry-Exit Gas Market is NP-Hard}, series = {Annals of Operations Research}, journal = {Annals of Operations Research}, number = {295}, doi = {10.1007/s10479-020-03725-2}, pages = {337 -- 362}, year = {2020}, abstract = {As a result of its liberalization, the European gas market is organized as an entry-exit system in order to decouple the trading and transport of natural gas. Roughly summarized, the gas market organization consists of four subsequent stages. First, the transmission system operator (TSO) is obliged to allocate so-called maximal technical capacities for the nodes of the network. Second, the TSO and the gas traders sign mid- to long-term capacity-right contracts, where the capacity is bounded above by the allocated technical capacities. These contracts are called bookings. Third, on a day-ahead basis, gas traders can nominate the amount of gas that they inject or withdraw from the network at entry and exit nodes, where the nominated amount is bounded above by the respective booking. Fourth and finally, the TSO has to operate the network such that the nominated amounts of gas can be transported. By signing the booking contract, the TSO guarantees that all possibly resulting nominations can indeed be transported. Consequently, maximal technical capacities have to satisfy that all nominations that comply with these technical capacities can be transported through the network. This leads to a highly challenging mathematical optimization problem. We consider the specific instantiations of this problem in which we assume capacitated linear as well as potential-based flow models. In this contribution, we formally introduce the problem of Computing Technical Capacities (CTC) and prove that it is NP-complete on trees and NP-hard in general. To this end, we first reduce the Subset Sum problem to CTC for the case of capacitated linear flows in trees. Afterward, we extend this result to CTC with potential-based flows and show that this problem is also NP-complete on trees by reducing it to the case of capacitated linear flow. Since the hardness results are obtained for the easiest case, i.e., on tree-shaped networks with capacitated linear as well as potential-based flows, this implies the hardness of CTC for more general graph classes.}, language = {en} } @article{CelebiKrebsSchmidt0202, author = {{\c{C}}elebi, Emre and Krebs, Vanessa and Schmidt, Martin}, title = {Γ-Robust Electricity Market Equilibrium Models with Transmission and Generation Investments}, series = {Energy Systems}, journal = {Energy Systems}, pages = {20}, year = {0202}, abstract = {We consider uncertain robust electricity market equilibrium problems including transmission and generation investments. Electricity market equilibrium modeling has a long tradition but is, in most of the cases, applied in a deterministic setting in which all data of the model are known. Whereas there exist some literature on stochastic equilibrium problems, the field of robust equilibrium models is still in its infancy. We contribute to this new field of research by considering Γ-robust electricity market equilibrium models on lossless DC networks with transmission and generation investments. We state the nominal market equilibrium problem as a mixed complementarity problem as well as its variational inequality and welfare optimization counterparts. For the latter, we then derive a Γ-robust formulation and show that it is indeed the counterpart of a market equilibrium problem with robustified player problems. Finally, we present two case studies to gain insights into the general effects of robustification on electricity market models. In particular, our case studies reveal that the transmission system operator tends to act more risk-neutral in the robust setting, whereas generating firms clearly behave more risk-averse.}, language = {en} } @article{GabrielLealSchmidt2020, author = {Gabriel, Steven A. and Leal, Marina and Schmidt, Martin}, title = {Solving Binary-Constrained Mixed Complementarity Problems Using Continuous Reformulations}, series = {Computers \& Operations Research}, journal = {Computers \& Operations Research}, pages = {24}, year = {2020}, abstract = {Mixed complementarity problems are of great importance in practice since they appear in various fields of applications like energy markets, optimal stopping, or traffic equilibrium problems. However, they are also very challenging due to their inherent, nonconvex structure. In addition, recent applications require the incorporation of integrality constraints. Since complementarity problems often model some kind of equilibrium, these recent applications ask for equilibrium points that additionally satisfy certain integer conditions. Obviously, this makes the problem even harder to solve. The solution approach used most frequently in the literature is to recast the complementarity conditions as disjunctive constraints using additional binary variables and big-M constraints. However, both latter aspects create issues regarding the tractability and correctness of the reformulation. In this paper, we follow the opposite route and restate the integrality conditions as complementarity constraints, leading to purely continuous reformulations that can be tackled by local solvers. We study these reformulations theoretically and provide a numerical study that shows that continuous reformulations are useful in practice both in terms of solution times and solution quality.