29 citations · 37 across the 5 of their papers we have counts for
5 papers
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
Frank E. Curtis, Vyacheslav Kungurtsev, Daniel P. Robinson +1
A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is present…
Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints
Frank E. Curtis, Daniel P. Robinson, Baoyu Zhou
A sequential quadratic optimization algorithm for minimizing an objective function defined by an expectation subject to nonlinear inequality and equality constraints is proposed, a…
A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization
Yutong Dai, Guanyi Wang, Frank E. Curtis +1
This paper introduces a new proximal stochastic gradient method with variance reduction and stabilization for minimizing the sum of a convex stochastic function and a group sparsit…
A Nonsmooth Dynamical Systems Perspective on Accelerated Extensions of ADMM
Guilherme França, Daniel P. Robinson, René Vidal
Recently, there has been great interest in connections between continuous-time dynamical systems and optimization methods, notably in the context of accelerated methods for smooth…
Sparse Recovery over Graph Incidence Matrices
Mengnan Zhao, M. Devrim Kaba, René Vidal +2
Classical results in sparse recovery guarantee the exact reconstruction of -sparse signals under assumptions on the dictionary that are either too strong or NP-hard to check. Mo…