100 citations · 462 across the 26 of their papers we have counts for
7 papers · 1 filter
Semi-Stochastic Coordinate Descent
Jakub Konečný, Zheng Qu, Peter Richtárik
We propose a novel stochastic gradient method---semi-stochastic coordinate descent (S2CD)---for the problem of minimizing a strongly convex function represented as the average of a…
Coordinate Descent with Arbitrary Sampling II: Expected Separable Overapproximation
Zheng Qu, Peter Richtárik
The design and complexity analysis of randomized coordinate descent methods, and in particular of variants which update a random subset (sampling) of coordinates in each iteration,…
Coordinate Descent with Arbitrary Sampling I: Algorithms and Complexity
Zheng Qu, Peter Richtárik
We study the problem of minimizing the sum of a smooth convex function and a convex block-separable regularizer and propose a new randomized coordinate descent method, which we cal…
Randomized Dual Coordinate Ascent with Arbitrary Sampling
Zheng Qu, Peter Richtárik, Tong Zhang
We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual…
Fast Distributed Coordinate Descent for Non-Strongly Convex Losses
Olivier Fercoq, Zheng Qu, Peter Richtárik +1
We propose an efficient distributed randomized coordinate descent method for minimizing regularized non-strongly convex loss functions. The method attains the optimal co…
Bundle-based pruning in the max-plus curse of dimensionality free method
Stephane Gaubert, Zheng Qu, Srinivas Sridharan
Recently a new class of techniques termed the max-plus curse of dimensionality-free methods have been developed to solve nonlinear optimal control problems. In these methods the di…
Checking the strict positivity of Kraus maps is NP-hard
Stephane Gaubert, Zheng Qu
Basic properties in Perron-Frobenius theory are strict positivity, primitivityand irreducibility. Whereas for nonnegative matrices, these properties are equivalent to elementary gr…