}, language = {en} } @article{RolandSchmidt2020, author = {Roland, Marius and Schmidt, Martin}, title = {Mixed-Integer Nonlinear Optimization for District Heating Network Expansion}, series = {at - Automatisierungstechnik}, journal = {at - Automatisierungstechnik}, pages = {22}, year = {2020}, abstract = {We present a mixed-integer nonlinear optimization model for computing the optimal expansion of an existing tree-shaped district heating network given a number of potential new consumers. To this end, we state a stationary and nonlinear model of all hydraulic and thermal effects in the pipeline network as well as nonlinear models for consumers and the network's depot. For the former, we consider the Euler momentum and the thermal energy equation. The thermal aspects are especially challenging. Here, we develop a novel polynomial approximation that we use in the optimization model. The expansion decisions are modeled by binary variables for which we derive additional valid inequalities that greatly help to solve the highly challenging problem. Finally, we present a case study in which we identify three major aspects that strongly influence investment decisions: the estimated average power demand of potentially new consumers, the distance between the existing network and the new consumers, and thermal losses in the network.}, language = {en} } @article{KleinertSchmidt2019, author = {Kleinert, Thomas and Schmidt, Martin}, title = {Computing Feasible Points of Bilevel Problems with a Penalty Alternating Direction Method}, series = {INFORMS Journal on Computing}, journal = {INFORMS Journal on Computing}, number = {33 (1)}, doi = {10.1287/ijoc.2019.0945}, pages = {198 -- 215}, year = {2019}, abstract = {Bilevel problems are highly challenging optimization problems that appear in many applications of energy market design, critical infrastructure defense, transportation, pricing, etc. Often, these bilevel models are equipped with integer decisions, which makes the problems even harder to solve. Typically, in such a setting in mathematical optimization one develops primal heuristics in order to obtain feasible points of good quality quickly or to enhance the search process of exact global methods. However, there are comparably few heuristics for bilevel problems. In this paper, we develop such a primal heuristic for bilevel problems with mixed-integer linear or quadratic upper level and linear or quadratic lower level. The heuristic is based on a penalty alternating direction method, which allows for a theoretical analysis. We derive a convergence theory stating that the method converges to a stationary point of an equivalent single-level reformulation of the bilevel problem and extensively test the method on a test set of more than 2800 instances - which is one of the largest computational test sets ever used in bilevel programming. The study illustrates the very good performance of the proposed method, both in terms of running times and solution quality. This renders the method a suitable sub-routine in global bilevel solvers as well as a reasonable standalone approach.}, language = {en} } @article{GruebelKleinertKrebsetal.2019, author = {Gr{\"u}bel, Julia and Kleinert, Thomas and Krebs, Vanessa and Orlinskaya, Galina and Schewe, Lars and Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {On Electricity Market Equilibria with Storage: Modeling, Uniqueness, and a Distributed ADMM}, series = {Computers \& Operations Research}, journal = {Computers \& Operations Research}, number = {114}, doi = {10.1016/j.cor.2019.104783}, year = {2019}, abstract = {We consider spot-market trading of electricity including storage operators as additional agents besides producers and consumers. Storages allow for shifting produced electricity from one time period to a later one. Due to this, multiple market equilibria may occur even if classical uniqueness assumptions for the case without storages are satisfied. For models containing storage operators, we derive sufficient conditions that ensure uniqueness of generation and demand. We also prove uniqueness of the market equilibrium for the special case of a single storage operator. Nevertheless, in case of multiple storage operators, uniqueness fails to hold in general, which we show