Content tagged with optimization

• [W8] Explicit second-order min-max optimization methods with optimal convergence guarantees
• [J43] The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
• [C91] The computational complexity of finding second-order stationary points
• [C90] What is the long-run distribution of stochastic gradient descent? A large deviations analysis
• [W7] Setwise coordinate descent for dual asynchronous decentralized optimization
• [C87] Riemannian stochastic optimization methods avoid strict saddle points
• [J37] Distributed stochastic optimization with large delays
• [J36] Multi-agent online optimization with delays: Asynchronicity, adaptivity, and optimism
• [C81] Pick your neighbor: Local Gauss-Southwell rule for fast asynchronous decentralized optimization
• [C78] AdaGrad avoids saddle points
• [C77] UnderGrad: A universal black-box optimization method with almost dimension-free convergence rate guarantees
• [C75] The dynamics of Riemannian Robbins-Monro algorithms
• [J34] Minibatch forward-backward-forward methods for solving stochastic variational inequalities
• [C73] Fast routing under uncertainty: Adaptive learning in congestion games with exponential weights
• [C71] Sifting through the noise: Universal first-order methods for stochastic variational inequalities
• [C70] Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements
• [C69] Equilibrium tracking and convergence in dynamic games
• [C68] Optimization in open networks via dual averaging
• [C67] Adaptive learning in continuous games: Optimal regret bounds and convergence to Nash equilibrium
• [C65] The last-iterate convergence rate of optimistic mirror descent in stochastic variational inequalities
• [C64] The limits of min-max optimization algorithms: Convergence to spurious non-critical sets
• [C62] Regret minimization in stochastic non-convex learning via a proximal-gradient approach
• [C61] Adaptive extra-gradient methods for min-max optimization and games
• [J31] On the convergence of mirror descent beyond stochastic convex programming
• [C60] On the almost sure convergence of stochastic gradient descent in non-convex problems
• [C57] Explore aggressively, update conservatively: Stochastic extragradient methods with variable stepsize scaling
• [C56] A new regret analysis for Adam-type algorithms
• [C51] Online and stochastic optimization beyond Lipschitz continuity: A Riemannian approach
• [D3] Online optimization and learning in games: Theory and applications
• [J29] Hessian barrier algorithms for linearly constrained optimization problems
• [C50] On the convergence of single-call stochastic extra-gradient methods
• [C49] An adaptive mirror-prox algorithm for variational inequalities with singular operators
• [C48] Convergent noisy forward-backward-forward algorithms in non-monotone variational inequalities
• [C46] Cautious regret minimization: Online optimization with long-term budget constraints
• [C44] Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile
• [C43] Large-scale network utility maximization: Countering exponential growth with exponentiated gradients
• [W3] Online convex optimization and no-regret learning: Algorithms, guarantees and applications
• [J26] Stochastic mirror descent dynamics and their convergence in monotone variational inequalities
• [J24] On the convergence of gradient-like flows with noisy gradient input
• [C39] On the convergence of stochastic forward-backward-forward algorithms with variance reduction
• [C37] Distributed asynchronous optimization with unbounded delays: How slow can you go?
• [C36] A resource allocation framework for network slicing
• [J22] Distributed stochastic optimization via matrix exponential learning
• [J21] A continuous-time approach to online optimization
• [C32] Stochastic mirror descent in variationally coherent optimization problems
• [W2] Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty
• [J15] A stochastic approximation algorithm for stochastic semidefinite programming
• [C24] Distributed learning for resource allocation under uncertainty
• [J11] Inertial game dynamics and applications to constrained optimization
• [C16] Distributed optimization in multi-user MIMO systems with imperfect and delayed information
• [C11] Accelerating population-based search heuristics by adaptive resource allocation
• [C8] Matrix exponential learning: Distributed optimization in MIMO systems