15 citations · 30 across the 6 of their papers we have counts for
8 papers
A Different Perspective On The Stochastic Convex Feasibility Problem
James Renegar, Song Zhou
We analyze a simple randomized subgradient method for approximating solutions to stochastic systems of convex functional constraints, the only input to the algorithm being the size…
A Simple Nearly-Optimal Restart Scheme For Speeding-Up First Order Methods
James Renegar, Benjamin Grimmer
We present a simple scheme for restarting first-order methods for convex optimization problems. Restarts are made based only on achieving specified decreases in objective values, t…
"Efficient" Subgradient Methods for General Convex Optimization
James Renegar
A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of itera…
Accelerated First-Order Methods for Hyperbolic Programming
James Renegar
A framework is developed for applying accelerated methods to general hyperbolic programming, including linear, second-order cone, and semidefinite programming as special cases. The…
A Framework for Applying Subgradient Methods to Conic Optimization Problems
James Renegar
A framework is presented whereby a general convex conic optimization problem is transformed into an equivalent convex optimization problem whose only constraints are linear equatio…
A Polynomial-Time Affine-Scaling Method for Semidefinite and Hyperbolic Programming
James Renegar, Mutiara Sondjaja
We develop a natural variant of Dikin's affine-scaling method, first for semidefinite programming and then for hyperbolic programming in general. We match the best complexity bound…
Efficient First-Order Methods for Linear Programming and Semidefinite Programming
James Renegar
We present a simple transformation of any linear program or semidefinite program into an equivalent convex optimization problem whose only constraints are linear equations. The obj…
Central Swaths (A Generalization of the Central Path)
James Renegar
We develop a natural generalization to the notion of the central path -- a notion that lies at the heart of interior-point methods for convex optimization. The generalization is ac…