425 citations · 1.1k across the 32 of their papers we have counts for
5 papers · 1 filter
Unbiased Simulation for Optimizing Stochastic Function Compositions
Jose Blanchet, Donald Goldfarb, Garud Iyengar +2
In this paper, we introduce an unbiased gradient simulation algorithms for solving convex optimization problem with stochastic function compositions. We show that the unbiased grad…
Greedy Approaches to Symmetric Orthogonal Tensor Decomposition
Cun Mu, Daniel Hsu, Donald Goldfarb
Finding the symmetric and orthogonal decomposition (SOD) of a tensor is a recurring problem in signal processing, machine learning and statistics. In this paper, we review, establi…
Using Negative Curvature in Solving Nonlinear Programs
Donald Goldfarb, Cun Mu, John Wright +1
Minimization methods that search along a curvilinear path composed of a non-ascent nega- tive curvature direction in addition to the direction of steepest descent, dating back to t…
Successive Rank-One Approximations for Nearly Orthogonally Decomposable Symmetric Tensors
Cun Mu, Daniel Hsu, Donald Goldfarb
Many idealized problems in signal processing, machine learning and statistics can be reduced to the problem of finding the symmetric canonical decomposition of an underlying symmet…
Linear Convergence of Stochastic Frank Wolfe Variants
Donald Goldfarb, Garud Iyengar, Chaoxu Zhou
In this paper, we show that the Away-step Stochastic Frank-Wolfe Algorithm (ASFW) and Pairwise Stochastic Frank-Wolfe algorithm (PSFW) converge linearly in expectation. We also sho…