by illustrative examples. We conclude the theoretical discussion with a general ex-post condition for proving the uniqueness of a given solution. In contrast to classical settings without storages, the computation of market equilibria is much more challenging since storage operations couple all trading events over time. For this reason, we propose a tailored parallel and distributed alternating direction method of multipliers (ADMM) for efficiently computing spot-market equilibria over long time horizons. We first analyze the parallel performance of the method itself. Finally, we show that the parallel ADMM clearly outperforms solving the respective problems directly and that it is capable of solving instances with more than 42 million variables in less than 13 minutes.}, language = {en} } @unpublished{ScheweSchmidtWeninger2019, author = {Schewe, Lars and Schmidt, Martin and Weninger, Dieter}, title = {A Decomposition Heuristic for Mixed-Integer Supply Chain Problems}, series = {Operations Research Letters}, journal = {Operations Research Letters}, number = {48(3)}, pages = {225 -- 232}, year = {2019}, abstract = {Mixed-integer supply chain models typically are very large but are also very sparse and can be decomposed into loosely coupled blocks. In this paper, we use general-purpose techniques to obtain a block decomposition of supply chain instances and apply a tailored penalty alternating direction method, which exploits the structural properties of the decomposed instances. We further describe problem-specific enhancements of the algorithm and present numerical results on real-world instances that illustrate the applicability of the approach.}, language = {en} } @unpublished{KleinertLabbePleinetal.2019, author = {Kleinert, Thomas and Labb{\´e}, Martine and Plein, Fr{\"a}nk and Schmidt, Martin}, title = {There's No Free Lunch: On the Hardness of Choosing a Correct Big-M in Bilevel Optimization}, series = {Operations Research}, journal = {Operations Research}, number = {68(6)}, pages = {1716 -- 1721}, year = {2019}, abstract = {One of the most frequently used approaches to solve linear bilevel optimization problems consists in replacing the lower-level problem with its Karush-Kuhn-Tucker (KKT) conditions and by reformulating the KKT complementarity conditions using techniques from mixed-integer linear optimization. The latter step requires to determine some big-M constant in order to bound the lower level's dual feasible set such that no bilevel-optimal solution is cut off. In practice, heuristics are often used to find a big-M although it is known that these approaches may fail. In this paper, we consider the hardness of two proxies for the above mentioned concept of a bilevel-correct big-M. First, we prove that verifying that a given big-M does not cut off any feasible vertex of the lower level's dual polyhedron cannot be done in polynomial time unless P=NP. Second, we show that verifying that a given big-M does not cut off any optimal point of the lower level's dual problem (for any point in the projection of the high-point relaxation onto the leader's decision space) is as hard as solving the original bilevel problem.}, language = {en} } @article{GatzertMartinSchmidtetal.2019, author = {Gatzert, Nadine and Martin, Alexander and Schmidt, Martin and Seith, Benjamin and Vogl, Nikolai}, title = {Portfolio Optimization with Irreversible Long-Term Investments in Renewable Energy under Policy Risk: A Mixed-Integer Multistage Stochastic Model and a Moving-Horizon Approach}, series = {European Journal of Operational Research}, journal = {European Journal of Operational Research}, pages = {29}, year = {2019}, abstract = {Portfolio optimization is an ongoing hot topic of mathematical optimization and management science. Due to the current financial market environment with low interest rates and volatile stock markets, it is getting more and more important to extend portfolio optimization models by other types of investments than classical assets. In this paper, we present a mixed-integer multistage stochastic model that includes investment opportunities in irreversible and long-term infrastructure projects in the context of renewable energies, which are also subject to policy risk. On realistic time scales for investment problems of this type, the resulting instances are by far too large to be solved with today's most evolved optimization software. Thus, we present a tailored moving-horizon approach together with suitable approximations and simplifications of the model. We evaluate these approximations and simplifications in a computational sensitivity analysis and derive a final model that can be tackled on a realistic instance by our moving-horizon approach.