TY - JOUR A1 - Papafitsoros, Konstantinos A1 - Hintermüller, Michael A1 - Rautenberg, Carlos T1 - Analytical aspects of spatially adapted total variation regularisation JF - Journal of Mathematical Analysis and Applications N2 - In this paper we study the structure of solutions of the one dimensional weighted total variation regularisation problem, motivated by its application in signal recovery tasks. We study in depth the relationship between the weight function and the creation of new discontinuities in the solution. A partial semigroup property relating the weight function and the solution is shown and analytic solutions for simply data functions are computed. We prove that the weighted total variation minimisation problem is well-posed even in the case of vanishing weight function, despite the lack of coercivity. This is based on the fact that the total variation of the solution is bounded by the total variation of the data, a result that it also shown here. Finally the relationship to the corresponding weighted fidelity problem is explored, showing that the two problems can produce completely different solutions even for very simple data functions. Y1 - 2017 VL - 454 SP - 891 EP - 935 ER - TY - INPR A1 - Wintergerst, David T1 - Application of Chance Constrained Optimization to Gas Networks N2 - We consider optimization problems with a joint probabilistic constraint under normally distributed uncertain parameters. The parametric constraints are replaced by one constraint stating that the probability of being feasible shall exceed or be equal to a prescribed threshold. In order to apply the concept to gas network optimization under uncertain boundary flows, which corresponds to the demand of customers, we derive an analytic gradient formula. The integral corresponding to the probability can be parameterized by spherical radial decomposition. For this parameterization gradient formulas are known under convexity assumptions of the parametric constraints in the parameter. For the application in gas networks that we have in mind, the convexity assumption of the parametric constraints is not satisfied. Therefore, we weaken it to convexity of the region of feasible parameters for a fixed optimization variable. We proceed to show that the assumptions needed for the gradient formula are met in the gas network optimization problem on a tree. For the numerical implementation we propose a multilevel sampling algorithm that uses a coarse approximation of the chance constraint to generate a warm start for the expensive approximation with fine sampling. The numerical results illustrate that this approach significantly reduces the computation time. KW - chance constraint KW - spherical radial decomposition KW - isothermal Euler equations KW - gas networks KW - multilevel Y1 - 2017 ER - TY - JOUR A1 - Bernstein, Aaron A1 - Disser, Yann A1 - Groß, Martin T1 - General Bounds for Incremental Maximization N2 - We propose a theoretical framework to capture incremental s olutions to cardinality con- strained maximization problems. The defining characterist ic of our framework is that the cardinality/support of the solution is bounded by a value k ∈ N that grows over time, and we allow the solution to be extended one element at a time. We i nvestigate the best-possible competitive ratio of such an incremental solution, i.e., th e worst ratio over all k between the incremental solution after k steps and an optimum solution of cardinality k . We define a large class of problems that contains many important cardin ality constrained maximization problems like maximum matching, knapsack, and packing/cov ering problems. We provide a general 2 . 618-competitive incremental algorithm for this class of pr oblems, and show that no algorithm can have competitive ratio below 2 . 18 in general. In the second part of the paper, we focus on the inherently inc remental greedy algorithm that increases the objective value as much as possible in eac h step. This algorithm is known to be 1 . 58-competitive for submodular objective functions, but it has unbounded competitive ratio for the class of incremental problems mentioned above . We define a relaxed submod- ularity condition for the objective function, capturing pr oblems like maximum (weighted) ( b -)matching and a variant of the maximum flow problem. We show t hat the greedy algo- rithm has competitive ratio (exactly) 2 . 313 for the class of problems that satisfy this relaxed submodularity condition. Note that our upper bounds on the competitive ratios transla te to approximation ratios for the underlying cardinality constrained problems. Y1 - 2017 ER - TY - INPR A1 - Groß, Martin A1 - Gupta, Anupam A1 - Kumar, Amit A1 - Matuschke, Jannik A1 - Schmidt, Daniel R. A1 - Schmidt, Melanie A1 - Verschae, José T1 - A Local-Search Algorithm for Steiner Forest N2 - In the Steiner Forest problem, we are given a graph and a collection of source-sink pairs, and the goal is to find a subgraph of minimum total length such that all pairs are connected. The problem is APX-Hard and can be 2 -approximated by, e.g., the elegant primal-dual algorithm of Agrawal, Klein, and Ravi from 1995. We give a local-search-based constant-factor approximati on for the problem. Local search brings in new techniques to an area that has for long not seen any improv ements and might be a step towards a combinatorial algorithm for the more general survivable n etwork design problem. Moreover, local search was an essential tool to tackle the dynamic MST/Stein er Tree problem, whereas dynamic Steiner Forest is still wide open. It is easy to see that any constant factor local search algori thm requires steps that add/drop many edges together. We propose natural local moves which, at each step , either (a) add a shortest path in the current graph and then drop a bunch of inessential edges, or (b) add a s et of edges to the current solution. This second type of moves is motivated by the potential function w e use to measure progress, combining the cost of the solution with a penalty for each connected compon ent. Our carefully-chosen local moves and potential function work in tandem to eliminate bad local min ima that arise when using more traditional local moves. Our analysis first considers the case where the local optimum is a single tree, and shows optimality w.r.t. moves that add a single edge (and drop a set of edges) is enough to bound the locality gap. For the general case, we show how to “project” the optimal solution o nto the different trees of the local optimum without incurring too much cost (and this argument uses opti mality w.r.t. both kinds of moves), followed by a tree-by-tree argument. We hope both the potential funct ion, and our analysis techniques will be useful to develop and analyze local-search algorithms in ot her contexts. Y1 - 2017 ER - TY - JOUR A1 - Abed, Fidaa A1 - Chen, Lin A1 - Disser, Yann A1 - Groß, Martin A1 - Megow, Nicole A1 - Meißner, Julie A1 - Richter, Alexander T. A1 - Rischke, Roman T1 - Scheduling Maintenance Jobs in Networks N2 - We investigate the problem of scheduling the maintenance of edges in a network, motivated by the goal of minimizing outages in transportation or telecommunication networks. We focus on maintaining connectivity between two nodes over time; for the special case of path networks, this is related to the problem of minimizing the busy time of machines. We show that the problem can be solved in polynomial time in arbitrary networks if preemption is allowed. If preemption is restricted to integral time points, the problem is NP-hard and in the non-preemptive case we give strong non-approximability results. Furthermore, we give tight bounds on the power of preemption, that is, the maximum ratio of the values of non-preemptive and preemptive optimal solutions. Interestingly, the preemptive and the non-preemptive problem can be solved efficiently on paths, whereas we show that mixing both leads to a weakly NP-hard problem that allows for a simple 2-approximation. Y1 - 2017 ER - TY - JOUR A1 - Groß, Martin A1 - Marc E., Pfetsch A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Skutella, Martin T1 - Algorithmic Results for Potential-Based Flows: Easy and Hard Cases N2 - Potential-based flows are an extension of classical network flows in which the flow on an arc is determined by the difference of the potentials of its incident nodes. Such flows are unique and arise, for example, in energy networks. Two important algorithmic problems are to determine whether there exists