5 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
New Penalized Stochastic Gradient Methods for Linearly Constrained Strongly Convex Optimization
Meng Li, Paul Grigas, Alper Atamturk
For minimizing a strongly convex objective function subject to linear inequality constraints, we consider a penalty approach that allows one to utilize stochastic methods for probl…
Stochastic In-Face Frank-Wolfe Methods for Non-Convex Optimization and Sparse Neural Network Training
Paul Grigas, Alfonso Lobos, Nathan Vermeersch
The Frank-Wolfe method and its extensions are well-suited for delivering solutions with desirable structural properties, such as sparsity or low-rank structure. We introduce a new…
Condition Number Analysis of Logistic Regression, and its Implications for Standard First-Order Solution Methods
Robert M. Freund, Paul Grigas, Rahul Mazumder
Logistic regression is one of the most popular methods in binary classification, wherein estimation of model parameters is carried out by solving the maximum likelihood (ML) optimi…
Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform Under Many Auction Types
Alfonso Lobos, Paul Grigas, Zheng Wen +1
We develop a novel optimization model to maximize the profit of a Demand-Side Platform (DSP) while ensuring that the budget utilization preferences of the DSP's advertiser clients…
Profit Maximization for Online Advertising Demand-Side Platforms
Paul Grigas, Alfonso Lobos, Zheng Wen +1
We develop an optimization model and corresponding algorithm for the management of a demand-side platform (DSP), whereby the DSP aims to maximize its own profit while acquiring val…