3 citations · 3 across the 1 of their papers we have counts for
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Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
David Applegate, Mateo Díaz, Haihao Lu +1
We study the problem of detecting infeasibility of large-scale linear programming problems using the primal-dual hybrid gradient method (PDHG) of Chambolle and Pock (2011). The lit…
Frank-Wolfe Methods with an Unbounded Feasible Region and Applications to Structured Learning
Haoyue Wang, Haihao Lu, Rahul Mazumder
The Frank-Wolfe (FW) method is a popular algorithm for solving large-scale convex optimization problems appearing in structured statistical learning. However, the traditional Frank…
Limiting Behaviors of Nonconvex-Nonconcave Minimax Optimization via Continuous-Time Systems
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Unlike nonconvex optimization, where gradient descent is guaranteed to converge to a local optimizer, algorithms for nonconvex-nonconcave minimax optimization can have topologicall…
The Landscape of the Proximal Point Method for Nonconvex-Nonconcave Minimax Optimization
Benjamin Grimmer, Haihao Lu, Pratik Worah +1
Minimax optimization has become a central tool in machine learning with applications in robust optimization, reinforcement learning, GANs, etc. These applications are often nonconv…
Contextual Reserve Price Optimization in Auctions via Mixed-Integer Programming
Joey Huchette, Haihao Lu, Hossein Esfandiari +1
We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue from the seller side. Fi…
Generalized Stochastic Frank-Wolfe Algorithm with Stochastic "Substitute" Gradient for Structured Convex Optimization
Haihao Lu, Robert M. Freund
The stochastic Frank-Wolfe method has recently attracted much general interest in the context of optimization for statistical and machine learning due to its ability to work with a…