a feasible flow and to maximize the flow between two designated nodes. We show that these problems can be solved for the single source and sink case by reducing the network to a single arc. However, if we additionally consider switches that allow to force the flow to 0 and decouple the potentials, these problems are NP-hard. Nevertheless, for particular series-parallel networks, one can use algorithms for the subset sum problem. Moreover, applying network presolving based on generalized series-parallel structures allows to significantly reduce the size of realistic energy networks. KW - Potential networks KW - Potential-based flows KW - Maximum flow problem KW - Series-parallel graphs KW - Network reduction Y1 - 2017 U6 - https://doi.org/10.1002/net.21865 VL - 73 IS - 3 SP - 303 EP - 324 ET - Networks ER - TY - JOUR A1 - Hajian, Soheil A1 - Hintermüller, Michael A1 - Ulbrich, Stefan T1 - Total variation diminishing schemes in optimal control of scalar conservation laws JF - IMA Journal of Numerical Analysis N2 - In this paper, optimal control problems subject to a nonlinear scalar conservation law are studied. Such optimal control problems are challenging both at the continuous and at the discrete level since the control-to-state operator poses difficulties as it is, e.g., not differentiable. Therefore discretization of the underlying optimal control problem should be designed with care. Here the discretize-then-optimize approach is employed where first the full discretization of the objective function as well as the underlying PDE is considered. Then, the derivative of the reduced objective is obtained by using an adjoint calculus. In this paper total variation diminishing Runge-Kutta (TVD-RK) methods for the time discretization of such problems are studied. TVD-RK methods, also called strong stability preserving (SSP), are originally designed to preserve total variation of the discrete solution. It is proven in this paper that providing an SSP state scheme, is enough to ensure stability of the discrete adjoint. However requiring SSP for both discrete state and adjoint is too strong. Also approximation properties that the discrete adjoint inherits from the discretization of the state equation are studied. Moreover order conditions are derived. In addition, optimal choices with respect to CFL constant are discussed and numerical experiments are presented. Y1 - 2017 U6 - https://doi.org/10.20347/WIAS.PREPRINT.2383 VL - 39 SP - 105 EP - 140 ER - TY - INPR A1 - Burlacu, Robert A1 - Geißler, Björn A1 - Schewe, Lars T1 - Solving Mixed-Integer Nonlinear Programs using Adaptively Refined Mixed-Integer Linear Programs N2 - We propose a method for solving mixed-integer nonlinear programs (MINLPs) to global optimality by discretization of occuring nonlinearities. The main idea is based on using piecewise linear functions to construct mixed-integer linear program (MIP) relaxations of the underlying MINLP. In order to find a global optimum of the given MINLP we develope an iterative algorithm which solves MIP relaxations that are adaptively refined. We are able to give convergence results for a wide range of MINLPs requiring only continuous nonlinearities with bounded domains and an oracle computing maxima of the nonlinearities on their domain. Moreover, the practicalness of our approach is shown numerically by an application from the field of gas network optimization. KW - Mixed-Integer Nonlinear Programming KW - Piecewise Linearization Y1 - 2017 ER - TY - INPR A1 - Kunkel, Peter A1 - Mehrmann, Volker T1 - Regular solutions of DAE hybrid systems and regularization techniques N2 - The solvability and regularity of hybrid differential-algebraic systems (DAEs) is studied, and classical stability estimates are extended to hybrid DAE systems. Different reasons for non-regularity are discussed and appropriate regularization techniques are presented. This includes a generalization of Filippov regularization in the case of so-called chattering. The results are illustrated by several numerical examples. KW - Differential-algebraic equation KW - hybrid system KW - switched system Y1 - 2017 ER - TY - INPR A1 - Egger, Herbert A1 - Kugler, Thomas A1 - Liljegren-Sailer, Björn A1 - Marheineke, Nicole A1 - Mehrmann, Volker T1 - On structure preserving model reduction for damped wave propagation in transport networks N2 - We consider the discretization and subsequent model reduction of a system of partial differential-algebraic equations describing the propagation of pressure waves in a pipeline network. Important properties like conservation of mass, dissipation of energy, passivity, existence of steady states, and exponential stability can be preserved by an appropriate semi- discretization in space via a mixed finite element method and also during the further dimension reduction by structure preserving Galerkin projection which is the main focus of this paper. Krylov subspace methods are employed for the construction of the reduced models and we discuss modifications needed to satisfy certain algebraic compatibility conditions; these are required to ensure the well-posedness of the reduced models and the preservation of the key properties. Our analysis is based on the underlying infinite dimensional problem and its Galerkin approximations. The proposed algorithms therefore have a direct interpretation in function spaces; in principle, they are even applicable directly to the original system of partial differential-algebraic equations while the intermediate discretization by finite elements is only required for the actual computations. The performance of the proposed methods is illustrated with numerical tests and the necessity for the compatibility conditions is demonstrated by examples. KW - partial differential-algebraic equations KW - port-Hamiltonian systems KW - Galerkin projection Y1 - 2017 ER - TY - JOUR A1 - Schmidt, Martin A1 - Sirvent, Mathias A1 - Wollner, Winnifried T1 - A Decomposition Method for MINLPs with Lipschitz Continuous Nonlinearities JF - Mathematical Programming N2 - Many mixed-integer optimization problems are constrained by nonlinear functions that do not possess desirable analytical properties like convexity or factorability or cannot even be evaluated exactly. This is, e.g., the case for problems constrained by differential equations or for models that rely on black-box simulation runs. For these problem classes, we present, analyze, and test algorithms that solve mixed-integer problems with only Lipschitz continuous nonlinearities. Our theoretical results depend on the assumptions made on the (in)exactness of function evaluations and on the knowledge of Lipschitz constants. If Lipschitz constants are known, we prove finite termination at approximate globally optimal points both for the case of exact and inexact function evaluations. If only approximate Lipschitz constants are known, we prove finite termination and derive additional conditions under which infeasibility can be detected. A computational study for gas transport problems and an academic case study show the applicability of our algorithms to real-world problems and how different assumptions on the constraint functions up- or downgrade the practical performance of the methods. KW - Mixed-Integer Nonlinear Optimization, Lipschitz Optimization, Inexact Function Evaluations, Decomposition Methods, Gas Networks Y1 - 2017 IS - 178(1) SP - 449 EP - 483 ER - TY - JOUR A1 - Gugat, Martin A1 - Ulbrich, Stefan T1 - On Lipschitz Solutions of Initial Boundary Value Problems for Balance Laws JF - Mathematical Models and Methods in Applied Sciences N2 - The flow of gas through networks of pipes can be modeled by the isothermal Euler equations and algebraic node conditions that model the flow through the vertices of the network graph. We prove the well-posedness of the system for gas with nonconstant compressibility factor that is given by an affine linear function. We consider initial data and control functions that are Lipschitz continuous and compatible with the node and boundary conditions. We show the existence of semi--global Lipschitz continuous solutions of the initial boundary value problem. The construction of the solution is based upon a fixed point iteration along the characteristic curves. The solutions of the intial boundary value problem on arbitrary networks satisfy a maximum principle in terms of the Riemann invariants that states that the maximum of the absolute values is attained for the initial or the boundary data. Y1 - 2017 ER - TY - JOUR