2 citations · 6 across the 21 of their papers we have counts for
19 papers · 1 filter
Adaptive Matrix Online Learning through Smoothing with Guarantees for Nonsmooth Nonconvex Optimization
Ruichen Jiang, Zakaria Mhammedi, Mehryar Mohri +1
We study online linear optimization with matrix variables constrained by the operator norm, a setting where the geometry renders designing data-dependent and efficient adaptive alg…
Improving Online-to-Nonconvex Conversion for Smooth Optimization via Double Optimism
Francisco Patitucci, Ruichen Jiang, Aryan Mokhtari
A recent breakthrough in nonconvex optimization is the online-to-nonconvex conversion framework of [Cutkosky et al., 2023], which reformulates the task of finding an -…
On the Complexity of Finding Stationary Points in Nonconvex Simple Bilevel Optimization
Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani +1
In this paper, we study the problem of solving a simple bilevel optimization problem, where the upper-level objective is minimized over the solution set of the lower-level problem.…
Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods
Ruichen Jiang, Aryan Mokhtari, Francisco Patitucci
We study the problem of finding an -first-order stationary point (FOSP) of a smooth function, given access only to gradient information. The best-known gradient query complexity…
Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence
Ruichen Jiang, Aryan Mokhtari
In this paper, we propose a quasi-Newton method for solving smooth and monotone nonlinear equations, including unconstrained minimization and minimax optimization as special cases.…
Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
Ruichen Jiang, Devyani Maladkar, Aryan Mokhtari
Adaptive gradient methods, such as AdaGrad, are among the most successful optimization algorithms for neural network training. While these methods are known to achieve better dimen…
Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization
Ruichen Jiang, Ali Kavis, Qiujiang Jin +2
We propose adaptive, line search-free second-order methods with optimal rate of convergence for solving convex-concave min-max problems. By means of an adaptive step size, our algo…
Stochastic Newton Proximal Extragradient Method
Ruichen Jiang, Michał Dereziński, Aryan Mokhtari
Stochastic second-order methods achieve fast local convergence in strongly convex optimization by using noisy Hessian estimates to precondition the gradient. However, these methods…
Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search
Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari
In this paper, we present the first explicit and non-asymptotic global convergence rates of the BFGS method when implemented with an inexact line search scheme satisfying the Armij…
Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search
Qiujiang Jin, Ruichen Jiang, Aryan Mokhtari
In this paper, we explore the non-asymptotic global convergence rates of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method implemented with exact line search. Notably, due to Dixo…
An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
Jincheng Cao, Ruichen Jiang, Erfan Yazdandoost Hamedani +1
In this paper, we focus on simple bilevel optimization problems, where we minimize a convex smooth objective function over the optimal solution set of another convex smooth constra…
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
Ruichen Jiang, Parameswaran Raman, Shoham Sabach +3
Second-order optimization methods, such as cubic regularized Newton methods, are known for their rapid convergence rates; nevertheless, they become impractical in high-dimensional…
Projection-Free Methods for Stochastic Simple Bilevel Optimization with Convex Lower-level Problem
Jincheng Cao, Ruichen Jiang, Nazanin Abolfazli +2
In this paper, we study a class of stochastic bilevel optimization problems, also known as stochastic simple bilevel optimization, where we minimize a smooth stochastic objective f…
Accelerated Quasi-Newton Proximal Extragradient: Faster Rate for Smooth Convex Optimization
Ruichen Jiang, Aryan Mokhtari
In this paper, we propose an accelerated quasi-Newton proximal extragradient (A-QPNE) method for solving unconstrained smooth convex optimization problems. With access only to the…
An Inexact Conditional Gradient Method for Constrained Bilevel Optimization
Nazanin Abolfazli, Ruichen Jiang, Aryan Mokhtari +1
Bilevel optimization is an important class of optimization problems where one optimization problem is nested within another. While various methods have emerged to address unconstra…
Online Learning Guided Curvature Approximation: A Quasi-Newton Method with Global Non-Asymptotic Superlinear Convergence
Ruichen Jiang, Qiujiang Jin, Aryan Mokhtari
Quasi-Newton algorithms are among the most popular iterative methods for solving unconstrained minimization problems, largely due to their favorable superlinear convergence propert…
A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem
Ruichen Jiang, Nazanin Abolfazli, Aryan Mokhtari +1
In this paper, we study a class of bilevel optimization problems, also known as simple bilevel optimization, where we minimize a smooth objective function over the optimal solution…
Generalized Optimistic Methods for Convex-Concave Saddle Point Problems
Ruichen Jiang, Aryan Mokhtari
The optimistic gradient method has seen increasing popularity for solving convex-concave saddle point problems. To analyze its iteration complexity, a recent work [arXiv:1906.01115…
Tightness and Equivalence of Semidefinite Relaxations for MIMO Detection
Ruichen Jiang, Ya-Feng Liu, Chenglong Bao +1
The multiple-input multiple-output (MIMO) detection problem, a fundamental problem in modern digital communications, is to detect a vector of transmitted symbols from the noisy out…