@misc{SagnolBlancoSauvage2017, author = {Sagnol, Guillaume and Blanco, Marco and Sauvage, Thibaut}, title = {The Cone of Flow Matrices: Approximation Hierarchies and Applications}, issn = {1438-0064}, doi = {10.1002/net.21820}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-64399}, year = {2017}, abstract = {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.}, language = {en} } @article{SagnolBlancoSauvage2018, author = {Sagnol, Guillaume and Blanco, Marco and Sauvage, Thibaut}, title = {The Cone of Flow Matrices: Approximation Hierarchies and Applications}, volume = {72}, journal = {Networks}, number = {1}, doi = {10.1002/net.21820}, pages = {128 -- 150}, year = {2018}, abstract = {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.}, language = {en} } @misc{SagnolBlancoSauvage2018, author = {Sagnol, Guillaume and Blanco, Marco and Sauvage, Thibaut}, title = {Approximation Hierarchies for the cone of flow matrices}, issn = {1438-0064}, doi = {10.1016/j.endm.2018.02.002}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-68424}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{SagnolBlancoSauvage2018, author = {Sagnol, Guillaume and Blanco, Marco and Sauvage, Thibaut}, title = {Approximation Hierarchies for the cone of flow matrices}, volume = {64}, booktitle = {INOC 2017 - 8th International Network Optimization Conference}, doi = {10.1016/j.endm.2018.02.002}, pages = {275 -- 284}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{Sagnol2012, author = {Sagnol, Guillaume}, title = {Network-related problems in optimal experimental design and second order cone programming}, volume = {51}, booktitle = {Proceedings of PROBASTAT'2011, Tatra Mountains Mathematical Publications}, doi = {10.2478/v10127-012-0016-x}, pages = {161 -- 171}, year = {2012}, abstract = {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.}, language = {en} } @article{Sagnol2013, author = {Sagnol, Guillaume}, title = {On the semidefinite representation of real functions applied to symmetric matrices}, volume = {439}, journal = {Linear Algebra and its Applications}, number = {10}, doi = {10.1016/j.laa.2013.08.021}, pages = {2829 -- 2843}, year = {2013}, abstract = {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.}, language = {en} } @article{Sagnol2013, author = {Sagnol, Guillaume}, title = {Approximation of a maximum-submodular-coverage problem involving spectral functions, with application to experimental designs}, volume = {161}, journal = {Discrete Applied Mathematics}, number = {1-2}, doi = {10.1016/j.dam.2012.07.016}, pages = {258 -- 276}, year = {2013}, abstract = {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\$.}, language = {en} } @article{Sagnol2011, author = {Sagnol, Guillaume}, title = {A class of Semidefinite Programs with rank-one solutions}, volume = {435}, journal = {Linear Algebra and its Applications}, number = {6}, doi = {10.1016/j.laa.2011.03.027}, pages = {1446 -- 1463}, year = {2011}, abstract = {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.}, language = {en} } @misc{SagnolBorndoerferGrimaetal.2016, author = {Sagnol, Guillaume and Bornd{\"o}rfer, Ralf and Grima, Micka{\"e}l and Seeling, Matthes and Spies, Claudia}, title = {Robust Allocation of Operating Rooms with Lognormal case Durations}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-58497}, year = {2016}, abstract = {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.}, language = {en} } @misc{SagnolBalzerBorndoerferetal.2016, author = {Sagnol, Guillaume and Balzer, Felix and Bornd{\"o}rfer, Ralf and Spies, Claudia and von Dincklage, Falk}, title = {Makespan and Tardiness in Activity Networks with Lognormal Activity Durations}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-59290}, year = {2016}, abstract = {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.}, language = {en} }