229 citations · 547 across the 23 of their papers we have counts for
4 papers · 1 filter
Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli +1
We consider the classical setting of optimizing a nonsmooth Lipschitz continuous convex function over a convex constraint set, when having access to a (stochastic) first-order orac…
Efficient Algorithms for Smooth Minimax Optimization
Kiran Koshy Thekumparampil, Prateek Jain, Praneeth Netrapalli +1
This paper studies first order methods for solving smooth minimax optimization problems where is smooth and is concave for each…
Making the Last Iterate of SGD Information Theoretically Optimal
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Stochastic gradient descent (SGD) is one of the most widely used algorithms for large scale optimization problems. While classical theoretical analysis of SGD for convex problems s…
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
We study stochastic gradient descent {\em without replacement} (\sgdwor) for smooth convex functions. \sgdwor is widely observed to converge faster than true \sgd where each sample…