iteration complexity
Iteration complexity is a measurement of the number of iterations required by an algorithm to achieve a certain level of accuracy or convergence, relevant in evaluating the efficiency of optimization algorithms.
- Adaptive Riemannian ADMM for Nonsmooth Optimization: Optimal Complexity without Smoothing
- Decreasing Entropic Regularization Averaged Gradient for Semi-Discrete Optimal Transport
- Multi-Objective Reinforcement Learning with Max-Min Criterion: A Game-Theoretic Approach
- Non-rectangular Robust MDPs with Normed Uncertainty Sets
- Quasi-Self-Concordant Optimization with $\ell_{\infty}$ Lewis Weights
- Semi-infinite Nonconvex Constrained Min-Max Optimization
- Stochastic Momentum Methods for Non-smooth Non-Convex Finite-Sum Coupled Compositional Optimization