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Two-stage submodular maximization under curvature
The concept of submodularity has wide applications in data science, artificial intelligence, and machine learning, providing a boost to the...
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Inexact penalty decomposition methods for optimization problems with geometric constraints
This paper provides a theoretical and numerical investigation of a penalty decomposition scheme for the solution of optimization problems with...
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Second order variational analysis of disjunctive constraint sets and its applications to optimization problems
In this paper, we examine the properly twice epi-differentiability and compute the second order epi-subderivative of the indicator function to a...
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Cone-Constrained Eigenvalue Problems: Structure of Cone Spectra
There is a rich literature devoted to the eigenvalue analysis of variational inequalities. Of special interest is the case in which the constraint...
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One more proof of the first linear programming bound for binary codes and two conjectures
We give one more proof of the first linear programming bound for binary codes, following the line of work initiated by Friedman and Tillich [9]. The...
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A novel regularization-based optimization approach to sparse mean-reverting portfolios selection
The construction of profitable mean-reverting portfolios, with fewer assets, but enough volatility is a real challenge for financial investors....
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Fixed set search applied to the multi-objective minimum weighted vertex cover problem
The Fixed Set Search (FSS) is a novel metaheuristic that adds a learning mechanism to the Greedy Randomized Adaptive Search Procedure (GRASP). In...
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On the geometry of elementary flux modes
Elementary flux modes (EFMs) play a prominent role in the constraint-based analysis of metabolic networks. They correspond to minimal functional...
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On combining variable ordering heuristics for constraint satisfaction problems
Variable ordering heuristics play a central role in solving constraint satisfaction problems. Combining two variable ordering heuristics may generate...
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A framework of distributionally robust possibilistic optimization
In this paper, an optimization problem with uncertain constraint coefficients is considered. Possibility theory is used to model the uncertainty....
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Toward a systematic conflict resolution framework for ontologies
BackgroundThe ontology authoring step in ontology development involves having to make choices about what subject domain knowledge to include. This...
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General Algorithm for Analytical Calculations of Jacobi Matrix Elements in Sparse Nonlinear Programming Problems
AbstractThe author deals with sparse non-linear programming problems of high dimension. When solving such problems numerically by gradient means, we...
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Dantzig–Wolfe reformulations for binary quadratic problems
The purpose of this paper is to provide strong reformulations for binary quadratic problems. We propose a first methodological analysis on a family...
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Singular Value Analysis of Linear Maps Under Conic Constraints
We have recently introduced and studied the concept of singular value of a rectangular matrix relative to a pair of closed convex cones. Such cones...
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Minimum cost b-matching problems with neighborhoods
In this paper, we deal with minimum cost b -matching problems on graphs where the nodes are assumed to belong to non-necessarily convex regions called...
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Exact Approaches for the Connected Vertex Cover Problem
Given a graph G, the Connected Vertex Cover problem (CVC) asks to find a minimum cardinality vertex cover of G that induces a connected subgraph....
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Weak notions of nondegeneracy in nonlinear semidefinite programming
The constraint nondegeneracy condition is one of the most relevant and useful constraint qualifications in nonlinear semidefinite programming. It can...
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Fast algorithms for maximizing monotone nonsubmodular functions
In recent years, with the more and more researchers studying the problem of maximizing monotone (nonsubmodular) objective functions, the...
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Exterior-Point Optimization for Sparse and Low-Rank Optimization
Many problems of substantial current interest in machine learning, statistics, and data science can be formulated as sparse and low-rank optimization...