}, language = {en} } @article{GrimmOrlinskayaScheweetal.2019, author = {Grimm, Veronika and Orlinskaya, Galina and Schewe, Lars and Schmidt, Martin and Z{\"o}ttl, Gregor}, title = {Optimal Design of Retailer-Prosumer Electricity Tariffs Using Bilevel Optimization}, series = {Computers \& Operations Research}, journal = {Computers \& Operations Research}, number = {114}, pages = {33}, year = {2019}, abstract = {We compare various flexible tariffs that have been proposed to cost-effectively govern a prosumer's electricity management - in particular time-of-use (TOU), critical-peak-pricing (CPP), and a real-time-pricing tariff (RTP). As the outside option, we consider a fixed-price tariff (FP) that restricts the specific characteristics of TOU, CPP, and RTP, so that the flexible tariffs are at least as profitable for the prosumer as the fixed-price tariff. We propose bilevel models to determine the optimal interplay between the retailer's tariff design and the prosumer's decisions on using the storage, on consumption, and on electricity purchases from as well as electricity sales to the grid. The single-level reformulations of the considered bilevel models are computationally highly challenging optimization problems since they, e.g., combine bilinearities and mixed-integer aspects for modeling certain tariff structures. Based on a computational study using real-world data, we find that RTP increases retailer profits, however, leads to the largest price volatility for the prosumer. TOU and CPP only yield mild additional retailer profits and, due to the multiplicity of optimal plans on the part of the prosumer, imply uncertain revenues for the retailer.}, language = {en} } @article{HauschildMarheinekeMehrmannetal.2019, author = {Hauschild, Sarah-Alexa and Marheineke, Nicole and Mehrmann, Volker and Mohring, Jan and Badlyan, Arbi Moses and Rein, Markus and Schmidt, Martin}, title = {Port-Hamiltonian modeling of district heating networks}, series = {Progress in Differential Algebraic Equations II (edited by Reis T., Grundel S., and Sch{\"o}ps S). Differential-Algebraic Equations Forum}, journal = {Progress in Differential Algebraic Equations II (edited by Reis T., Grundel S., and Sch{\"o}ps S). Differential-Algebraic Equations Forum}, pages = {17}, year = {2019}, abstract = {This paper provides a first contribution to port-Hamiltonian modeling of district heating networks. By introducing a model hierarchy of flow equations on the network, this work aims at a thermodynamically consistent port-Hamiltonian embedding of the partial differential-algebraic systems. We show that a spatially discretized network model describing the advection of the internal energy density with respect to an underlying incompressible stationary Euler-type hydrodynamics can be considered as a parameter-dependent finite-dimensional port-Hamiltonian system. Moreover, we present an infinite-dimensional port-Hamiltonian formulation for a compressible instationary thermodynamic fluid flow in a pipe. Based on these first promising results, we raise open questions and point out research perspectives concerning structure-preserving discretization, model reduction, and optimization.}, language = {en} } @article{RobiniusScheweSchmidtetal.2018, author = {Robinius, Martin and Schewe, Lars and Schmidt, Martin and Stolten, Detlef and Th{\"u}rauf, Johannes and Welder, Lara}, title = {Robust Optimal Discrete Arc Sizing for Tree-Shaped Potential Networks}, series = {Computational Optimization and Applications}, journal = {Computational Optimization and Applications}, number = {73(3)}, doi = {10.1007/s10589-019-00085-x}, pages = {791 -- 819}, year = {2018}, abstract = {We consider the problem of discrete arc sizing for tree-shaped potential networks with respect to infinitely many demand scenarios. This means that the arc sizes need to be feasible for an infinite set of scenarios. The problem can be seen as a strictly robust counterpart of a single-scenario network design problem, which is shown to be NP-complete even on trees. In order to obtain a tractable problem, we introduce a method for generating a finite scenario set such that optimality of a sizing for this finite set implies the sizing's optimality for the originally given infinite set of scenarios. We further prove that the size of the finite scenario set is quadratically bounded above in the number of nodes of the underlying tree and that it can be computed in polynomial time. The resulting problem can then be solved as a standard mixed-integer linear optimization problem. Finally, we show the applicability of our theoretical results by computing globally optimal arc sizes for a realistic hydrogen transport network of Eastern Germany.