Stable Set Polytopes with Rank |V(G)|/3 for the Lovász-Schrijver SDP Operator

We study the lift-and-project rank of the stable set polytope of graphs with respect to the Lovász–Schrijver SDP operator \( \text{LS}_+ \) applied to the fractional stable set polytope. In particular, we show that for every positive integer \( \ell \), the smallest possible graph with \( \text{LS}_+ \)-rank \( \ell \) contains \( 3\ell … Read more

Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization

Building on the blueprint from Goemans and Williamson (1995) for the Max-Cut problem, we construct a polynomial-time approximation algorithm for orthogonally constrained quadratic optimization problems. First, we derive a semidefinite relaxation and propose a randomized rounding algorithm to generate feasible solutions from the relaxation. Second, we derive constant-factor approximation guarantees for our algorithm. When optimizing … Read more

Insights into the computational complexity of the single-source capacitated facility location problem with customer preferences

Single-source capacitated facility location problems are well studied in the operations research literature, yet classic problems often lack practicability by disregarding the customers’ perspective: An authority that assigns customers to open facilities deprives customers from choosing facilities according to their individual preferences. In reality, this can render solutions infeasible, as customers may deviate to their … Read more

Polynomial-Time Algorithms for Setting Tight Big-M Coefficients in Transmission Expansion Planning with Disconnected Buses

The increasing penetration of renewable energy into power systems necessitates the development of effective methodologies to integrate initially disconnected generation sources into the grid. This paper introduces the Longest Shortest-Path-Connection (LSPC) algorithm, a graph-based method to enhance the mixed-integer linear programming disjunctive formulation of Transmission Expansion Planning (TEP) using valid inequalities (VIs). Traditional approaches for … Read more

A Generalized Voting Game for Categorical Network Choices

This paper develops a unified game-theoretical framework for data classification and network discovery based on pairwise influences in multivariate categorical choices. Data points, interpreted as individuals, are connected through a signed weighted graph and take values according to a voting rule that aggregates the influence of attractive (friend-like) and repulsive (enemy-like) neighbors. The framework consists … Read more

On parametric formulations for the Asymmetric Traveling Salesman Problem

The traveling salesman problem is a widely studied classical combinatorial problem for which there are several integer linear formulations. In this work, we consider the Miller-Tucker-Zemlin (MTZ), Desrochers-Laporte (DL) and Single Commodity Flow (SCF) formulations. We argue that the choice of some parameters of these formulations is arbitrary and, therefore, there are families of formulations … Read more

A folding preprocess for the max k-cut problem

Given graph G = (V,E) with vertex set V and edge set E, the max k-cut problem seeks to partition the vertex set V into at most k subsets that maximize the weight (number) of edges with endpoints in different parts. This paper proposes a graph folding procedure (i.e., a procedure that reduces the number … Read more

An analytical lower bound for a class of minimizing quadratic integer optimization problems

Lower bounds on minimization problems are essential for convergence of both branching-based and iterative solution methods for optimization problems. They are also required for evaluating the quality of feasible solutions by providing conservative optimality gaps. We provide an analytical lower bound for a class of quadratic optimization problems with binary decision variables. In contrast to … Read more

Accelerating Benders decomposition for solving a sequence of sample average approximation replications

Sample average approximation (SAA) is a technique for obtaining approximate solutions to stochastic programs that uses the average from a random sample to approximate the expected value that is being optimized. Since the outcome from solving an SAA is random, statistical estimates on the optimal value of the true problem can be obtained by solving … Read more

Single-Scenario Facet Preservation for Stochastic Mixed-Integer Programs

We consider improving the polyhedral representation of the extensive form of a stochastic mixed-integer program (SMIP). Given a facet for a single-scenario version of an SMIP, our main result provides necessary and sufficient conditions under which this inequality remains facet-defining for the extensive form. We then present several implications, which show that common recourse structures … Read more