In this paper, we study the set \(\mathcal{S}^\kappa = \{ (x,y)\in\mathcal{G}\times\mathbb{R}^n : y_j = x_j^\kappa , j=1,\dots,n\}\), where \(\kappa > 1\) and the ground set \(\mathcal{G}\) is a nonempty polytope contained in \( [0,1]^n\). This nonconvex set is closely related to separable standard quadratic programming and appears as a substructure in potential-based network flow problems … Read more
Algorithms for continuous optimization problems have a rich history of design and innovation over the past several decades, in which mathematical analysis of their convergence and complexity properties plays a central role. Besides their theoretical properties, optimization algorithms are interesting also for their practical usefulness as computational tools for solving real-world problems. There are often … Read more
We revisit the convergence analysis of two approximation hierarchies for polynomial optimization on the unit sphere. The first one is based on the moment-sos approach and gives semidefinite bounds for which Fang and Fawzi (2021) showed an analysis in \(O(1/r^2)\) for the r-th level bound, using the polynomial kernel method. The second hierarchy was recently … Read more
In the earlier paper “On solving large-scale multistage stochastic optimization problems with a new specialized interior-point approach, European Journal of Operational Research, 310 (2023), 268–285” the authors presented a novel approach based on a specialized interior-point method (IPM) for solving (risk neutral) large-scale multistage stochastic optimization problems. The method computed the Newton direction by combining … Read more
We show how to extract alternative solutions for optimization problems solved by Benders Decom- position. In practice, alternative solutions provide useful insights for complex applications; some solvers do support generation of alternative solutions but none appear to support such generation when using Benders Decomposition. We propose a new post-processing method that extracts multiple optimal and … Read more
We present a Julia-based interface to the precompiled HALLaR and cuHALLaR binaries for large-scale semidefinite programs (SDPs). Both solvers are established as fast and numerically stable, and accept problem data in formats compatible with SDPA and a new enhanced data format taking advantage of Hybrid Sparse Low-Rank (HSLR) structure. The interface allows users to load … Read more
We study a two-player zero-sum inspection game with incomplete information, where an inspector deploys resources to maximize the expected damage value of detected illegal items hidden by an adversary across capacitated locations. Inspection and illegal resources differ in their detection capabilities and damage values. Both players face uncertainty regarding each other’s available resources, modeled via … Read more
Recognizing the strength of parametric optimization to model uncertainty, we extend the classical linear programming duality theory to a parametric setting. For linear programs with parameters in general locations, we prove parametric weak and strong duality theorems and parametric complementary slackness theorems. ArticleDownload
Sparse Principal Component Analysis (SPCA) is a fundamental technique for dimensionality reduction, and is NP-hard. In this paper, we introduce a randomized approximation algorithm for SPCA, which is based on the basic SDP relaxation. Our algorithm has an approximation ratio of at most the sparsity constant with high probability, if called enough times. Under a … Read more
A moment body is a linear projection of the spectraplex, the convex set of trace-one positive semidefinite matrices. Determining whether a given point lies within a given moment body is a problem with numerous applications in quantum state estimation or polynomial optimization. This moment body membership oracle can be addressed with semidefinite programming, for which … Read more