A1 - Adam, Lukas A1 - Henrion, Rene A1 - Outrata, Jiri T1 - On M-stationarity conditions in MPECs and the associated qualification conditions JF - Mathematical Programming N2 - Depending on whether a mathematical program with equilibrium constraints (MPEC) is considered in its original or its enhanced (via KKT conditions) form, the assumed qualification conditions as well as the derived necessary optimality conditions may differ significantly. In this paper, we study this issue when imposing one of the weakest possible qualification conditions, namely the calmness of the perturbation mapping associated with the respective generalized equations in both forms of the MPEC. It is well known that the calmness property allows one to derive the so-called M-stationarity conditions. The restrictiveness of assumptions and the strength of conclusions in the two forms of the MPEC is also strongly related to the qualification conditions on the “lower level”. For instance, even under the Linear Independence Constraint Qualification (LICQ) for a lower level feasible set described by C 1 functions, the calmness properties of the original and the enhanced perturbation mapping are drastically different. When passing to C 1,1 data, this difference still remains true under the weaker Mangasarian-Fromovitz Constraint Qualification, whereas under LICQ both the calmness assumption and the derived optimality conditions are fully equivalent for the original and the enhanced form of the MPEC. After clarifying these relations, we provide a compilation of practically relevant consequences of our analysis in the derivation of necessary optimality conditions. The obtained results are finally applied to MPECs with structured equilibria. KW - equilibrium constraints KW - optimality conditions KW - constraint qualification KW - calmness KW - perturbation mapping Y1 - 2017 ER - TY - JOUR A1 - Gonzalez Grandon, Tatiana A1 - Heitsch, Holger A1 - Henrion, Rene T1 - A joint model of probabilistic/robust constraints for gas transport management in stationary networks JF - Computational Management Science N2 - We present a novel mathematical algorithm to assist gas network operators in managing uncertainty, while increasing reliability of transmission and supply. As a result, we solve an optimization problem with a joint probabilistic constraint over an infinite system of random inequalities. Such models arise in the presence of uncertain parameters having partially stochastic and partially non-stochastic character. The application that drives this new approach is a stationary network with uncertain demand (which are stochastic due to the possibility of fitting statistical distributions based on historical measurements) and with uncertain roughness coefficients in the pipes (which are uncertain but non-stochastic due to a lack of attainable measurements). We study the sensitivity of local uncertainties in the roughness coefficients and their impact on a highly reliable network operation. In particular, we are going to answer the question, what is the maximum uncertainty that is allowed (shaping a 'maximal' uncertainty set) around nominal roughness coefficients, such that random demands in a stationary gas network can be satisfied at given high probability level for no matter which realization of true roughness coefficients within the uncertainty set. One ends up with a constraint, which is probabilistic with respect to the load of gas and robust with respect to the roughness coefficients. We demonstrate how such constraints can be dealt with in the framework of the so-called spheric-radial decomposition of multivariate Gaussian distributions. The numerical solution of a corresponding optimization problem is illustrated. The results might assist the network operator with the implementation of cost-intensive roughness measurements. KW - chance constraint KW - robust constraint KW - uncertainty set KW - spheric-radial decomposition Y1 - 2017 U6 - https://doi.org/10.1007/s10287-017-0284-7 VL - 14 SP - 443 EP - 460 ER - TY - JOUR A1 - Gugat, Martin A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Sirvent, Mathias A1 - Wintergerst, David T1 - MIP-Based Instantaneous Control of Mixed-Integer PDE-Constrained Gas Transport Problems JF - Computational Optimization and Applications N2 - We study the transient optimization of gas transport networks including both discrete controls due to switching of controllable elements and nonlinear fluid dynamics described by the system of isothermal Euler equations, which are partial differential equations in time and 1-dimensional space. This combination leads to mixed-integer optimization problems subject to nonlinear hyperbolic partial differential equations on a graph. We propose an instantaneous control approach in which suitable Euler discretizations yield systems of ordinary differential equations on a graph. This networked system of ordinary differential equations is shown to be well-posed and affine-linear solutions of these systems are derived analytically. As a consequence, finite-dimensional mixed-integer linear optimization problems are obtained for every time step that can be solved to global optimality using general-purpose solvers. We illustrate our approach in practice by presenting numerical results on a realistic gas transport network. KW - Mixed-integer optimal control KW - Instantaneous control KW - Partial differential equations on graphs KW - Gas networks KW - Mixed-integer linear optimization Y1 - 2017 U6 - https://doi.org/10.1007/s10589-017-9970-1 VL - 70 IS - 1 SP - 267 EP - 294 ER - TY - THES A1 - Wollenberg, Tobias T1 - Two-Stage Stochastic Semidefinite Programming: Theory, Algorithms, and Application to AC Power Flow under Uncertainty N2 - In real life decision problems, one almost always is confronted with uncertainty and risk. For practical optimization problems this is manifested by unknown parameters within the input data, or, an inexact knowledge about the system description itself. In case the uncertain problem data is governed by a known probability distribution, stochastic programming offers a variety of models hedging against uncertainty and risk. Most widely employed are two-stage models, who admit a recourse structure: The first-stage decisions are taken before the random event occurs. After its outcome, a recourse (second-stage) action is made, often but not always understood as some "compensation". In the present thesis, the optimization problems that involve parameters which are not known with certainty are semidefinite programming problems. The constraint sets of these optimization problems are given by intersections of the cone of symmetric, positive semidefinite matrices with either affine or more general equations. Objective functions, formally, may be fairly general, although they often are linear as in the present thesis. We consider risk neutral and risk averse two-stage stochastic semidefinite programs with continuous and mixed-integer recourse, respectively. For these stochastic optimization problems we analyze their structure, derive solution methods relying on decomposition, and finally apply our results to unit commitment in alternating current (AC) power systems. Furthermore, deterministic unit commitment in AC power transmission systems is addressed. Beside traditional unit commitment constraints, the physics of power flow are included. To gain globally optimal solutions a recent semidefinite programming (SDP) approach is used which leads to large-scale semidefinite programs with discrete variables on top. As even the SDP relaxation of these programs is too large for being handled in an all-at-once manner by general SDP solvers, it requires an efficient and reliable method to tackle them. To this end, an algorithm based on Benders decomposition is proposed. With power demand (load) and in-feed from renewables serving as sources of uncertainty, two-stage stochastic programs are set up heading for unit commitment schedules which are both cost-effective and robust with respect to data perturbations. The impact of different, risk neutral and risk averse, stochastic criteria on the shapes of the optimal stochastic solutions will be examined. To tackle the resulting two-stage programs, we propose to approximate AC power flow by semidefinite relaxations. This leads to two-stage stochastic mixed-integer semidefinite programs having a special structure. To solve the latter, the L-shaped method and dual decomposition have been applied and compared. KW - Stochastic Programming