TY - JOUR A1 - Sagnol, Guillaume A1 - Blanco, Marco A1 - Sauvage, Thibaut T1 - The Cone of Flow Matrices: Approximation Hierarchies and Applications JF - Networks N2 - Let G be a directed acyclic graph with n arcs, a source s and a sink t. We introduce the cone K of flow matrices, which is a polyhedral cone generated by the matrices $\vec{1}_P\vec{1}_P^T\in\RR^{n\times n}$, where $\vec{1}_P\in\RR^n$ is the incidence vector of the (s,t)-path P. We show that several hard flow (or path) optimization problems, that cannot be solved by using the standard arc-representation of a flow, reduce to a linear optimization problem over $\mathcal{K}$. This cone is intractable: we prove that the membership problem associated to $\mathcal{K}$ is NP-complete. However, the affine hull of this cone admits a nice description, and we give an algorithm which computes in polynomial-time the decomposition of a matrix $X\in \operatorname{span} \mathcal{K}$ as a linear combination of some $\vec{1}_P\vec{1}_P^T$'s. Then, we provide two convergent approximation hierarchies, one of them based on a completely positive representation of~K. We illustrate this approach by computing bounds for the quadratic shortest path problem, as well as a maximum flow problem with pairwise arc-capacities. Y1 - 2018 U6 - https://doi.org/10.1002/net.21820 VL - 72 IS - 1 SP - 128 EP - 150 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Blanco, Marco A1 - Sauvage, Thibaut T1 - Approximation Hierarchies for the cone of flow matrices N2 - Let $G$ be a directed acyclic graph with $n$ arcs, a source $s$ and a sink $t$. We introduce the cone $K$ of flow matrices, which is a polyhedral cone generated by the matrices $1_P 1_P^T \in R^{n\times n}$, where $1_P\in R^n$ is the incidence vector of the $(s,t)$-path $P$. Several combinatorial problems reduce to a linear optimization problem over $K$. This cone is intractable, but we provide two convergent approximation hierarchies, one of them based on a completely positive representation of $K$. We illustrate this approach by computing bounds for a maximum flow problem with pairwise arc-capacities. T3 - ZIB-Report - 18-20 KW - Flows in graphs KW - Approximation hierarchies KW - Copositive programming Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-68424 SN - 1438-0064 ER - TY - CHAP A1 - Sagnol, Guillaume A1 - Blanco, Marco A1 - Sauvage, Thibaut T1 - Approximation Hierarchies for the cone of flow matrices T2 - INOC 2017 – 8th International Network Optimization Conference N2 - Let $G$ be a directed acyclic graph with $n$ arcs, a source $s$ and a sink $t$. We introduce the cone $K$ of flow matrices, which is a polyhedral cone generated by the matrices $1_P 1_P^T \in R^{n\times n}$, where $1_P\in R^n$ is the incidence vector of the $(s,t)$-path $P$. Several combinatorial problems reduce to a linear optimization problem over $K$. This cone is intractable, but we provide two convergent approximation hierarchies, one of them based on a completely positive representation of $K$. We illustrate this approach by computing bounds for a maximum flow problem with pairwise arc-capacities. Y1 - 2018 U6 - https://doi.org/10.1016/j.endm.2018.02.002 VL - 64 SP - 275 EP - 284 ER - TY - JOUR A1 - Sagnol, Guillaume A1 - Barner, Christoph A1 - Borndörfer, Ralf A1 - Grima, Mickaël A1 - Seeling, Mathes A1 - Spies, Claudia A1 - Wernecke, Klaus T1 - Robust Allocation of Operating Rooms: a Cutting Plane Approach to handle Lognormal Case Durations JF - European Journal of Operational Research N2 - The problem of allocating operating rooms (OR) to surgical cases is a challenging task, involving both combinatorial aspects and uncertainty handling. We formulate this problem as a parallel machines scheduling problem, in which job durations follow a lognormal distribution, and a fixed assignment of jobs to machines must be computed. We propose a cutting-plane approach to solve the robust counterpart of this optimization problem. To this end, we develop an algorithm based on fixed-point iterations that identifies worst-case scenarios and generates cut inequalities. The main result of this article uses Hilbert's projective geometry to prove the convergence of this procedure under mild conditions. We also propose two exact solution methods for a similar problem, but with a polyhedral uncertainty set, for which only approximation approaches were known. Our model can be extended to balance the load over several planning periods in a rolling horizon. We present extensive numerical experiments for instances based on real data from a major hospital in Berlin. In particular, we find that: (i) our approach performs well compared to a previous model that ignored the distribution of case durations; (ii) compared to an alternative stochastic programming approach, robust optimization yields solutions that are more robust against uncertainty, at a small price in terms of average cost; (iii) the \emph{longest