}, language = {en} } @unpublished{HanteSchmidt2023, author = {Hante, Falk M. and Schmidt, Martin}, title = {Gas Transport Network Optimization: Mixed-Integer Nonlinear Models}, pages = {8}, year = {2023}, abstract = {Although modern societies strive towards energy systems that are entirely based on renewable energy carriers, natural gas is still one of the most important energy sources. This became even more obvious in Europe with Russia's 2022 war against the Ukraine and the resulting stop of gas supplies from Russia. Besides that it is very important to use this scarce resource efficiently. To this end, it is also of significant relevance that its transport is organized in the most efficient, i.e., cost- or energy-efficient, way. The corresponding mathematical optimization models have gained a lot of attention in the last decades in different optimization communities. These models are highly nonlinear mixed-integer problems that are constrained by algebraic constraints and partial differential equations (PDEs), which usually leads to models that are not tractable. Hence, simplifications have to be made and in this chapter, we present a commonly accepted finite-dimensional stationary model, i.e., a model in which the steady-state solutions of the PDEs are approximated with algebraic constraints. For more details about the involved PDEs and the treatment of transient descriptions we refer to Hante and Schmidt (2023). The presented finite-dimensional as well as mixed-integer nonlinear and nonconvex model is still highly challenging if it needs to be solved for real-world gas transport networks. Hence, we also review some classic solution approaches from the literature.}, language = {en} } @misc{BeckSchmidt2021, author = {Beck, Yasmine and Schmidt, Martin}, title = {A Gentle and Incomplete Introduction to Bilevel Optimization}, pages = {104}, year = {2021}, abstract = {These are lecture notes on bilevel optimization. The class of bilevel optimization problems is formally introduced and motivated using examples from different fields. Afterward, the main focus is on how to solve linear and mixed-integer linear bilevel optimization problems. To this end, we first consider various single-level reformulations of bilevel optimization problems with linear or convex follower problems, discuss geometric properties of linear bilevel problems, and study different algorithms for solving linear bilevel problems. Finally, we consider mixed-integer linear bilevel problems, discuss the main obstacles for deriving exact as well as effective solution methods, and derive a branch-and-bound method for solving these problems.}, language = {en} } @unpublished{CattaruzzaLabbePetrisetal.2021, author = {Cattaruzza, Diego and Labb{\´e}, Martine and Petris, Matteo and Roland, Marius and Schmidt, Martin}, title = {Exact and Heuristic Solution Techniques for Mixed-Integer Quantile Minimization Problems}, pages = {35}, year = {2021}, abstract = {We consider mixed-integer linear quantile minimization problems that yield large-scale problems that are very hard to solve for real-world instances. We motivate the study of this problem class by two important real-world problems: a maintenance planning problem for electricity networks and a quantile-based variant of the classic portfolio optimization problem. For these problems, we develop valid inequalities and present an overlapping alternating direction method. Moreover, we discuss an adaptive scenario clustering method for which we prove that it terminates after a finite number of iterations with a global optimal solution. We study the computational impact of all presented techniques and finally show that their combination leads to an overall method that can solve the maintenance planning problem on large-scale real-world instances provided by the ROADEF challenge 2020 and that they also lead to significant improvements when solving a quantile-version of the classic portfolio optimization problem.}, language = {en} } @unpublished{SchmidtThuerauf2022, author = {Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {An Exact Method for Nonlinear Network Flow Interdiction Problems}, pages = {28}, year = {2022}, abstract = {We study network flow interdiction problems with nonlinear and nonconvex flow models. The resulting model is a max-min bilevel optimization problem in which the follower's problem is nonlinear and nonconvex. In this game, the leader attacks a limited number of arcs with the goal to maximize the load shed and the follower aims at minimizing the load shed by solving a transport problem in the interdicted network. We develop an exact algorithm consisting of lower and upper bounding schemes that computes an optimal interdiction under the assumption that the interdicted network remains weakly connected. The main challenge consists of computing valid upper bounds for the maximal load shed, whereas lower bounds can directly be derived from the follower's problem. To compute an upper bound, we propose solving a specific bilevel problem, which is derived from restricting the flexibility of the follower when adjusting the load flow. This bilevel problem still has a nonlinear and nonconvex follower's problem, for which we then prove necessary and sufficient optimality conditions. Consequently, we obtain equivalent single-level reformulations of the specific bilevel model to compute upper bounds. Our numerical results show the applicability of this exact approach using the example of gas networks.