KW - Semidefinite Programming KW - Decomposition Methods KW - Risk Aversion KW - Unit Commitment with AC Power Flow Y1 - 2017 ER - TY - JOUR A1 - Schultz, Rüdiger A1 - Wollenberg, Tobias T1 - Unit commitment under uncertainty in AC transmission systems via risk averse semidefinite stochastic programs JF - RAIRO-Operations Research N2 - This paper addresses unit commitment under uncertainty of load and power infeed from renewables in alternating current (AC) power systems. Beside traditional unit-commitment constraints, the physics of power flow are included. To gain globally optimal solutions a recent semidefinite programming approach is used, which leads us to risk averse two-stage stochastic mixed integer semidefinite programs for which a decomposition algorithm is presented. KW - Stochastic Programming KW - Semidefinite Programming KW - AC Power Flow Y1 - 2017 U6 - https://doi.org/https://doi.org/10.1051/ro/2016031 VL - 51 IS - 2 SP - 391 EP - 416 ER - TY - INPR A1 - Huck, Christoph A1 - Tischendorf, Caren T1 - Topology motivated discretization of hyperbolic PDAEs describing flow networks KW - PDAE KW - DAE KW - Index KW - discretization Y1 - 2017 ER - TY - INPR A1 - Aßmann, Denis A1 - Liers, Frauke A1 - Stingl, Michael A1 - Vera, Juan T1 - Deciding Robust Feasibility and Infeasibility Using a Set Containment Approach: An Application to Stationary Passive Gas Network Operations N2 - In this paper we study feasibility and infeasibility of nonlinear two-stage fully adjustable robust feasibility problems with an empty first stage. This is equivalent to deciding set containment of a projection of the feasible region and the uncertainty set. For answering this question, two very general approaches using methods from polynomial optimization are presented --- one for showing feasibility and one for showing infeasibility. The developed methods are approximated through sum of squares polynomials and solved using semidefinite programs. Deciding robust feasibility and infeasibility is important for gas network operations, which is a \nonconvex quadratic problem with absolute values functions. Concerning the gas network problem, different topologies are considered. It is shown that a tree structured network can be decided exactly using linear programming. Furthermore, a method is presented to reduce a tree network with one additional arc to a single cycle network. In this case, removing the absolute values and solving the problem can be decided with linearly many polynomial optimization problems. Lastly, the effectivity of the methods is tested on a variety of small cyclic networks. For instances where robust feasibility or infeasibility can be decided, level~2 or level~3 of the Lasserre relaxation hierarchy is typically sufficient. KW - robust optimization KW - polynomial optimization KW - stationary gas transport Y1 - 2017 ER - TY - INPR A1 - Gotzes, Claudia A1 - Nitsche, Sabrina A1 - Schultz, Rüdiger T1 - Probability of Feasible Loads in Passive Gas Networks with up to Three Cycles N2 - Gas networks are of growing importance for the economy and offer interesting mathematical problems at the same time. The classical linear network flow allows for approximate models that more and more have come to their limits. This has raised interest in nonlinear but, for simplicity, still steady-state models. The present paper aims at mobilizing techniques from symbolic computation and reparametrization of multivariate integrals to enable validation of stochastic nominations following Gaussian distributions in passive gas networks with more than one cycle. KW - Mathematical Gas Network Models, Stochastic Nomination Validation, Parametric Models Y1 - 2017 ER - TY - JOUR A1 - Grimm, Veronika A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Zöttl, Gregor T1 - A Multilevel Model of the European Entry-Exit Gas Market JF - Mathematical Methods of Operations Research N2 - In entry-exit gas markets as they are currently implemented in Europe, network constraints do not affect market interaction beyond the technical capacities determined by the TSO that restrict the quantities individual firms can trade at the market. It is an up to now unanswered question to what extent existing network capacity remains unused in an entry-exit design and to what extent feasible adjustments of the market design could alleviate inefficiencies. In this paper, we offer a four-level modeling framework that is capable of analyzing these issues and provide some first results on the model structure. In order to decouple gas trading from network congestion management, the TSO is required to determine technical capacities and corresponding booking fees at every entry and exit node up front. Firms book those capacities, which gives them the right to charge or discharge an amount of gas at a certain node up to this capacity in every scenario. Beyond these technical capacities and the resulting bookings, gas trade is unaffected by network constraints. The technical capacities have to ensure that transportation of traded quantities is always feasible. We assume that the TSO is regulated and determines technical capacities, fees, and transportation costs under a welfare objective. As a first step we moreover assume perfect competition among gas traders and show that the booking and nomination decisions can be analyzed in a single level. We prove that this aggregated model has a unique solution. We also show that the TSO's decisions can be subsumed in one level as well. If so, the model boils down to a mixed-integer nonlinear bilevel problem with robust aspects. In addition, we provide a first-best benchmark that allows to assess welfare losses that occur in an entry-exit system. Our approach provides a generic framework to analyze various aspects in the context of semi-liberalized gas markets. Therefore, we finally discuss and provide guidance on how to include several important aspects into the approach, such as network and production capacity investment, uncertain data, market power, and intra-day trading. KW - Entry-Exit System KW - Gas Market KW - Multilevel Modeling Y1 - 2017 IS - 89(2) SP - 223 EP - 255 ER - TY - INPR A1 - Hiller, Benjamin A1 - Koch, Thorsten A1 - Schewe, Lars A1 - Schwarz, Robert A1 - Schweiger, Jonas T1 - A System to Evaluate Gas Network Capacities: Concepts and Implementation T2 - EJOR N2 - Since 2005, the gas market in the European Union is liberalized and the trading of natural gas is decoupled from its transport. The transport is done by so-called transmissions system operators (TSOs). The market model established by the European Union views the gas transmission network as a black box, providing shippers (gas traders and consumers) the opportunity to transport gas from any entry to any exit. TSOs are required to offer maximum independent capacities at each entry and exit such that the resulting gas flows can be realized by the network without compromising security of supply. Therefore, evaluating the available transport capacities is extremely important to the TSOs. This paper gives an overview of the toolset for evaluating gas network capacities that has been developed within the ForNe project, a joint research project of seven research partners initiated by Open Grid Europe, Germany's biggest TSO. While most of the relevant mathematics is described in the book "Evaluating Gas Network Capacities", this article sketches the system as a whole, describes some developments that have taken place recently, and gives some details about the current implementation. Y1 - 2017 ER - TY - JOUR A1 - Domschke, Pia A1 - Dua, Aseem A1 - Stolwijk, Jeroen J. A1 - Lang, Jens A1 - Mehrmann, Volker T1 - Adaptive Refinement Strategies for the Simulation of Gas Flow in Networks using a Model Hierarchy N2 - A model hierarchy that is based on the one-dimensional isothermal Euler equations of fluid dynamics is used for the simulation and optimisation of gas flow through a pipeline network. Adaptive refinement strategies have the aim of bringing the simulation error below a prescribed tolerance while keeping the computational costs low. While spatial and temporal stepsize adaptivity is well studied in the literature, model adaptivity is a new field of research. The problem of finding an optimal refinement strategy that combines these three types of adaptivity is a generalisation of the unbounded knapsack problem. A refinement strategy that is currently used in gas flow simulation software is compared to two novel greedy-like strategies. Both a theoretical