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SIAMJO
2011
7 years 9 months ago
Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-ran...
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Pa...
SIAMJO
2011
8 years 21 days ago
Approximating Semidefinite Packing Programs
In this paper we define semidefinite packing programs and describe an algorithm to approximately solve these problems. Semidefinite packing programs arise in many applications s...
Garud Iyengar, David J. Phillips, Clifford Stein
SIAMJO
2011
8 years 24 days ago
Adaptive Multilevel Inexact SQP Methods for PDE-Constrained Optimization
We present a class of inexact adaptive multilevel trust-region SQP-methods for the efficient solution of optimization problems governed by nonlinear partial differential equations...
J. Carsten Ziems, Stefan Ulbrich
SIAMJO
2011
8 years 1 months ago
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
Min Tao, Xiaoming Yuan
SIAMJO
2011
8 years 1 months ago
Prox-Penalization and Splitting Methods for Constrained Variational Problems
This paper is concerned with the study of a class of prox-penalization methods for solving variational inequalities of the form Ax + NC (x) 0 where H is a real Hilbert space, A : H...
Hedy Attouch, Marc-Olivier Czarnecki, Juan Peypouq...
SIAMJO
2011
8 years 1 months ago
Minimizing the Condition Number of a Gram Matrix
Abstract. The condition number of a Gram matrix defined by a polynomial basis and a set of points is often used to measure the sensitivity of the least squares polynomial approxim...
Xiaojun Chen, Robert S. Womersley, Jane J. Ye
SIAMJO
2011
8 years 1 months ago
A Unifying Polyhedral Approximation Framework for Convex Optimization
Abstract. We propose a unifying framework for polyhedral approximation in convex optimization. It subsumes classical methods, such as cutting plane and simplicial decomposition, bu...
Dimitri P. Bertsekas, Huizhen Yu
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