}, language = {en} } @unpublished{KrugLeugeringMartinetal.2022, author = {Krug, Richard and Leugering, G{\"u}nter and Martin, Alexander and Schmidt, Martin and Weninger, Dieter}, title = {A Consensus-Based Alternating Direction Method for Mixed-Integer and PDE-Constrained Gas Transport Problems}, pages = {26}, year = {2022}, abstract = {We consider dynamic gas transport optimization problems, which lead to large-scale and nonconvex mixed-integer nonlinear optimization problems (MINLPs) on graphs. Usually, the resulting instances are too challenging to be solved by state-of-the-art MINLP solvers. In this paper, we use graph decompositions to obtain multiple optimization problems on smaller blocks, which can be solved in parallel and which may result in simpler classes of optimization problems since not every block necessarily contains mixed-integer or nonlinear aspects. For achieving feasibility at the interfaces of the several blocks, we employ a tailored consensus-based penalty alternating direction method. Our numerical results show that such decomposition techniques can outperform the baseline approach of just solving the overall MINLP from scratch. However, a complete answer to the question of how to decompose MINLPs on graphs in dependence of the given model is still an open topic for future research.}, language = {en} } @unpublished{GruebelKrugSchmidtetal.2022, author = {Gr{\"u}bel, Julia and Krug, Richard and Schmidt, Martin and Wollner, Winnifried}, title = {A Successive Linear Relaxation Method for MINLPs with Multivariate Lipschitz Continuous Nonlinearities}, pages = {34}, year = {2022}, abstract = {We present a novel method for mixed-integer optimization problems with multivariate and Lipschitz continuous nonlinearities. In particular, we do not assume that the nonlinear constraints are explicitly given but that we can only evaluate them and that we know their global Lipschitz constants. The algorithm is a successive linear relaxation method in which we alternate between solving a master problem, which is a mixed-integer linear relaxation of the original problem, and a subproblem, which is designed to tighten the linear relaxation of the next master problem by using the Lipschitz information about the respective functions. By doing so, we follow the ideas of Schmidt et al. (2018, 2021) and improve the tackling of multivariate constraints. Although multivariate nonlinearities obviously increase modeling capabilities, their incorporation also significantly increases the computational burden of the proposed algorithm. We prove the correctness of our method and also derive a worst-case iteration bound. Finally, we show the generality of the addressed problem class and the proposed method by illustrating that both bilevel optimization problems with nonconvex and quadratic lower levels as well as nonlinear and mixed-integer models of gas transport can be tackled by our method. We provide the necessary theory for both applications and briefly illustrate the outcomes of the new method when applied to these two problems.}, language = {en} } @unpublished{HanteSchmidt2023, author = {Hante, Falk M. and Schmidt, Martin}, title = {Gas Transport Network Optimization: PDE-Constrained Models}, pages = {8}, year = {2023}, abstract = {The optimal control of gas transport networks was and still is a very important topic for modern economies and societies. Accordingly, a lot of research has been carried out on this topic during the last years and decades. Besides mixed-integer aspects in gas transport network optimization, one of the main challenges is that a physically and technically detailed modeling of transient gas dynamics leads to theoretically and computationally highly demanding models involving nonlinear partial differential equations (PDEs). For further background on the application, historical notes and a detailed discussion of mixed-integer aspects for stationary descriptions we refer to Hante and Schmidt (2023). In this chapter, we focus on the most common modeling approaches concerning transient descriptions, point out the challenges, and summarize important contributions concerning the optimization of the most relevant control parameters for this particular class of problems.