experiment and a realistic gas flow simulation show that the novel strategies significantly outperform the current refinement strategy with respect to the computational cost incurred. KW - gas supply networks KW - model hierarchy KW - error estimators KW - model adaptivity KW - refinement strategies Y1 - 2017 U6 - https://doi.org/10.1553/etna_vol48s97 VL - Electronic Transactions on Numerical Analysis IS - Vol. 48 SP - 97 EP - 113 ER - TY - JOUR A1 - Grimm, Veronika A1 - Kleinert, Thomas A1 - Liers, Frauke A1 - Schmidt, Martin A1 - Zöttl, Gregor T1 - Optimal Price Zones of Electricity Markets: A Mixed-Integer Multilevel Model and Global Solution Approaches JF - Optimization Methods and Software N2 - Mathematical modeling of market design issues in liberalized electricity markets often leads to mixed-integer nonlinear multilevel optimization problems for which no general-purpose solvers exist and which are intractable in general. In this work, we consider the problem of splitting a market area into a given number of price zones such that the resulting market design yields welfare-optimal outcomes. This problem leads to a challenging multilevel model that contains a graph-partitioning problem with multi-commodity flow connectivity constraints and nonlinearities due to proper economic modeling. Furthermore, it has highly symmetric solutions. We develop different problem-tailored solution approaches. In particular, we present an extended KKT transformation approach as well as a generalized Benders approach that both yield globally optimal solutions. These methods, enhanced with techniques such as symmetry breaking and primal heuristics, are evaluated in detail on academic as well as on realistic instances. It turns out that our approaches lead to effective solution methods for the difficult optimization tasks presented here, where the problem-specific generalized Benders approach performs considerably better than the methods based on KKT transformation. KW - Multilevel Optimization KW - Mixed-Integer Nonlinear Optimization KW - Graph Partitioning KW - Generalized Benders Decomposition KW - Electricity Market Design} Y1 - 2017 IS - 34(2) SP - 406 EP - 436 ER - TY - JOUR A1 - Gugat, Martin A1 - Hante, Falk T1 - Lipschitz Continuity of the Value Function in Mixed-Integer Optimal Control Problems JF - Mathematics of Control, Signals, and Systems Y1 - 2017 U6 - https://doi.org/10.1007/s00498-016-0183-4 VL - 29 IS - 1 ER - TY - INPR A1 - Stolwijk, Jeroen J. A1 - Mehrmann, Volker T1 - Error Analysis and Model Adaptivity for Flows in Gas Networks N2 - In the simulation and optimization of gas flow in a pipeline network, a hierarchy of models is used that employs different formulations of the Euler equations. While the optimization is performed on piecewise linear models, the flow simulation is based on the simulation of one to three dimensional Euler equations including the temperature distributions. To decide which model class in the hierarchy is adequate to achieve a desired accuracy, this paper presents an error and perturbation analysis for a two level model hierarchy including the isothermal Euler equations in semilinear form and the stationary Euler equations in purely algebraic form. The focus of the work is on the effect of data uncertainty, discretization and rounding errors in the numerical simulation of these models and their interaction. Two simple discretization schemes for the semilinear model are compared with respect to their conditioning and temporal stepsizes are determined for which a well-conditioned problem is obtained. The results are based on new componentwise relative condition numbers for the solution of nonlinear systems of equations. Moreover, the model error between the semilinear and the algebraic model is computed, the maximum pipeline length is determined for which the algebraic model can be used safely, and a condition is derived for which the isothermal model is adequate. KW - gas network KW - isothermal Euler equations KW - error analysis KW - condition number KW - data uncertainty Y1 - 2017 ER - TY - JOUR A1 - Schewe, Lars A1 - Schmidt, Martin T1 - Computing Feasible Points for Binary MINLPs with MPECs JF - Mathematical Programming Computation N2 - Nonconvex mixed-binary nonlinear optimization problems frequently appear in practice and are typically extremely hard to solve. In this paper we discuss a class of primal heuristics that are based on a reformulation of the problem as a mathematical program with equilibrium constraints. We then use different regularization schemes for this class of problems and use an iterative solution procedure for solving series of regularized problems. In the case of success, these procedures result in a feasible solution of the original mixed-binary nonlinear problem. Since we rely on local nonlinear programming solvers the resulting method is fast and we further improve its reliability by additional algorithmic techniques. We show the strength of our method by an extensive computational study on 662 MINLPLib2 instances, where our methods are able to produce feasible solutions for 60% of all instances in at most 10s. KW - Mixed-Integer Nonlinear Optimization KW - MINLP KW - MPEC KW - Complementarity Constraints KW - Primal Heuristic Y1 - 2016 IS - 11(1) SP - 95 EP - 118 ER - TY - JOUR A1 - Hante, Falk A1 - Leugering, Günter A1 - Martin, Alexander A1 - Schewe, Lars A1 - Schmidt, Martin T1 - Challenges in optimal control problems for gas and fluid flow in networks of pipes and canals: From modeling to industrial applications N2 - We consider optimal control problems for the flow of gas or fresh water in pipe networks as well as drainage or sewer systems in open canals. The equations of motion are taken to be represented by the nonlinear isothermal Euler gas equations, the water hammer equations, or the St.~Venant equations for flow. We formulate model hierarchies and derive an abstract model for such network flow problems including pipes, junctions, and controllable elements such as valves, weirs, pumps, as well as compressors. We use the abstract model to give an overview of the known results and challenges concerning equilibria, well-posedness, controllability, and optimal control. A major challenge concerning the optimization is to deal with switching on-off states that are inherent to controllable devices in such applications combined with continuous simulation and optimization of the gas flow. We formulate the corresponding mixed-integer nonlinear optimal control problems and outline a decomposition approach as a solution technique. KW - Networks KW - pipes KW - optimal control KW - Euler and St. Venant equations KW - hierarchy of models Y1 - 2016 ER - TY - JOUR A1 - Leövey, Hernan A1 - Heitsch, Holger A1 - Römisch, Werner T1 - Are Quasi-Monte Carlo algorithms efficient for two-stage stochastic programs? JF - Computational Optimization and Applications N2 - Quasi-Monte Carlo algorithms are studied for designing discrete approximations of two-stage linear stochastic programs with random right-hand side and continuous probability distribution. The latter should allow for a transformation to a distribution with independent marginals. The two-stage integrands are piecewise linear, but neither smooth nor lie in the function spaces considered for QMC error analysis. We show that under some weak geometric condition on the two-stage model all terms of their ANOVA decomposition, except the one of highest order, are continuously differentiable and that first and second order ANOVA terms have mixed first order partial derivatives and belong to L2 . Hence, randomly shifted lattice rules (SLR) may achieve the optimal rate of convergence O(n−1+δ ) with δ ∈ (0, 12 ] and a constant not depending on the dimension if the effective superposition dimension is at most two. We discuss effective dimensions and dimension reduction for two-stage integrands. The geometric condition is shown to be satisfied almost everywhere if the underlying probability distribution is normal and principal component analysis (PCA) is used for transforming the covariance matrix. Numerical experiments for a large scale two-stage stochastic production planning model with normal demand show that indeed convergence rates close to the optimal are achieved when using SLR and randomly scrambled Sobol’ point sets accompanied with PCA for dimension reduction. Y1 - 2016 U6 - https://doi.org/10.1007/s10589-016-9843-z VL - 65 IS - 3 SP - 567 EP - 603 ER - TY - JOUR A1 - Guigues, Vincent A1 - Henrion, Rene T1 - Joint dynamic probabilistic constraints with projected linear decision rules JF - Optimization Methods and Software N2 - We consider multistage stochastic linear optimization problems combining joint dynamic probabilistic constraints with hard constraints. We develop a method for projecting decision rules onto hard constraints of wait-and-see type. We establish the relation between the original (infinite dimensional) problem and approximating problems working with projections from different subclasses of decision policies. Considering the subclass of linear decision rules and a generalized linear model for the underlying stochastic process with noises that are Gaussian or truncated Gaussian, we show that the value and gradient of the objective and constraint functions of the approximating problems can be computed analytically. Y1 - 2016 U6 - https://doi.org/10.1080/10556788.2016.1233972 VL - 32 SP - 1006 EP - 1032 ER - TY - JOUR A1 - Diniz, Andre Luiz A1 - Henrion, Rene T1 - On probabilistic constraints with multivariate truncated Gaussian and lognormal distributions JF - Energy Systems N2 - Many engineering problems with uncertain data, notably arising in power management, can be formulated as optimization problems subject to probabilistic constraints. While dealing with such constraints under continuous distributions of the underlying random parameter remains a difficult task in general both from the numerical and theoretical point of view, quite some progress has been made in the special case of multivariate Gaussian distributions. These are not perfectly adequate, however, in many circumstances, in particular not, when modeling uncertain inflows to hydro reservoirs or uncertain demands in gas networks. Interesting alternatives are offered by truncations of multivariate Gaussian distributions to polyhedra or by multivariate lognormal distributions. The paper discusses the applicability of such distributions in the context of a simple joint linear probabilistic constraint putting the emphasis on the numerical approximation of probabilities and their gradients (w.r.t. decisions to be optimized) as well as on the convexity of the set of feasible decisions. Y1 - 2016 U6 - https://doi.org/10.1007/s12667-015-0180-6 VL - 8 SP - 149 EP - 167 ER - TY - JOUR A1 - van Ackooij, Wim A1 - Henrion, Rene T1 - (Sub-) Gradient formulae for probability functions of random inequality systems under Gaussian distribution JF - SIAM/ASA J. Uncertainty Quantification N2 - We consider probability functions of parameter-dependent random inequality systems under Gaussian distribution. As a main result, we provide an upper estimate for the Clarke subdifferential of such probability functions without imposing compactness conditions. A constraint qualification ensuring continuous differentiability is formulated. Explicit formulae are derived from the general result in case of linear random inequality systems. In the case of a constant coefficient matrix an upper estimate for even the smaller Mordukhovich subdifferential is proven. Y1 - 2017 U6 - https://doi.org/10.1137/16M1061308 VL - 5 SP - 63 EP - 87 ER - TY - JOUR A1 - Gotzes, Claudia A1 - Heitsch, Holger A1 - Henrion, Rene A1 - Schultz, Rüdiger T1 - On the quantification of nomination feasibility in stationary gas networks with random load JF - Mathematical Methods of Operations Research N2 - The paper considers the computation of the probability of feasible load constellations in a stationary gas network with uncertain demand. More precisely, a network with a single entry and several exits with uncertain loads is studied. Feasibility of a load constellation is understood in the sense of an existing flow meeting these loads along with given pressure bounds in the pipes. In a first step, feasibility of deterministic exit loads is characterized algebraically and these general conditions are specified to networks involving at most one cycle. This prerequisite is essential for determining probabilities in a stochastic setting when exit loads are assumed to follow some (joint) Gaussian distribution when modeling uncertain customer demand. The key of our approach is the application of the spheric-radial decomposition of Gaussian random vectors coupled with Quasi Monte-Carlo sampling. This approach requires an efficient algorithmic treatment of the mentioned algebraic relations moreover depending on a scalar parameter. Numerical results are illustrated for different network examples and demonstrate a clear superiority in terms of precision over simple generic Monte-Carlo sampling. They lead to fairly accurate probability values even for moderate sample size. Y1 - 2016 U6 - https://doi.org/10.1007/s00186-016-0564-y VL - 84 IS - 2 SP - 427 EP - 457 ER - TY - JOUR A1 - Gugat, Martin A1 - Zuazua, Enrique ED - Grimble, Mike J. T1 - Exact penalization of terminal constraints for optimal control problems JF - OPTIMAL CONTROL APPLICATIONS AND METHODS N2 - We study optimal control problems for linear systems with prescribed initial and terminal states. We analyze the exact penalization of the terminal constraints. We show that for systems that are exactly controllable, the norm-minimal exact control can be computed as the solution of an optimization problem without terminal constraint but with a nonsmooth penalization of the end conditions in the objective function, if the penalty parameter is sufficiently large. We describe the application of the method for hyperbolic and parabolic systems of partial differential equations, considering the wave and heat equations as particular examples. Copyright © 2016 John Wiley & Sons, Ltd. Y1 - 2016 U6 - https://doi.org/10.1002/oca.2238 VL - 37 IS - 6 SP - 1329 EP - 1354 ER - TY - CHAP A1 - Hante, Falk T1 - On the relaxation gap for PDE mixed-integer optimal control problems T2 - Proceedings in Applied Mathematics and Mechanics Y1 - 2016 U6 - https://doi.org/10.1002/pamm.201610380 VL - 16 SP - 783 EP - 784 ER - TY - JOUR A1 - Gugat, Martin A1 - Trelat, Emmanuel A1 - Zuazua, Enrique ED - Sepulchre, Rodolphe T1 - Optimal Neumann control for the 1D wave equation: Finite horizon, infinite horizon, boundary tracking terms and the turnpike property JF - Systems & Control Letters N2 - We consider a vibrating string that is fixed at one end with Neumann control action at the other end. We investigate the optimal control problem of steering this system from given initial data to rest, in time TT, by minimizing an objective functional that is the convex sum of the L2L2-norm of the control and of a boundary Neumann tracking term. We provide an explicit solution of this optimal control problem, showing that if the weight of the tracking term is positive, then the optimal control action is concentrated at the beginning and at the end of the time interval, and in-between it decays exponentially. We show that the optimal control can actually be written in that case as the sum of an exponentially decaying term and of an exponentially increasing term. This implies that, if the time TT is large, then the optimal trajectory approximately consists of three arcs, where the first and the third short-time arcs are transient arcs, and in the middle arc the optimal control and the corresponding state are exponentially close to 00. This is an example of a turnpike phenomenon for a problem of optimal boundary control. If T=+∞T=+∞ (infinite time horizon problem), then only the exponentially decaying component of the control remains, and the norms of the optimal control action and of the optimal state decay exponentially in time. In contrast to this situation, if the weight of the tracking term is zero and only the control cost is minimized, then the optimal control is distributed uniformly along the whole interval [0,T][0,T] and coincides with the control given by the Hilbert Uniqueness Method. In addition, we establish a similarity theorem stating that, for every T>0T>0, there exists an appropriate weight λ<1λ<1 for which the optimal solutions of the corresponding finite horizon optimal control problem and of the infinite horizon optimal control problem coincide along the first part of the time interval [0,2][0,2]. We also discuss the turnpike phenomenon from the perspective of a general framework with a strongly continuous semi-group. KW - Neumann boundary control KW - Turnpike phenomenon KW - Exact control Y1 - 2016 U6 - https://doi.org/10.1016/j.sysconle.2016.02.001 VL - 90 SP - 61 EP - 70 ER - TY - JOUR A1 - Grimm, Veronika A1 - Schewe, Lars A1 - Schmidt, Martin A1 - Zöttl, Gregor T1 - Uniqueness of Market Equilibrium on a Network: A Peak-Load Pricing Approach JF - European Journal of Operational Research N2 - In this paper we analyze peak-load pricing in the presence of network constraints. In our setup, firms facing fluctuating demand decide on the size and location of production facilities. They make production decisions constrained by the invested capacities, taking into account that market prices reflect scarce transmission capacities. We state general conditions for existence and uniqueness of the market equilibrium and provide a characterization of equilibrium investment and production. The presented analysis covers the cases of perfect competition and monopoly - the case of strategic firms is approximated by a conjectural variations approach. Our result is a prerequisite for analyzing regulatory policy options with computational multilevel equilibrium models, since uniqueness of the equilibrium at lower levels is of key importance when solving these models. Thus, our paper contributes to an evolving strand of literature that analyzes regulatory policy based on computational multilevel equilibrium models and aims at taking into account individual objectives of various agents, among them not only generators and customers but also, e.g., the regulator deciding on network expansion. KW - Pricing KW - Peak-Load Pricing KW - Networks KW - Uniqueness Y1 - 2017 U6 - https://doi.org/10.1016/j.ejor.2017.03.036 VL - 261 IS - 3 SP - 971 EP - 983 ER - TY - JOUR A1 - Grimm, Veronika A1 - Martin, Alexander A1 - Schmidt, Martin A1 - Weibelzahl, Martin A1 - Zöttl, Gregor T1 - Transmission and generation investment in electricity markets: The effects of market splitting and network fee regimes JF - European Journal of Operational Research N2 - We propose an equilibrium model that allows to analyze the long-run impact of the electricity market design on transmission line expansion by the regulator and investment in generation capacity by private firms in liberalized electricity markets. The model incorporates investment decisions of the transmission system operator and private firms in expectation of an energy-only market and cost-based redispatch. In different specifications we consider the cases of one vs. multiple price zones (market splitting) and analyze different approaches to recover network cost—in particular lump sum, generation capacity based, and energy based fees. In order to compare the outcomes of our multilevel market model with a first best benchmark, we also solve the corresponding integrated planner problem. Using two test networks we illustrate that energy-only markets can lead to suboptimal locational decisions for generation capacity and thus imply excessive network expansion. Market splitting heals these problems only partially. These results are valid for all considered types of network tariffs, although investment slightly differs across those regimes. KW - Electricity market modeling KW - Mixed-integer nonlinear optimization KW - Multilevel programming KW - Network expansion KW - Transmission management Y1 - 2016 U6 - https://doi.org/10.1016/j.ejor.2016.03.044 VL - 254 IS - 2 SP - 493 EP - 509 ER - TY - JOUR A1 - Geißler, Björn A1 - Morsi, Antonio A1 - Schewe, Lars A1 - Schmidt, Martin T1 - Penalty Alternating Direction Methods for Mixed-Integer Optimization: A New View on Feasibility Pumps JF - SIAM Journal on Optimization N2 - Feasibility pumps are highly effective primal heuristics for mixed-integer linear and nonlinear optimization. However, despite their success in practice there are only few works considering their theoretical properties. We show that feasibility pumps can be seen as alternating direction methods applied to special reformulations of the original problem, inheriting the convergence theory of these methods. Moreover, we propose a novel penalty framework that encompasses this alternating direction method, which allows us to refrain from random perturbations that are applied in standard versions of feasibility pumps in case of failure. We present a convergence theory for the new penalty based alternating direction method and compare the new variant of the feasibility pump with existing versions in an extensive numerical study for mixed-integer linear and nonlinear problems. Y1 - 2017 U6 - https://doi.org/10.1137/16M1069687 VL - 27 IS - 3 SP - 1611 EP - 1636 ER - TY - JOUR A1 - Geißler, Björn A1 - Morsi, Antonio A1 - Schewe, Lars A1 - Schmidt, Martin T1 - Solving Highly Detailed Gas Transport MINLPs: Block Separability and Penalty Alternating Direction Methods JF - INFORMS Journal on Computing N2 - Detailed modeling of gas transport problems leads to nonlinear and nonconvex mixed-integer optimization or feasibility models (MINLPs) because both the incorporation of discrete controls of the network as well as accurate physical and technical modeling is required in order to achieve practical solutions. Hence, ignoring certain parts of the physics model is not valid for practice. In the present contribution we extend an approach based on linear relaxations of the underlying nonlinearities by tailored model reformulation techniques yielding block-separable MINLPs. This combination of techniques allows us to apply a penalty alternating direction method and thus to solve highly detailed MINLPs for large-scale real-world instances. The practical strength of the proposed method is demonstrated by a computational study in which we apply the method to instances from steady-state gas transport including both pooling effects with respect to the mixing of gases of different composition and a highly detailed compressor station model. Y1 - 2016 IS - 30(2) SP - 309 EP - 323 ER - TY - CHAP A1 - Gamrath, Gerald A1 - Hiller, Benjamin A1 - Witzig, Jakob T1 - Reoptimization Techniques for MIP Solvers T2 - Lecture Notes in Computer Science N2 - Recently, there have been many successful applications of optimization algorithms that solve a sequence of quite similar mixed-integer programs (MIPs) as subproblems. Traditionally, each problem in the sequence is solved from scratch. In this paper we consider reoptimization techniques that try to benefit from information obtained by solving previous problems of the sequence. We focus on the case that subsequent MIPs differ only in the objective function or that the feasible region is reduced. We propose extensions of the very complex Branch&Bound algorithms employed by general MIP solvers based on the idea to ``warmstart'' using the final search frontier of the preceding solver run. We extend the academic MIP solver SCIP by these techniques to obtain a reoptimizing Branch&Bound solver and report computational results which show the effectiveness of the approach. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-319-20086-6_14 VL - 9125 PB - Springer ER - TY - INPR A1 - Hiller, Benjamin A1 - Saitenmacher, René A1 - Walther, Tom T1 - Analysis of operating modes of complex compressor stations N2 - We consider the modeling of operation modes for complex compressor stations (i.e. ones with several in- or outlets) in gas networks. In particular, we propose a refined model that allows to precompute tighter relaxations for each operation mode. These relaxations may be used to strengthen the compressor station submodels in gas network optimization problems. We provide a procedure to obtain the refined model from the input data for the original model. Y1 - 2016 ER - TY - JOUR A1 - Schweiger, Jonas A1 - Liers, Frauke T1 - A Decomposition Approach for Optimum Gas Network Extension with a Finite Set of Demand Scenarios N2 - Today's gas markets demand more flexibility from the network operators which in turn have to invest into their network infrastructure. As these investments are very cost-intensive and long-living, network extensions should not only focus on a single bottleneck scenario, but should increase the flexibility to fulfill different demand scenarios. In this work, we formulate a model for the network extension problem for multiple demand scenarios and propose a scenario decomposition in order to solve the arising challenging optimization tasks. In fact, euch subproblem consists of a mixed-integer nonlinear optimization problem (MINLP). Valid bounds are derived even without solving the subproblems to optimality. Furthermore, we develop heuristics that prove capable of improving the initial solutions substantially. Results of computational experiments on realistic network topologies are presented. It turns out that our method is able to