Journal ArticleDOI
The trust region subproblem with non-intersecting linear constraints
Samuel Burer,Boshi Yang +1 more
TLDR
It is shown that the convex relaxation has no gap for arbitrary m as long as the linear constraints are non-intersecting, which is equivalent to saying that the optimal value of eTRS is solvable in polynomial time.Abstract:
This paper studies an extended trust region subproblem (eTRS) in which the trust region intersects the unit ball with $$m$$ m linear inequality constraints. When $$m=0,\,m = 1$$ m = 0 , m = 1 , or $$m = 2$$ m = 2 and the linear constraints are parallel, it is known that the eTRS optimal value equals the optimal value of a particular convex relaxation, which is solvable in polynomial time. However, it is also known that, when $$m \ge 2$$ m ? 2 and at least two of the linear constraints intersect within the ball, i.e., some feasible point of the eTRS satisfies both linear constraints at equality, then the same convex relaxation may admit a gap with eTRS. This paper shows that the convex relaxation has no gap for arbitrary $$m$$ m as long as the linear constraints are non-intersecting.read more
Citations
More filters
Journal ArticleDOI
A gentle, geometric introduction to copositive optimization
TL;DR: This paper illustrates the fundamental connection that allows the reformulation of nonconvex quadratic problems as convex ones in a unified way by focusing on examples having just a few variables or a few constraints for which the quadRatic problem can be formulated as a copositive-style problem, which itself can be recast in terms of linear, second-order-cone, and semidefinite optimization.
Proceedings ArticleDOI
Polynomial solvability of variants of the trust-region subproblem
TL;DR: It is proved that for each fixed pair |S| and |K| the problem can be solved in polynomial time provided that either (1) |S | > 0 and the number of faces of P that intersect [EQUATION] is polynomially bounded, or (2) |K | = 0 and m is bounded.
Book
Low-Rank Semidefinite Programming: Theory and Applications
TL;DR: The theory of low-rank semidefinite programming is reviewed, presenting theorems that guarantee the existence of a low- Rank solution, heuristics for computing low- rank solutions, and algorithms for finding low-Rank approximate solutions.
Journal ArticleDOI
A Note on Polynomial Solvability of the CDT Problem
TL;DR: This paper focuses on the complexity of extensions of the classical trust-region subproblem, which addresses the minimization of a quadratic function over a unit ball in ${\mathbb{R}}^n,$ ...
Journal ArticleDOI
A Second-Order Cone Based Approach for Solving the Trust Region Subproblem and Its Variants
TL;DR: This study highlights an explicit connection between the classical nonconvex TRS and smooth convex quadratic minimization, which allows for the application of cheap iterative methods such as Nesterov's accelerated gradient descent, to the TRS.
References
More filters
Book
Trust Region Methods
TL;DR: This chapter discusses Trust-Region Mewthods for General Constained Optimization and Systems of Nonlinear Equations and Nonlinear Fitting, and some of the methods used in this chapter dealt with these systems.
Journal ArticleDOI
Computing a Trust Region Step
Jorge J. Moré,Danny C. Sorensen +1 more
TL;DR: An algorithm for the problem of minimizing a quadratic function subject to an ellipsoidal constraint is proposed and it is shown that this algorithm is guaranteed to produce a nearly optimal solution in a finite number of iterations.
Book
A Reformulation-Linearization Technique for Solving Discrete and Continuous Nonconvex Problems
Hanif D. Sherali,Warren P. Adams +1 more
TL;DR: This paper presents RLT-Based Global Optimization Algorithms for Nonconvex Polynomial Programming Problems and Reformulation-Convexification Technique for Polynomials Programs: Design and Implementation, and some special applications to Discrete and Continuous Non Convex Programs.
Journal ArticleDOI
On the Rank of Extreme Matrices in Semidefinite Programs and the Multiplicity of Optimal Eigenvalues
TL;DR: It is proved that clustering must occur at extreme points of the set of optimal solutions, if the number of variables is sufficiently large and a lower bound on the multiplicity of the critical eigenvalue is given.
Journal ArticleDOI
On cones of nonnegative quadratic functions
Jos F. Sturm,Shuzhong Zhang +1 more
TL;DR: In this paper, the authors derive linear matrix inequality characterizations and dual decomposition algorithms for certain matrix cones which are generated by a given set using generalized co-positivity, which are in fact cones of nonconvex quadratic functions that are nonnegative on a certain domain.