43 citations · 83 across the 10 of their papers we have counts for
12 papers · 1 filter
Random extrapolation for primal-dual coordinate descent
Ahmet Alacaoglu, Olivier Fercoq, Volkan Cevher
We introduce a randomly extrapolated primal-dual coordinate descent method that adapts to sparsity of the data matrix and the favorable structures of the objective function. Our me…
Scalable Semidefinite Programming
Alp Yurtsever, Joel A. Tropp, Olivier Fercoq +2
Semidefinite programming (SDP) is a powerful framework from convex optimization that has striking potential for data science applications. This paper develops a provably correct ra…
Linear convergence of dual coordinate descent on non-polyhedral convex problems
Ion Necoara, Olivier Fercoq
This paper deals with constrained convex problems, where the objective function is smooth strongly convex and the feasible set is given as the intersection of a large number of clo…
Almost surely constrained convex optimization
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara +1
We propose a stochastic gradient framework for solving stochastic composite convex optimization problems with (possibly) infinite number of linear inclusion constraints that need t…
A Conditional Gradient-Based Augmented Lagrangian Framework
Alp Yurtsever, Olivier Fercoq, Volkan Cevher
This paper considers a generic convex minimization template with affine constraints over a compact domain, which covers key semidefinite programming applications. The existing cond…
Stochastic Frank-Wolfe for Composite Convex Minimization
Francesco Locatello, Alp Yurtsever, Olivier Fercoq +1
A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to so…