expected processing time first} (LEPT) heuristic performs well and efficiently protects against extreme scenarios, but only if a good prediction model for the durations is available. Finally, we draw a number of managerial implications from these observations. Y1 - 2018 U6 - https://doi.org/10.1016/j.ejor.2018.05.022 VL - 271 IS - 2 SP - 420 EP - 435 ER - TY - CHAP A1 - Sagnol, Guillaume A1 - Hege, Hans-Christian A1 - Weiser, Martin T1 - Using sparse kernels to design computer experiments with tunable precision T2 - 22nd Intern. Conf. on Computational Statistics - COMPSTAT 2016, Oviedo, Spain, 23-26 August 2016, Proceedings ISBN 978-90-73592-36-0 Y1 - 2016 SP - 397 EP - 408 ER - TY - CHAP A1 - Sagnol, Guillaume T1 - Network-related problems in optimal experimental design and second order cone programming T2 - Proceedings of PROBASTAT'2011, Tatra Mountains Mathematical Publications N2 - In the past few years several applications of optimal experimental designs have emerged to optimize the measurements in communication networks. The optimal design problems arising from this kind of applications share three interesting properties: (i) measurements are only available at a small number of locations of the network; (ii) each monitor can simultaneously measure several quantities, which can be modeled by ``multiresponse experiments"; (iii) the observation matrices depend on the topology of the network. In this paper, we give an overview of these experimental design problems and recall recent results for the computation of optimal designs by Second Order Cone Programming (SOCP). New results for the network-monitoring of a discrete time process are presented. In particular, we show that the optimal design problem for the monitoring of an AR1 process can be reduced to the standard form and we give experimental results. Y1 - 2012 U6 - https://doi.org/10.2478/v10127-012-0016-x VL - 51 SP - 161 EP - 171 ER - TY - JOUR A1 - Sagnol, Guillaume T1 - On the semidefinite representation of real functions applied to symmetric matrices JF - Linear Algebra and its Applications N2 - We present a new semidefinite representation for the trace of a real function f applied to symmetric matrices, when a semidefinite representation of the convex function f is known. Our construction is intuitive, and yields a representation that is more compact than the previously known one. We also show with the help of matrix geometric means and the Riemannian metric of the set of positive definite matrices that for a rational number p in the interval (0,1], the matrix X raised to the exponent p is the largest element of a set represented by linear matrix inequalities. We give numerical results for a problem inspired from the theory of experimental designs, which show that the new semidefinite programming formulation yields a speed-up factor in the order of 10. Y1 - 2013 U6 - https://doi.org/10.1016/j.laa.2013.08.021 VL - 439 IS - 10 SP - 2829 EP - 2843 ER - TY - JOUR A1 - Sagnol, Guillaume T1 - Approximation of a maximum-submodular-coverage problem involving spectral functions, with application to experimental designs JF - Discrete Applied Mathematics N2 - We study a family of combinatorial optimization problems defined by a parameter $p\in[0,1]$, which involves spectral functions applied to positive semidefinite matrices, and has some application in the theory of optimal experimental design. This family of problems tends to a generalization of the classical maximum coverage problem as $p$ goes to $0$, and to a trivial instance of the knapsack problem as $p$ goes to $1$. In this article, we establish a matrix inequality which shows that the objective function is submodular for all $p\in[0,1]$, from which it follows that the greedy approach, which has often been used for this problem, always gives a design within $1-1/e$ of the optimum. We next study the design found by rounding the solution of the continuous relaxed problem, an approach which has been applied by several authors. We prove an inequality which generalizes a classical result from the theory of optimal designs, and allows us to give a rounding procedure with an approximation factor which tends to $1$ as $p$ goes to $1$. Y1 - 2013 U6 - https://doi.org/10.1016/j.dam.2012.07.016 VL - 161 IS - 1-2 SP - 258 EP - 276 ER - TY - JOUR A1 - Sagnol, Guillaume T1 - A class of Semidefinite Programs with rank-one solutions JF - Linear Algebra and its Applications N2 - We show that a class of semidefinite programs (SDP) admits a solution that is a positive semidefinite matrix of rank at most $r$, where $r$ is the rank of the matrix involved in the objective function of the SDP. The optimization problems of this class are semidefinite packing problems, which are the SDP analogs to vector packing problems. Of particular interest is the case in which our result guarantees the existence of a solution of rank one: we show that the computation of this