@misc{BertholdGleixnerHeinzetal.2011, author = {Berthold, Timo and Gleixner, Ambros and Heinz, Stefan and Vigerske, Stefan}, title = {On the computational impact of MIQCP solver components}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-11998}, number = {11-01}, year = {2011}, abstract = {We provide a computational study of the performance of a state-of-the-art solver for nonconvex mixed-integer quadratically constrained programs (MIQCPs). Since successful general-purpose solvers for large problem classes necessarily comprise a variety of algorithmic techniques, we focus especially on the impact of the individual solver components. The solver SCIP used for the experiments implements a branch-and-cut algorithm based on linear outer approximation to solve MIQCPs to global optimality. Our analysis is based on a set of 86 publicly available test instances.}, language = {en} } @misc{GeerdesKarl2003, author = {Geerdes, Hans-Florian and Karl, Holger}, title = {The Potential of Relaying in Cellular Networks}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7641}, number = {03-42}, year = {2003}, abstract = {Relaying is a protocol extension for cellular wireless computer networks; in order to utilize radio resources more efficiently, several hops are allowed within one cell. This paper investigates the principle potential of relaying by casting transmission scheduling as a mathematical optimization problem, namely, a linear program. We analyze the throughput gains showing that, irrespective of the concrete scheduling algorithm, performance gains of up to 30\\% on average for concrete example networks are achievable.}, language = {en} } @misc{Geerdes2003, author = {Geerdes, Hans-Florian}, title = {Assessing Capacity Improvements by Relaying in Cellular Networks}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-7651}, number = {03-43}, year = {2003}, abstract = {Relaying -- allowing multiple wireless hops -- is a protocol extension for cellular networks conceived to improve data throughput. Its benefits have only been quantified for small example networks. For assessing its general potential, we define a complex resource allocation\slash{}scheduling problem. Several mathematical models are presented for this problem; while a time-expanded MIP approach turns out intractable, a sophisticated column generation scheme leads to good computational results. We thereby show that for selected cases relaying can increase data throughput by 30\\% on the average.}, language = {en} } @misc{Potra2000, author = {Potra, Florian}, title = {A path-following method for linear complementarity problems based on the affine invariant Kantorovich Theorem}, doi = {10.1007/s10107-003-0501-8}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-5981}, number = {00-30}, year = {2000}, abstract = {A path following algorithm for linear complementarity problems is presented. Given a point \$z\$ that approximates a point \$z(\tau)\$ on the central path with complementarity gap \$\tau\$, one determines a parameter \$\theta\in (0,1)\$ so that this point satisfies the hypothesis of the affine invariant Kantorovich Theorem for the equation defining \$z((1-\theta)\tau)\$. It is shown that \$\theta\$ is bounded below by a multiple of \$n^{-1/2}\$, where \$n\$ is the dimension of the problem. Since the hypothesis of of the Kantorovich Theorem is satisfied the sequence generated by Newton's method, or by the simplified Newton method, will converge to \$z((1-\theta)\tau)\$. We show that the number of steps required to obtain an acceptable approximation of \$z((1-\theta)\tau)\$ is bounded above by a number independent of \$n\$. Therefore the algorithm has \$O(\sqrt{n}L)\$-iteration complexity. The parameters of the algorithm can be determined in such a way that only one Newton step is needed each time the complementarity gap is decreased.}, language = {en} } @misc{Weiser2002, author = {Weiser, Martin}, title = {Linear convergence of an interior point method for linear control constrained optimal control problems}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-6809}, number = {02-13}, year = {2002}, abstract = {The paper provides a detailed analysis of a short step interior point algorithm applied to linear control constrained optimal control problems. Using an affine invariant local norm and an inexact Newton corrector, the well-known convergence results from finite dimensional linear programming can be extended to the infinite dimensional setting of optimal control. The present work complements a recent paper of Weiser and Deuflhard, where convergence rates have not been derived. The choice of free parameters, i.e. the corrector accuracy and the number of corrector steps, is discussed.}, language = {en} } @misc{KosterZymolkaKutschka2007, author = {Koster, Arie M.C.A. and Zymolka, Adrian and Kutschka, Manuel}, title = {Algorithms to Separate {0,1/2}-Chvatal-Gomory Cuts}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-9531}, number = {07-10}, year = {2007}, abstract = {Chvatal-Gomory cuts are among the most well-known classes of cutting planes for general integer linear programs (ILPs). In case the constraint multipliers are either 0 or \$\frac{1}{2}\$, such cuts are known as \$\{0,\frac{1}{2}\}\$-cuts. It has been proven by Caprara and Fischetti (1996) that separation of \$\{0,\frac{1}{2}\}\$-cuts is NP-hard. In this paper, we study ways to separate \$\{0,\frac{1}{2}\}\$-cuts effectively in practice. We propose a range of preprocessing rules to reduce the size of the separation problem. The core of the preprocessing builds a Gaussian elimination-like procedure. To separate the most violated \$\{0,\frac{1}{2}\}\$-cut, we formulate the (reduced) problem as integer linear program. Some simple heuristic separation routines complete the algorithmic framework. Computational experiments on benchmark instances show that the combination of preprocessing with exact and/or heuristic separation is a very vital idea to generate strong generic cutting planes for integer linear programs and to reduce the overall computation times of state-of-the-art ILP-solvers.