112 citations · 288 across the 11 of their papers we have counts for
7 papers · 1 filter
Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods
Maher Nouiehed, Maziar Sanjabi, Tianjian Huang +2
Recent applications that arise in machine learning have surged significant interest in solving min-max saddle point games. This problem has been extensively studied in the convex-c…
Solving Non-Convex Non-Concave Min-Max Games Under Polyak-Łojasiewicz Condition
Maziar Sanjabi, Meisam Razaviyayn, Jason D. Lee
In this short note, we consider the problem of solving a min-max zero-sum game. This problem has been extensively studied in the convex-concave regime where the global solution can…
Convergence to Second-Order Stationarity for Constrained Non-Convex Optimization
Maher Nouiehed, Jason D. Lee, Meisam Razaviyayn
We consider the problem of finding an approximate second-order stationary point of a constrained non-convex optimization problem. We first show that, unlike the gradient descent me…
Provably Correct Automatic Subdifferentiation for Qualified Programs
Sham Kakade, Jason D. Lee
The Cheap Gradient Principle (Griewank 2008) --- the computational cost of computing the gradient of a scalar-valued function is nearly the same (often within a factor of ) as t…
Stochastic subgradient method converges on tame functions
Damek Davis, Dmitriy Drusvyatskiy, Sham Kakade +1
This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity? We prove that the stochastic s…
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solutions for Nonconvex Distributed Optimization
Mingyi Hong, Jason D. Lee, Meisam Razaviyayn
In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM),…