solution actually reduces to a Second Order Cone Program (SOCP). We point out an application in statistics, in the optimal design of experiments. Y1 - 2011 U6 - https://doi.org/10.1016/j.laa.2011.03.027 VL - 435 IS - 6 SP - 1446 EP - 1463 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Harman, Radoslav T1 - Computing exact D-optimal designs by mixed integer second order cone programming N2 - Let the design of an experiment be represented by an $s$-dimensional vector $\vec{w}$ of weights with non-negative components. Let the quality of $\vec{w}$ for the estimation of the parameters of the statistical model be measured by the criterion of $D$-optimality defined as the $m$-th root of the determinant of the information matrix $M(\vec{w})=\sum_{i=1}^s w_iA_iA_i^T$, where $A_i$, $i=1,...,s$, are known matrices with $m$ rows. In the paper, we show that the criterion of $D$-optimality is second-order cone representable. As a result, the method of second order cone programming can be used to compute an approximate $D$-optimal design with any system of linear constraints on the vector of weights. More importantly, the proposed characterization allows us to compute an \emph{exact} $D$-optimal design, which is possible thanks to high-quality branch-and-cut solvers specialized to solve mixed integer second order cone problems. We prove that some other widely used criteria are also second order cone representable, for instance the criteria of $A$-, and $G$-optimality, as well as the criteria of $D_K$- and $A_K$-optimality, which are extensions of $D$-, and $A$-optimality used in the case when only a specific system of linear combinations of parameters is of interest. We present several numerical examples demonstrating the efficiency and universality of the proposed method. We show that in many cases the mixed integer second order cone programming approach allows us to find a provably optimal exact design, while the standard heuristics systematically miss the optimum. T3 - ZIB-Report - 13-39 KW - Optimal Design of Experiments KW - D-optimality KW - SOCP Representability KW - Exact Designs Y1 - 2013 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-41932 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Barner, Christoph A1 - Borndörfer, Ralf A1 - Grima, Mickaël A1 - Seeling, Matthes A1 - Spies, Claudia A1 - Wernecke, Klaus T1 - Robust Allocation of Operating Rooms: a Cutting Plane Approach to handle Lognormal Case Durations N2 - The problem of allocating operating rooms (OR) to surgical cases is a challenging task, involving both combinatorial aspects and uncertainty handling. We formulate this problem as a parallel machines scheduling problem, in which job durations follow a lognormal distribution, and a fixed assignment of jobs to machines must be computed. We propose a cutting-plane approach to solve the robust counterpart of this optimization problem. To this end, we develop an algorithm based on fixed-point iterations that identifies worst-case scenarios and generates cut inequalities. The main result of this article uses Hilbert's projective geometry to prove the convergence of this procedure under mild conditions. We also propose two exact solution methods for a similar problem, but with a polyhedral uncertainty set, for which only approximation approaches were known. Our model can be extended to balance the load over several planning periods in a rolling horizon. We present extensive numerical experiments for instances based on real data from a major hospital in Berlin. In particular, we find that: (i) our approach performs well compared to a previous model that ignored the distribution of case durations; (ii) compared to an alternative stochastic programming approach, robust optimization yields solutions that are more robust against uncertainty, at a small price in terms of average cost; (iii) the \emph{longest expected processing time first} (LEPT) heuristic performs well and efficiently protects against extreme scenarios, but only if a good prediction model for the durations is available. Finally, we draw a number of managerial implications from these observations. T3 - ZIB-Report - 16-18 KW - robust optimization KW - lognormal duration KW - Hilbert's projective metric Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-58502 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Borndörfer, Ralf A1 - Grima, Mickaël A1 - Seeling, Matthes A1 - Spies, Claudia T1 - Robust Allocation of Operating Rooms with Lognormal case Durations N2 - The problem of allocating operating rooms (OR) to surgical cases is a challenging task, involving both combinatorial aspects and uncertainty handling. In this article, we formulate this problem as a job shop scheduling problem, in which the job durations follow a lognormal distribution. We propose to use a cutting-plane approach to solve a robust version of this optimization problem. To this end, we develop an algorithm