}, language = {en} } @unpublished{BeckLjubicSchmidt2021, author = {Beck, Yasmine and Ljubic, Ivana and Schmidt, Martin}, title = {Exact Methods for Discrete Γ-Robust Interdiction Problems with an Application to the Bilevel Knapsack Problem}, pages = {39}, year = {2021}, abstract = {Developing solution methods for discrete bilevel problems is known to be a challenging task - even if all parameters of the problem are exactly known. Many real-world applications of bilevel optimization, however, involve data uncertainty. We study discrete min-max problems with a follower who faces uncertainties regarding the parameters of the lower-level problem. Adopting a Γ-robust approach, we present an extended formulation and a multi-follower formulation to model this type of problem. For both settings, we provide a generic branch-and-cut framework. Specifically, we investigate interdiction problems with a monotone Γ-robust follower and we derive problem-tailored cuts, which extend existing techniques that have been proposed for the deterministic case. For the Γ-robust knapsack interdiction problem, we computationally evaluate and compare the performance of the proposed algorithms for both modeling approaches.}, language = {en} } @unpublished{HannesVolkerRolandetal.2022, author = {Hannes, D{\"a}nschel and Volker, Mehrmann and Roland, Marius and Schmidt, Martin}, title = {Adaptive Nonlinear Optimization of District Heating Networks Based on Model and Discretization Catalogs}, pages = {29}, year = {2022}, abstract = {We propose an adaptive optimization algorithm for operating district heating networks in a stationary regime. The behavior of hot water flow in the pipe network is modeled using the incompressible Euler equations and a suitably chosen energy equation. By applying different simplifications to these equations, we derive a catalog of models. Our algorithm is based on this catalog and adaptively controls where in the network which model is used. Moreover, the granularity of the applied discretization is controlled in a similar adaptive manner. By doing so, we are able to obtain optimal solutions at low computational costs that satisfy a prescribed tolerance w.r.t. the most accurate modeling level. To adaptively control the switching between different levels and the adaptation of the discretization grids, we derive error measure formulas and a posteriori error measure estimators. Under reasonable assumptions we prove that the adaptive algorithm terminates after finitely many iterations. Our numerical results show that the algorithm is able to produce solutions for problem instances that have not been solvable before.}, language = {en} } @unpublished{BeckSchmidtThueraufetal.2022, author = {Beck, Yasmine and Schmidt, Martin and Th{\"u}rauf, Johannes and Bienstock, Daniel}, title = {On a Computationally Ill-Behaved Bilevel Problem with a Continuous and Nonconvex Lower Level}, pages = {16}, year = {2022}, abstract = {It is well known that bilevel optimization problems are hard to solve both in theory and practice. In this paper, we highlight a further computational difficulty when it comes to solving bilevel problems with continuous but nonconvex lower levels. Even if the lower-level problem is solved to ɛ-feasibility regarding its nonlinear constraints for an arbitrarily small but positive ɛ, the obtained bilevel solution as well as its objective value may be arbitrarily far away from the actual bilevel solution and its actual objective value. This result even holds for bilevel problems for which the nonconvex lower level is uniquely solvable, for which the strict complementarity condition holds, for which the feasible set is convex, and for which Slater's constraint qualification is satisfied for all feasible upper-level decisions. Since the consideration of ɛ-feasibility cannot be avoided when solving nonconvex problems to global optimality, our result shows that computational bilevel optimization with continuous and nonconvex lower levels needs to be done with great care. Finally, we illustrate that the nonlinearities in the lower level are the key reason for the observed bad behavior by showing that linear bilevel problems behave much better at least on the level of feasible solutions.}, language = {en} } @unpublished{ThueraufGruebelSchmidt2024, author = {Th{\"u}rauf, Johannes and Gr{\"u}bel, Julia and Schmidt, Martin}, title = {Adjustable Robust Nonlinear Network Design under Demand Uncertainties}, year = {2024}, abstract = {We study network design problems for nonlinear and nonconvex flow models under demand uncertainties. To this end, we apply the concept of adjustable robust optimization to compute a network design that admits a feasible transport for all, possibly infinitely many, demand scenarios within a given uncertainty set. For solving the corresponding adjustable robust mixed-integer nonlinear optimization problem, we show that a given network design is robust feasible, i.e., it admits a feasible transport for all demand uncertainties, if and only if a finite number of worst-case demand scenarios can be routed through the network. We compute these worst-case scenarios by solving polynomially many nonlinear optimization problems. Embedding this result for robust feasibility in an adversarial approach leads to an exact algorithm that computes an optimal robust network design in a finite number of iterations. Since all of the results are valid for general potential-based flows, the approach can be applied to different utility networks such as gas, hydrogen, or water networks. We finally demonstrate the applicability of the method by computing robust gas networks that are protected from future demand fluctuations.