}, language = {en} } @misc{ShinanoAchterbergBertholdetal.2010, author = {Shinano, Yuji and Achterberg, Tobias and Berthold, Timo and Heinz, Stefan and Koch, Thorsten}, title = {ParaSCIP - a parallel extension of SCIP}, doi = {10.1007/978-3-642-24025-6_12}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-11921}, number = {10-27}, year = {2010}, abstract = {Mixed integer programming (MIP) has become one of the most important techniques in Operations Research and Discrete Optimization. SCIP (Solving Constraint Integer Programs) is currently one of the fastest non-commercial MIP solvers. It is based on the branch-and-bound procedure in which the problem is recursively split into smaller subproblems, thereby creating a so-called branching tree. We present ParaSCIP, an extension of SCIP, which realizes a parallelization on a distributed memory computing environment. ParaSCIP uses SCIP solvers as independently running processes to solve subproblems (nodes of the branching tree) locally. This makes the parallelization development independent of the SCIP development. Thus, ParaSCIP directly profits from any algorithmic progress in future versions of SCIP. Using a first implementation of ParaSCIP, we were able to solve two previously unsolved instances from MIPLIB2003, a standard test set library for MIP solvers. For these computations, we used up to 2048 cores of the HLRN~II supercomputer.}, language = {en} } @misc{HeinzKuBeck2013, author = {Heinz, Stefan and Ku, Wen-Yang and Beck, J. Christopher}, title = {Recent improvements using constraint integer programming for resource allocation and scheduling}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-17676}, year = {2013}, abstract = {Recently, we compared the performance of mixed-integer programming (MIP), constraint programming (CP), and constraint integer programming (CIP) to a state-of-the-art logic-based Benders manual decomposition (LBBD) for a resource allocation/scheduling problem. For a simple linear relaxation, the LBBD and CIP models deliver comparable performance with MIP also performing well. Here we show that algorithmic developments in CIP plus the use of an existing tighter relaxation substantially improve one of the CIP approaches. Furthermore, the use of the same relaxation in LBBD and MIP models significantly improves their performance. While such a result is known for LBBD, to the best of our knowledge, the other results are novel. Our experiments show that both CIP and MIP approaches are competitive with LBBD in terms of the number of problems solved to proven optimality, though MIP is about three times slower on average. Further, unlike the LBBD and CIP approaches, the MIP model is able to obtain provably high-quality solutions for all problem instances.}, language = {en} } @misc{SteffyWolter2011, author = {Steffy, Daniel and Wolter, Kati}, title = {Valid Linear Programming Bounds for Exact Mixed-Integer Programming}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-12332}, number = {11-08}, year = {2011}, abstract = {Fast computation of valid linear programming (LP) bounds serves as an important subroutine for solving mixed-integer programming problems exactly. We introduce a new method for computing valid LP bounds designed for this application. The algorithm corrects approximate LP dual solutions to be exactly feasible, giving a valid bound. Solutions are repaired by performing a projection and a shift to ensure all constraints are satisfied; bound computations are accelerated by reusing structural information through the branch-and-bound tree. We demonstrate this method to be widely applicable and faster than solving a sequence of exact LPs. Several variations of the algorithm are described and computationally evaluated in an exact branch-and-bound algorithm within the mixed-integer programming framework SCIP.}, language = {en} } @misc{HeinzBeck2011, author = {Heinz, Stefan and Beck, J. Christopher}, title = {Solving Resource Allocation/Scheduling Problems with Constraint Integer Programming}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-12691}, number = {11-14}, year = {2011}, abstract = {Constraint Integer Programming (CIP) is a generalization of mixed-integer programming (MIP) in the direction of constraint programming (CP) allowing the inference techniques that have traditionally been the core of \P to be integrated with the problem solving techniques that form the core of complete MIP solvers. In this paper, we investigate the application of CIP to scheduling problems that require resource and start-time assignments to satisfy resource capacities. The best current approach to such problems is logic-based Benders decomposition, a manual decomposition method. We present a CIP model and demonstrate that it achieves performance competitive to the decomposition while out-performing the standard MIP and CP formulations.}, language = {en} }