based on fixed-point iterations to solve the subproblems that identify worst-case scenarios and generate cut inequalities. The procedure is illustrated with numerical experiments based on real data from a major hospital in Berlin. T3 - ZIB-Report - 16-16 KW - robust optimization KW - lognormal duration KW - Hilbert's projective metric Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-58497 UR - http://www.pms2016.com/congreso/ficha.en.html SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume T1 - Picos Documentation. Release 0.1.1. N2 - PICOS is a user friendly interface to several conic and integer programming solvers, very much like YALMIP under MATLAB. The main motivation for PICOS is to have the possibility to enter an optimization problem as a high level model, and to be able to solve it with several different solvers. Multidimensional and matrix variables are handled in a natural fashion, which makes it painless to formulate a SDP or a SOCP. This is very useful for educational purposes, and to quickly implement some models and test their validity on simple examples. Furthermore, with PICOS you can take advantage of the python programming language to read and write data, construct a list of constraints by using python list comprehensions, take slices of multidimensional variables, etc. T3 - ZIB-Report - 12-48 KW - mathematical programming KW - SDP KW - SOCP KW - python Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-17396 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume T1 - On the semidefinite representations of real functions applied to symmetric matrices N2 - We present a new semidefinite representation for the trace of a real function f applied to symmetric matrices, when a semidefinite representation of the convex function f is known. Our construction is intuitive, and yields a representation that is more compact than the previously known one. We also show with the help of matrix geometric means and the Riemannian metric of the set of positive definite matrices that for a rational number p in the interval (0,1], the matrix X raised to the exponent p is the largest element of a set represented by linear matrix inequalities. We give numerical results for a problem inspired from the theory of experimental designs, which show that the new semidefinite programming formulation yields a speed-up factor in the order of 10. T3 - ZIB-Report - 12-50 KW - semidefinite representability KW - optimal experimental designs KW - SDP KW - matrix geometric mean Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-17511 SN - 1438-0064 VL - 439 SP - 2829 EP - 2843 ER - TY - GEN A1 - Borndörfer, Ralf A1 - Buwaya, Julia A1 - Sagnol, Guillaume A1 - Swarat, Elmar T1 - Network Spot Checking Games: Theory and Application to Toll Enforcing in Transportation Networks N2 - We introduce the class of spot-checking games (SC games). These games model problems where the goal is to distribute fare inspectors over a toll network. In an SC game, the pure strategies of network users correspond to paths in a graph, and the pure strategies of the inspectors are subset of edges to be controlled. Although SC games are not zero-sum, we show that a Nash equilibrium can be computed by linear programming. The computation of a strong Stackelberg equilibrium is more relevant for this problem, but we show that this is NP-hard. However, we give some bounds on the \emph{price of spite}, which measures how the payoff of the inspector degrades when committing to a Nash equilibrium. Finally, we demonstrate the quality of these bounds for a real-world application, namely the enforcement of a truck toll on German motorways. T3 - ZIB-Report - 14-07 KW - Game Theory KW - Stackelberg Equilibrium KW - Security Games KW - Mixed Integer Programming KW - Price of Anarchy Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-47139 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Balzer, Felix A1 - Borndörfer, Ralf A1 - Spies, Claudia A1 - von Dincklage, Falk T1 - Makespan and Tardiness in Activity Networks with Lognormal Activity Durations N2 - We propose an algorithm to approximate the distribution of the completion time (makespan) and the tardiness costs of a project, when durations are lognormally distributed. This problem arises naturally for the optimization of surgery scheduling, where it is very common to assume lognormal procedure times. We present an analogous of Clark's formulas to compute the moments of the maximum of a set of lognormal variables. Then, we use moment matching formulas to approximate the earliest starting time of each activity of the project by a shifted lognormal variable. This approach can be seen as a lognormal variant of a state-of-the-art method used for the statistical static timing analysis (SSTA) of digital circuits. We carried out numerical experiments with instances based on real data from the application to surgery scheduling. We obtained very