solve these challenging instances to optimality within a reasonable amount of time. Y1 - 2016 ER - TY - JOUR A1 - Hante, Falk A1 - Mommer, Mario A1 - Potschka, Andreas T1 - Newton-Picard preconditioners for time-periodic, parabolic optimal control problems JF - SIAM Journal on Numerical Analysis Y1 - 2016 U6 - https://doi.org/10.1137/140967969 VL - 53 IS - 5 SP - 2206 EP - 2225 ER - TY - JOUR A1 - Domschke, Pia A1 - Groß, Martin A1 - Hiller, Benjamin A1 - Hante, Falk A1 - Schewe, Lars A1 - Schmidt, Martin T1 - Mathematische Modellierung, Simulation und Optimierung von Gastransportnetzwerken JF - gwf-gas/erdgas Y1 - 2015 VL - 11 SP - 880 EP - 885 ER - TY - RPRT A1 - Arulselvan, Ashwin A1 - Cseh, Ágnes A1 - Groß, Martin A1 - Manlove, David F. A1 - Matuschke, Jannik T1 - Matchings with lower quotas: Algorithms and complexity N2 - We study a natural generalization of the maximum weight many-to-one matching problem. We are given an undirected bipartite graph G = (A∪P,E) with weights on the edges in E, and with lower and upper quotas on the vertices in P. We seek a maximum weight many-to-one matching satisfying two sets of constraints: vertices in A are incident to at most one matching edge, while vertices in P are either unmatched or they are incident to a number of matching edges between their lower and upper quota. This problem, which we call maximum weight many-to-one matching with lower and upper quotas (wmlq), has applications to the assignment of students to projects within university courses, where there are constraints on the minimum and maximum numbers of students that must be assigned to each project. In this paper, we provide a comprehensive analysis of the complexity of wmlq from the viewpoints of classical polynomial time algorithms, fixed-parameter tractability, as well as approximability. We draw the line between NP-hard and polynomially tractable instances in terms of degree and quota constraints and provide efficient algorithms to solve the tractable ones. We further show that the problem can be solved in polynomial time for instances with bounded treewidth; however, the corresponding runtime is exponential in the treewidth with the maximum upper quota u_max as basis, and we prove that this dependence is necessary unless FPT = W[1]. The approximability of wmlq is also discussed: we present an approximation algorithm for the general case with performance guarantee umax + 1, which is asymptotically best possible unless P = NP. Finally, we elaborate on how most of our positive results carry over to matchings in arbitrary graphs with lower quotas. Y1 - 2016 U6 - https://doi.org/10.1007/978-3-662-48971-0_16 ER - TY - INPR A1 - Bärmann, Andreas A1 - Gellermann, Thorsten A1 - Merkert, Maximilian A1 - Schneider, Oskar T1 - Staircase Compatibility and its Applications in Scheduling and Piecewise Linearization N2 - We consider the clique problem with multiple-choice constraints (CPMC) and characterize a case where it is possible to give an efficient description of the convex hull of its feasible solutions. This case, which we call staircase compatibility, generalizes common properties in applications and allows for a linear description of the integer feasible solutions to (CPMC) with a totally unimodular constraint matrix of polynomial size. We derive two such totally unimodular reformulations for the problem: one that is obtained by a strengthening of the compatibility constraints and one that is based on a representation as a dual network flow problem. Furthermore, we show a natural way to derive integral solutions from fractional solutions to the problem by determining integral extreme points generating this fractional solution. We also evaluate our reformulations from a computational point of view by applying them to two different real-world applications. The first one is a problem in railway timetabling where we try to adapt a given timetable slightly such that energy costs from operating the trains are reduced. The second one is the piecewise linearization of non-linear flow problems on a gas network. In both cases, we are able to reduce the solution times significantly by passing to the theoretically stronger formulations of the problem. KW - Clique Problem KW - Multiple-Choice Constraints KW - Total Unimodularity KW - Scheduling KW - Piecewise Linearization Y1 - 2016 ER - TY - JOUR A1 - Liers, Frauke A1 - Merkert, Maximilian T1 - Structural Investigation of Piecewise Linearized Network Flow Problems N2 - In this work we study polyhedra in the context of network flow problems, where the flow value on each arc lies in one of several predefined intervals. This is motivated by nonlinear problems on transportation networks, where nonlinearities are handled by piecewise linear approximation or relaxation - a common and established approach in many applications. Several methods for modeling piecewise linear functions are known which provide a complete description for a single network arc. However, in general this property is lost when considering multiple arcs. We show how to strengthen the formulation for specific substructures consisting of multiple arcs by linear inequalities. For the case of paths of degree-two-nodes we give a complete description of the polyhedron projected to the integer variables. Our model is based on - but not limited to - the multiple choice method; we also show how to transfer our results to a formulation based on the incremental method. Computational results show that a state-of-the-art MIP-solver greatly benefits from using our cutting planes for random and realistic network topologies. KW - Combinatorial optimization KW - Complete description KW - Network flow problems KW - Piecewise linear functions Y1 - 2016 U6 - https://doi.org/10.1137/15M1006751 VL - 26 SP - 2863 EP - 2886 ER - TY - JOUR A1 - Bärmann, Andreas A1 - Liers, Frauke A1 - Martin, Alexander A1 - Merkert, Maximilian A1 - Thurner, Christoph A1 - Weninger, Dieter T1 - Solving network design problems via iterative aggregation JF - Mathematical Programming Computation N2 - In this work, we present an exact approach for solving network design problems that is based on an iterative graph aggregation procedure. The scheme allows existing preinstalled capacities. Starting with an initial aggregation, we solve a sequence of network design master problems over increasingly fine-grained representations of the original network. In each step, a subproblem is solved that either proves optimality of the solution or gives a directive where to refine the representation of the network in the subsequent iteration. The algorithm terminates with a globally optimal solution to the original problem. Our implementation uses a standard integer programming solver for solving the master problems as well as the subproblems. The computational results on random and realistic instances confirm the profitable use of the iterative aggregation technique. The computing time often reduces drastically when our method is compared to solving the original problem from scratch. KW - Aggregation KW - Network design KW - Combinatorial optimization KW - Mixed-integer programming KW - Branch-and-cut Y1 - 2015 U6 - https://doi.org/10.1007/s12532-015-0079-1 VL - 7 IS - 2 SP - 189 EP - 217 ER - TY - JOUR A1 - Gugat, Martin A1 - Wintergerst, David A1 - Schultz, Rüdiger ED - Iske, Armin T1 - Networks of pipelines for gas with nonconstant compressibility factor: stationary states JF - Computational and Applied Mathematics N2 - For the management of gas transportation networks, it is essential to know how the stationary states of the system are determined by the boundary data. The isothermal Euler equations are an accurate pde-model for the gas flow through each pipe. A compressibility factor is used to model the nonlinear relationship between density and pressure that occurs in real gas in contrast to ideal gas. The gas flow through the nodes is governed by algebraic node conditions that require the conservation of mass and the continuity of the pressure. We examine networks that are described by arbitrary finite graphs and show that for suitably chosen boundary data, subsonic stationary states exist and are uniquely determined by the boundary data. Our construction of the stationary states is based upon explicit representations of the stationary states on each single pipe that can easily be evaluated numerically. We also use the monotonicity properties of these states as functions of the boundary data. Y1 - 2016 U6 - https://doi.org/10.1007/s40314-016-0383-z ER -