Mustafa Ç. Pınar

The robust spanning tree problem with interval data

Motivated by telecommunications applications we investigate the minimum spanning tree problem where edge costs are interval numbers. Since minimum spanning trees depend on the realization of the edge costs, we define the robust spanning tree problem to hedge against the worst case contingency, and present a mixed integer programming formulation of the problem. We also define some useful optimality concepts, and present characterizations for these entities leading to polynomial time recognition algorithms. These entities are then used to preprocess a given graph with interval data prior to the solution of the robust spanning tree problem. Computational results show that these preprocessing procedures are quite effective in reducing the time to compute a robust spanning tree.

On Smoothing Exact Penalty Functions for Convex Constrained Optimization

A quadratic smoothing approximation to nondifferentiable exact penalty functions for convex constrained optimization is proposed and its properties are established. The smoothing approximation is used as the basis of an algorithm for solving problems with (i) embedded network structures, and (ii) nonlinear minimax problems. Extensive numerical results with large-scale problems illustrate the efficiency of this approach.

On the S-procedure and Some Variants

We give a concise review and extension of S-procedure that is an instrumental tool in control theory and robust optimization analysis. We also discuss the approximate S-Lemma as well as its applications in robust optimization.

Sufficient global optimality conditions for bivalent quadratic optimization

We prove a sufficient global optimality condition for the problem of minimizing a quadratic function subject to quadratic equality constraints where the variables are allowed to take values −1 and 1. We extend the condition to quadratic problems with matrix variables and orthonormality constraints, and in particular to the quadratic assignment problem.

An exact algorithm for the capacitated vertex p -center problem

We develop a simple and practical exact algorithm for the problem of locating p facilities and assigning clients to them within capacity restrictions in order to minimize the maximum distance between a client and the facility to which it is assigned (capacitated p-center). The algorithm iteratively sets a maximum distance value within which it tries to assign all clients, and thus solves bin-packing or capacitated concentrator location subproblems using off-the-shelf optimization software. Computational experiments yield promising results.

The Robust Network Loading Problem Under Hose Demand Uncertainty: Formulation, Polyhedral Analysis, and Computations

We consider the network loading problem (NLP) under a polyhedral uncertainty description of traffic demands. After giving a compact multicommodity flow formulation of the problem, we state a decomposition property obtained from projecting out the flow variables. This property considerably simplifies the resulting polyhedral analysis and computations by doing away with metric inequalities. Then we focus on a specific choice of the uncertainty description, called the “hose model,” which specifies aggregate traffic upper bounds for selected endpoints of the network. We study the polyhedral aspects of the NLP under hose demand uncertainty and use the results as the basis of an efficient branch-and-cut algorithm. The results of extensive computational experiments on well-known network design instances are reported.

A smooth penalty function algorithm for network-structured problems

Abstract We discuss the design and implementation of an algorithm for the solution of large scale optimization problems with embedded network structures. The algorithm uses a linear-quadratic penalty (LQP) function to eliminate the side constraints and produces a differentiable, but non-separable, problem. A simplicial decomposition is subsequently used to decompose the problem into a sequence of linear network problems. Numerical issues and implementation details are also discussed. The algorithm is particularly suitable for vector architectures and was implemented on a CRAY Y-MP. We report very promising numerical results with a set of large linear multicommodity network flow problems drawn from a military planning application.

Robust scenario optimization based on downside-risk measure for multi-period portfolio selection

We develop and test multistage portfolio selection models maximizing expected end-of-horizon wealth while minimizing one-sided deviation from a target wealth level. The trade-off between two objectives is controlled by means of a non-negative parameter as in Markowitz Mean-Variance portfolio theory. We use a piecewise-linear penalty function, leading to linear programming models and ensuring optimality of subsequent stage decisions. We adopt a simulated market model to randomly generate scenarios approximating the market stochasticity. We report results of rolling horizon simulation with two variants of the proposed models depending on the inclusion of transaction costs, and under different simulated stock market conditions. We compare our results with the usual stochastic programming models maximizing expected end-of-horizon portfolio value. The results indicate that the robust investment policies are indeed quite stable in the face of market risk while ensuring expected wealth levels quite similar to the competing expected value maximizing stochastic programming model at the expense of solving larger linear programs.

Parallel Decomposition of Multicommodity Network Flows Using a Linear-Quadratic Penalty Algorithm

The central theme of this paper is the design and evaluation of a parallel decomposition algorithm for multicommodity network flow problems based on the notion of piecewise linear-quadratic penalty (LQP) functions. The algorithm induces separability of both the constraint set and the objective function by commodity during the subproblem phase. Hence it is suitable for implementations on coarse grain parallel architectures. A master problem, of significantly smaller dimension than the original problem, uses dense linear algebra computations. Hence it exploits a vector architecture, but is also suited for parallel implementation and can be executed using Basic Linear Algebra (BLAS) subroutines. Computational results on a CRAY Y-MPE264 supercomputer with a set of large multicommodity network flow problems drawn from a military application are presented and analyzed. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.

Decomposing Linear Programs for Parallel Solution

Coarse grain parallelism inherent in the solution of Linear Programming (LP) problems with block angular constraint matrices has been exploited in recent research works. However, these approaches suffer from unscalability and load imbalance since they exploit only the existing block angular structure of the LP constraint matrix. In this paper, we consider decomposing LP constraint matrices to obtain block angular structures with specified number of blocks for scalable parallelization. We propose hypergraph models to represent LP constraint matrices for decomposition. In these models, the decomposition problem reduces to the well-known hypergraph partitioning problem. A Kernighan-Lin based multiway hypergraph partitioning heuristic is implemented for experimenting with the performance of the proposed hypergraph models on the decomposition of the LP problems selected from NETLIB suite. Initial results are promising and justify further research on other hypergraph partitioning heuristics for decomposing large LP problems.