}, language = {en} } @unpublished{HanteSchmidtTopalovic2024, author = {Hante, Falk M. and Schmidt, Martin and Topalovic, Antonia}, title = {Stabilizing GNEP-Based Model Predictive Control: Quasi-GNEPs and End Constraints}, pages = {26}, year = {2024}, abstract = {We present a feedback scheme for non-cooperative dynamic games and investigate its stabilizing properties. The dynamic games are modeled as generalized Nash equilibrium problems (GNEP), in which the shared constraint consists of linear time-discrete dynamic equations (e.g., sampled from a partial or ordinary differential equation), which are jointly controlled by the players' actions. Further, the individual objectives of the players are interdependent and defined over a fixed time horizon. The feedback law is synthesized by moving-horizon model predictive control (MPC). We investigate the asymptotic stability of the resulting closed-loop dynamics. To this end, we introduce α-quasi GNEPs, a family of auxiliary problems based on a modification of the Nikaido-Isoda function, which approximate the original games. Basing the MPC scheme on these auxiliary problems, we derive conditions on the players' objectives, which guarantee asymptotic stability of the closed-loop if stabilizing end constraints are enforced. This analysis is based on showing that the associated optimal-value function is a Lyapunov function. Additionally, we identify a suitable Lyapunov function for the MPC scheme based on the original GNEP, whose solution fulfills the stabilizing end constraints. The theoretical results are complemented by numerical experiments.}, language = {en} } @unpublished{GoerigkKurtzSchmidtetal.2023, author = {Goerigk, Marc and Kurtz, Jannis and Schmidt, Martin and Th{\"u}rauf, Johannes}, title = {Connections between Robust and Bilevel Optimization}, pages = {22}, year = {2023}, abstract = {Robust and bilevel optimization share the common feature that they involve a certain multilevel structure. Hence, although they model something rather different when used in practice, they seem to have a similar mathematical structure. In this paper, we analyze the connections between different types of robust problems (static robust problems with and without decision-dependence of their uncertainty sets, worst-case regret problems, and two-stage robust problems) as well as of bilevel problems (optimistic problems, pessimistic problems, and robust bilevel problems). It turns out that bilevel optimization seems to be more general in the sense that for most types of robust problems, one can find proper reformulations as bilevel problems but not necessarily the other way around. We hope that these results pave the way for a stronger connection between the two fields - in particular to use both theory and algorithms from one field in the other and vice versa.}, language = {en} } @unpublished{GrimmGruebelSchmidtetal.2023, author = {Grimm, Veronika and Gr{\"u}bel, Julia and Schmidt, Martin and Schwartz, Alexandra and Wiertz, Ann-Kathrin and Z{\"o}ttl, Gregor}, title = {On a Tractable Single-Level Reformulation of a Multilevel Model of the European Entry-Exit Gas Market with Market Power}, pages = {31}, year = {2023}, abstract = {We propose a framework that allows to quantitatively analyze the interplay of the different agents involved in gas trade and transport in the context of the European entry-exit system. Previous contributions have focused on the case of perfectly competitive buyers and sellers of gas, which allows to replace the respective market equilibrium problem by a single welfare maximization problem. Our novel framework considers the mathematically more challenging case of a monopolistic and thus strategic gas seller. In this framework, the objective functions of the gas sellers and buyers cannot be aggregated into a common objective function, which is why a multilevel formulation is necessary to accurately capture the sequential nature of the decisions taken. For this setup, we derive sufficient conditions that allow for reformulating the challenging four-level model as a computationally tractable single-level reformulation. We prove the correctness of this reformulation and use it for solving several test instances to illustrate the applicability of our approach.}, language = {en} }