307 citations · 693 across the 70 of their papers we have counts for
6 papers · 1 filter
An Extended Frank-Wolfe Method with "In-Face" Directions, and its Application to Low-Rank Matrix Completion
Robert M. Freund, Paul Grigas, Rahul Mazumder
Motivated principally by the low-rank matrix completion problem, we present an extension of the Frank-Wolfe method that is designed to induce near-optimal solutions on low-dimensio…
A Computational Framework for Multivariate Convex Regression and its Variants
Rahul Mazumder, Arkopal Choudhury, Garud Iyengar +1
We study the nonparametric least squares estimator (LSE) of a multivariate convex regression function. The LSE, given as the solution to a quadratic program with linear co…
Scalable Computation of Regularized Precision Matrices via Stochastic Optimization
Yves F. Atchadé, Rahul Mazumder, Jie Chen
We consider the problem of computing a positive definite inverse covariance matrix aka precision matrix which optimizes a regularized Gaussian maximum lik…
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
Rahul Mazumder, Peter Radchenko
We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on…
Best Subset Selection via a Modern Optimization Lens
Dimitris Bertsimas, Angela King, Rahul Mazumder
In the last twenty-five years (1990-2014), algorithmic advances in integer optimization combined with hardware improvements have resulted in an astonishing 200 billion factor speed…
A New Perspective on Boosting in Linear Regression via Subgradient Optimization and Relatives
Robert M. Freund, Paul Grigas, Rahul Mazumder
In this paper we analyze boosting algorithms in linear regression from a new perspective: that of modern first-order methods in convex optimization. We show that classic boosting a…