On Hazan’s algorithm for symmetric programming problems
We describe the generalization of Hazan’s algorithm for symmetric programming problems CitationNotre Dame Report, December, 2012ArticleDownload View PDF
Linear, Cone and Semidefinite Programming
AN EFFICIENT ALGORITHM FOR SECOND-ORDER CONE LINEAR COMPLEMENTARITY PROBLEMS
Recently, the globally uniquely solvable (GUS) property of the linear transformation $M\in R^{n\times n}$ in the second-order cone linear complementarity problem (SOCLCP) receives much attention and has been studied substantially. Yang and Yuan [30] contributed a new characterization of the GUS property of the linear transformation, which is formulated by basic linear-algebra-related properties. In this … Read more
Calmness modulus of linear semi-infinite programs
Our main goal is to compute or estimate the calmness modulus of the argmin mapping of linear semi-infinite optimization problems under canonical perturbations, i.e., perturbations of the objective function together with continuous perturbations of the right-hand side of the constraint system (with respect to an index ranging in a compact Hausdorff space). Specifically, we provide … Read more
A SMOOTHING MAJORIZATION METHOD FOR hBc$ MATRIX MINIMIZATION
We discuss the $l_2$-$l_p$ (with $p\in (0,1)$ matrix minimization for recovering low rank matrix. A smoothing approach is developed for solving this non-smooth, non-Lipschitz and non-convex optimization problem, in which the smoothing parameter is used as a variable and a majorization method is adopted to solve the smoothing problem. The convergence theorem shows that any … Read more
S-semigoodness for Low-Rank Semidefinite Matrix Recovery
We extend and characterize the concept of $s$-semigoodness for a sensing matrix in sparse nonnegative recovery (proposed by Juditsky , Karzan and Nemirovski [Math Program, 2011]) to the linear transformations in low-rank semidefinite matrix recovery. We show that s-semigoodness is not only a necessary and sufficient condition for exact $s$-rank semidefinite matrix recovery by a … Read more
Robust Least Square Semidefinite Programming with Applications to Correlation Stress Testing
In this paper, we consider a least square semidefinite programming problem under ellipsoidal data uncertainty. We show that the robustification of this uncertain problem can be reformulated as a semidefinite linear programming problem with an additional second-order cone constraint. We then provide an explicit quantitative sensitivity analysis on how the solution under the robustification depends … Read more
S_1/2 Regularization Methods and Fixed Point Algorithms for Affine Rank Minimization Problems
The affine rank minimization problem is to minimize the rank of a matrix under linear constraints. It has many applications in various areas such as statistics, control, system identification and machine learning. Unlike the literatures which use the nuclear norm or the general Schatten $q~(0<q<1)$ quasi-norm to approximate the rank of a matrix, in this … Read more
Spectral bounds for the independence ratio and the chromatic number of an operator
We define the independence ratio and the chromatic number for bounded, self-adjoint operators on an L^2-space by extending the definitions for the adjacency matrix of finite graphs. In analogy to the Hoffman bounds for finite graphs, we give bounds for these parameters in terms of the numerical range of the operator. This provides a theoretical … Read more
VSDP: A Matlab toolbox for verifi ed semide finite-quadratic-linear programming
VSDP is a software package that is designed for the computation of verified results in conic programming. The current version of VSDP supports the constraint cone consisting of the product of semidefinite cones, second-order cones and the nonnegative orthant. It provides functions for computing rigorous error bounds of the true optimal value, verified enclosures of … Read more
On metric regularity for weakly almost piecewise smooth functions and some applications in nonlinear semidefinite programming
The one-to-one relation between the points fulfilling the KKT conditions of an optimization problem and the zeros of the corresponding Kojima function is well-known. In the present paper we study the interplay between metric regularity and strong regularity of this a priori nonsmooth function in the context of semidefinite programming. Having in mind the topological … Read more