Can Decentralized Stochastic Minimax Optimization Algorithms Converge Linearly for Finite-Sum Nonconvex-Nonconcave Problems?
Yihan ZhangWenhao JiangFeng ZhengChiu C. TanXinghua ShiHongchang GaoPublished in: CoRR (2023)
Keyphrases
- optimization problems
- combinatorial optimization
- evolutionary algorithm
- discrete optimization
- objective function
- convex optimization problems
- optimization methods
- quadratic optimization problems
- optimization approaches
- nonlinear programming
- combinatorial optimization problems
- metaheuristic
- worst case
- stochastic optimization
- computational complexity
- distributed constraint optimization
- benchmark problems
- global optimization
- np complete
- subgradient method
- approximation schemes
- distributed systems
- stochastic search
- monte carlo methods
- monte carlo
- optimal control problems
- reinforcement learning
- newton method
- multi agent
- optimization criteria
- stationary points
- convergence analysis
- global convergence
- partial solutions
- learning algorithm
- convergence rate
- decision problems