Subsampling Algorithms for Semidefinite Programming
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Publication:5168846
DOI10.1214/10-SSY018zbMath1291.90169arXiv0803.1990MaRDI QIDQ5168846
Publication date: 21 July 2014
Full work available at URL: https://arxiv.org/abs/0803.1990
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- Primal-dual subgradient methods for convex problems
- The convergence behavior of Ritz values in the presence of close eigenvalues
- On the distribution of the largest eigenvalue in principal components analysis
- Tracy-Widom limit for the largest eigenvalue of a large class of complex sample covariance matrices
- Exact matrix completion via convex optimization
- Matrix Algorithms
- The Fastest Mixing Markov Process on a Graph and a Connection to a Maximum Variance Unfolding Problem
- Fast computation of low-rank matrix approximations
- Sampling from large matrices
- The design and implementation of the MRRR algorithm
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- First-Order Methods for Sparse Covariance Selection
- Robust Stochastic Approximation Approach to Stochastic Programming
- Spectral Algorithms
- LAPACK Users' Guide
- On Estimating the Largest Eigenvalue with the Lanczos Algorithm
- Acceleration of Stochastic Approximation by Averaging
- Estimating the Largest Eigenvalue by the Power and Lanczos Algorithms with a Random Start
- Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming
- ARPACK Users' Guide
- Orthogonal Eigenvectors and Relative Gaps
- Patterns in eigenvalues: the 70th Josiah Willard Gibbs lecture
- A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
- Fast monte-carlo algorithms for finding low-rank approximations
- Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication
- Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix
- Approximating Subdifferentials by Random Sampling of Gradients
- A Direct Formulation for Sparse PCA Using Semidefinite Programming
