436 citations · 1.4k across the 41 of their papers we have counts for
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Alternating Direction Method of Multipliers for Quantization
Tianjian Huang, Prajwal Singhania, Maziar Sanjabi +2
Quantization of the parameters of machine learning models, such as deep neural networks, requires solving constrained optimization problems, where the constraint set is formed by t…
Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances
Meisam Razaviyayn, Tianjian Huang, Songtao Lu +3
The min-max optimization problem, also known as the saddle point problem, is a classical optimization problem which is also studied in the context of zero-sum games. Given a class…
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…
On the Linear Convergence of the Approximate Proximal Splitting Method for Non-Smooth Convex Optimization
Mojtaba Kadkhodaie, Maziar Sanjabi, Zhi-Quan Luo
Consider the problem of minimizing the sum of two convex functions, one being smooth and the other non-smooth. In this paper, we introduce a general class of approximate proximal s…
A Stochastic Successive Minimization Method for Nonsmooth Nonconvex Optimization with Applications to Transceiver Design in Wireless Communication Networks
Meisam Razaviyayn, Maziar Sanjabi, Zhi-Quan Luo
Consider the problem of minimizing the expected value of a cost function parameterized by a random variable. The classical sample average approximation (SAA) method for solving thi…