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Convex hulls are useful as tight bounding proxies for a variety of tasks including collision detection, ray intersection, and distance computation. Unfortunately, the complexity of polyhedral convex hulls grows linearly with their input. We…

Graphics · Computer Science 2026-04-17 Alec Jacobson

We study the complexity of computing the mixed-integer hull $\operatorname{conv}(P\cap\mathbb{Z}^n\times\mathbb{R}^d)$ of a polyhedron $P$. Given an inequality description, with one integer variable, the mixed-integer hull can have…

Optimization and Control · Mathematics 2015-03-11 Robert Hildebrand , Timm Oertel , Robert Weismantel

We provide a general framework to construct finite dimensional approximations of the space of convex functions, which also applies to the space of c-convex functions and to the space of support functions of convex bodies. We give estimates…

Numerical Analysis · Mathematics 2014-03-11 Quentin Mérigot , Edouard Oudet

A classical approach for obtaining valid inequalities for a set involves weighted aggregations of the inequalities that describe such set. When the set is described by linear inequalities, thanks to the Farkas lemma, we know that every…

Optimization and Control · Mathematics 2021-06-25 Santanu S. Dey , Gonzalo Munoz , Felipe Serrano

In this paper, we focus on the nonconvex-strongly-concave minimax optimization problem (MCC), where the inner maximization subproblem contains constraints that couple the primal variable of the outer minimization problem. We prove that by…

Optimization and Control · Mathematics 2024-09-02 Xiaoyin Hu , Kim-Chuan Toh , Shiwei Wang , Nachuan Xiao

The paper considers the minimization of a separable convex function subject to linear ascending constraints. The problem arises as the core optimization in several resource allocation scenarios, and is a special case of an optimization of a…

Optimization and Control · Mathematics 2016-08-30 Akhil P T , Rajesh Sundaresan

Given a directed graph D = (N, A) and a sequence of positive integers 1 <= c_1 < c_2 < ... < c_m <= |N|, we consider those path and cycle polytopes that are defined as the convex hulls of simple paths and cycles of D of cardinality c_p for…

Combinatorics · Mathematics 2007-10-17 Volker Kaibel , Ruediger Stephan

In this paper we develop a theory of convexity for a free Abelian group M (the lattice of integer points), which we call theory of discrete convexity. We characterize those subsets X of the group M that could be call "convex". One property…

Combinatorics · Mathematics 2007-05-23 V. I. Danilov , G. A. Koshevoy

We develop techniques to convexify a set that is invariant under permutation and/or change of sign of variables and discuss applications of these results. First, we convexify the intersection of the unit ball of a permutation and…

Optimization and Control · Mathematics 2021-08-10 Jinhak Kim , Mohit Tawarmalani , Jean-Philippe P. Richard

In this paper, we study the solution uniqueness of an individual feasible vector of a class of convex optimization problems involving convex piecewise affine functions and subject to general polyhedral constraints. This class of problems…

Optimization and Control · Mathematics 2017-11-17 Seyedahmad Mousavi , Jinglai Shen

Error bounds, which refer to inequalities that bound the distance of vectors in a test set to a given set by a residual function, have proven to be extremely useful in analyzing the convergence rates of a host of iterative methods for…

Optimization and Control · Mathematics 2015-12-14 Zirui Zhou , Anthony Man-Cho So

This paper derives a discrete dual problem for a prototypical hybrid high-order method for convex minimization problems. The discrete primal and dual problem satisfy a weak convex duality that leads to a priori error estimates with…

Numerical Analysis · Mathematics 2026-04-10 Ngoc Tien Tran

We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. Approximate solutions can be found by solving a convexified version of the problem, in which…

Optimization and Control · Mathematics 2016-01-12 Madeleine Udell , Stephen Boyd

Regularized empirical risk minimization with constrained labels (in contrast to fixed labels) is a remarkably general abstraction of learning. For common loss and regularization functions, this optimization problem assumes the form of a…

Machine Learning · Computer Science 2016-02-23 Iaroslav Shcherbatyi , Bjoern Andres

Motivated by previous efforts toward mathematically analyzing the treatment of monomials in spatial branch-and-bound, we study the convex hull of the graph of a simple monomial on a nonnegative box domain in arbitrary dimension, where at…

Optimization and Control · Mathematics 2026-05-05 Jon Lee , Daphne Skipper , Emily Speakman

We examine the duality theory for a class of non-convex functions obtained by composing a convex function with a continuous one. Using Fenchel duality, we derive a dual problem that satisfies weak duality under general assumptions. To…

Optimization and Control · Mathematics 2025-10-08 Vittorio Latorre

We construct a convergent family of outer approximations for the problem of optimizing polynomial functions over convex bodies subject to polynomial constraints. This is achieved by generalizing the polarization hierarchy, which has…

Optimization and Control · Mathematics 2024-06-17 Martin Plávala , Laurens T. Ligthart , David Gross

Let C be a real nonsingular affine curve of genus one, embedded in affine n-space, whose set of real points is compact. For any polynomial f which is nonnegative on C(R), we prove that there exist polynomials f_i with f \equiv \sum_i f_i^2…

Algebraic Geometry · Mathematics 2010-03-25 Claus Scheiderer

This paper studies binary quadratic programs in which the objective is defined by a Euclidean distance matrix, subject to a general polyhedral constraint set. This class of nonconcave maximisation problems includes the capacitated,…

Optimization and Control · Mathematics 2023-09-19 Hoa T. Bui , Sandy Spiers , Ryan Loxton

In this paper, we present an algorithm for minimizing the difference between two submodular functions using a variational framework which is based on (an extension of) the concave-convex procedure [17]. Because several commonly used metrics…

Machine Learning · Computer Science 2012-07-09 Mukund Narasimhan , Jeff A. Bilmes
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