We analyze two algorithms for approximating the general optimal transport (OT) distance
between two discrete distributions of size $ n $, up to accuracy $\varepsilon $. For the first …

Semidefinite programming (SDP) is a powerful framework from convex optimization that has
striking potential for data science applications. This paper develops a provably correct …

We study dual-based algorithms for distributed convex optimization problems over networks,
where the objective is to minimize a sum Σ i= 1 mfi (z) of functions over in a network. We …

We study the problem of computing an optimal policy of an infinite-horizon discounted
constrained Markov decision process (constrained MDP). Despite the popularity of …

We study the minimization of a convex function f (X) over the set of n\times n positive semi-
definite matrices, but when the problem is recast as\min_U g (U):= f (UU^⊤), with …

We study the complexity of approximating the Wasserstein barycenter of $ m $ discrete
measures, or histograms of size $ n $, by contrasting two alternative approaches that use …

We present a novel method for convex unconstrained optimization that, without any
modifications ensures:(1) accelerated convergence rate for smooth objectives,(2) standard …

We propose a practical inexact augmented Lagrangian method (iALM) for nonconvex
problems with nonlinear constraints. We characterize the total computational complexity of …

We consider variational inequalities coming from monotone operators, a setting that
includes convex minimization and convex-concave saddle-point problems. We assume an …

We consider online convex optimization with stochastic constraints where the objective
functions are arbitrarily time-varying and the constraint functions are independent and …