promising results, especially for the approximation of the mean overtime in operating rooms, for which our algorithm yields results of a similar quality to Monte-Carlo simulations requiring an amount of computing time several orders of magnitude larger. T3 - ZIB-Report - 16-23 KW - scheduling KW - activity network KW - lognormal distribution Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-59290 SN - 1438-0064 ER - TY - GEN A1 - Sagnol, Guillaume A1 - Hege, Hans-Christian A1 - Weiser, Martin T1 - Using sparse kernels to design computer experiments with tunable precision N2 - Statistical methods to design computer experiments usually rely on a Gaussian process (GP) surrogate model, and typically aim at selecting design points (combinations of algorithmic and model parameters) that minimize the average prediction variance, or maximize the prediction accuracy for the hyperparameters of the GP surrogate. In many applications, experiments have a tunable precision, in the sense that one software parameter controls the tradeoff between accuracy and computing time (e.g., mesh size in FEM simulations or number of Monte-Carlo samples). We formulate the problem of allocating a budget of computing time over a finite set of candidate points for the goals mentioned above. This is a continuous optimization problem, which is moreover convex whenever the tradeoff function accuracy vs. computing time is concave. On the other hand, using non-concave weight functions can help to identify sparse designs. In addition, using sparse kernel approximations drastically reduce the cost per iteration of the multiplicative weights updates that can be used to solve this problem. T3 - ZIB-Report - 16-33 KW - Optimal design of computer experiments KW - Sparse kernels KW - Gaussian Process Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-59605 SN - 1438-0064 ER - TY - JOUR A1 - Sagnol, Guillaume A1 - Harman, Radoslav T1 - Computing exact D-optimal designs by mixed integer second-order cone programming JF - The Annals of Statistics N2 - Let the design of an experiment be represented by an $s-$dimensional vector $w$ of weights with nonnegative components. Let the quality of $w$ for the estimation of the parameters of the statistical model be measured by the criterion of $D-$optimality, defined as the $m$th root of the determinant of the information matrix $M(w)=\sum_{i=1}^s w_i A_i A_i^T$, where $A_i$,$i=1,\ldots,s$ are known matrices with $m$ rows. In this paper, we show that the criterion of $D-$optimality is second-order cone representable. As a result, the method of second-order cone programming can be used to compute an approximate $D-$optimal design with any system of linear constraints on the vector of weights. More importantly, the proposed characterization allows us to compute an exact $D-$optimal design, which is possible thanks to high-quality branch-and-cut solvers specialized to solve mixed integer second-order cone programming problems. Our results extend to the case of the criterion of $D_K-$optimality, which measures the quality of $w$ for the estimation of a linear parameter subsystem defined by a full-rank coefficient matrix $K$. We prove that some other widely used criteria are also second-order cone representable, for instance, the criteria of $A-$, $A_K$-, $G-$ and $I-$optimality. We present several numerical examples demonstrating the efficiency and general applicability of the proposed method. We show that in many cases the mixed integer second-order cone programming approach allows us to find a provably optimal exact design, while the standard heuristics systematically miss the optimum. Y1 - 2015 U6 - https://doi.org/10.1214/15-AOS1339 VL - 43 IS - 5 SP - 2198 EP - 2224 ER - TY - CHAP A1 - Duarte, Belmiro P.M. A1 - Sagnol, Guillaume A1 - Oliveira, Nuno M.C. T1 - A Robust Minimax Semidefinite Programming Formulation for Optimal Design of Experiments for Model Parametrization T2 - 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering N2 - Model-based optimal design of experiments (M-bODE) is a crucial step in model parametrization since it encloses a framework that maximizes the amount of information extracted from a battery of lab experiments. We address the design of M-bODE for dynamic models considering a continuous representation of the design. We use Semidefinite Programming (SDP) to derive robust minmax formulations for nonlinear models, and extend the formulations to other criteria. The approaches are demonstrated for a CSTR where a two-step reaction occurs. Y1 - 2015 U6 - https://doi.org/10.1016/B978-0-444-63578-5.50146-8 VL - 37 SP - 905 EP - 910 ER - TY - JOUR A1 - Duarte, Belmiro P.M. A1 - Sagnol, Guillaume A1 - Wong, Weng Kee T1 - An algorithm based on Semidefinite Programming for finding minimax optimal designs JF - Computational Statistics & Data Analysis Y1 - 2018 U6 - https://doi.org/10.1016/j.csda.2017.09.008 VL - 119 SP - 99 EP - 117 ER -