Pages that link to "Item:Q116219"

The following pages link to Primal-dual subgradient methods for convex problems (Q116219):

Displaying 50 items.

• Communication-computation tradeoff in distributed consensus optimization for MPC-based coordinated control under wireless communications (Q2012089) (← links)
• Gradient-free method for nonsmooth distributed optimization (Q2018475) (← links)
• Resolving learning rates adaptively by locating stochastic non-negative associated gradient projection points using line searches (Q2022225) (← links)
• Nearly optimal first-order methods for convex optimization under gradient norm measure: an adaptive regularization approach (Q2031939) (← links)
• Distributed linear regression by averaging (Q2039793) (← links)
• Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems (Q2042418) (← links)
• Flexible Bayesian dynamic modeling of correlation and covariance matrices (Q2057355) (← links)
• Ensemble slice sampling. Parallel, black-box and gradient-free inference for correlated \& multimodal distributions (Q2058806) (← links)
• A distributed Bregman forward-backward algorithm for a class of Nash equilibrium problems (Q2095329) (← links)
• Surplus-based accelerated algorithms for distributed optimization over directed networks (Q2097723) (← links)
• Gradient projection Newton algorithm for sparse collaborative learning using synthetic and real datasets of applications (Q2104053) (← links)
• Learning in nonatomic games. I: Finite action spaces and population games (Q2106066) (← links)
• Optimum dimensional synthesis of planar mechanisms with geometric constraints (Q2121228) (← links)
• Primal dual methods for Wasserstein gradient flows (Q2143215) (← links)
• Asymptotic properties of dual averaging algorithm for constrained distributed stochastic optimization (Q2154832) (← links)
• Aggregation of estimators and stochastic optimization (Q2197367) (← links)
• Primal-dual incremental gradient method for nonsmooth and convex optimization problems (Q2230784) (← links)
• Incrementally updated gradient methods for constrained and regularized optimization (Q2251572) (← links)
• Faster algorithms for extensive-form game solving via improved smoothing functions (Q2288198) (← links)
• A modular analysis of adaptive (non-)convex optimization: optimism, composite objectives, variance reduction, and variational bounds (Q2290691) (← links)
• A non-monotone conjugate subgradient type method for minimization of convex functions (Q2302755) (← links)
• Accelerated primal-dual gradient descent with linesearch for convex, nonconvex, and nonsmooth optimization problems (Q2313241) (← links)
• Universal method of searching for equilibria and stochastic equilibria in transportation networks (Q2314190) (← links)
• Primal convergence from dual subgradient methods for convex optimization (Q2340335) (← links)
• Sparse learning via Boolean relaxations (Q2349117) (← links)
• Quasi-monotone subgradient methods for nonsmooth convex minimization (Q2349846) (← links)
• Large-scale unit commitment under uncertainty (Q2351161) (← links)
• On efficient randomized algorithms for finding the PageRank vector (Q2354453) (← links)
• On the efficiency of a randomized mirror descent algorithm in online optimization problems (Q2354481) (← links)
• On the robustness of learning in games with stochastically perturbed payoff observations (Q2357809) (← links)
• Hedge algorithm and dual averaging schemes (Q2392814) (← links)
• Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case (Q2397263) (← links)
• Iteratively reweighted \(\ell _1\) algorithms with extrapolation (Q2419549) (← links)
• Distributed and consensus optimization for non-smooth image reconstruction (Q2516369) (← links)
• Inexact subgradient methods for quasi-convex optimization problems (Q2629635) (← links)
• Asymptotic optimality in stochastic optimization (Q2656586) (← links)
• General Hölder smooth convergence rates follow from specialized rates assuming growth bounds (Q2696991) (← links)
• A Subgradient Method Based on Gradient Sampling for Solving Convex Optimization Problems (Q2795104) (← links)
• A simple but usually fast branch-and-bound algorithm for the capacitated facility location problem (Q2815470) (← links)
• A family of subgradient-based methods for convex optimization problems in a unifying framework (Q2829570) (← links)
• A subgradient method for free material design (Q2832891) (← links)
• Learning in games via reinforcement and regularization (Q2833105) (← links)
• Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization (Q2921184) (← links)
• Block Stochastic Gradient Iteration for Convex and Nonconvex Optimization (Q2945126) (← links)
• MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization (Q3179624) (← links)
• Inertial Game Dynamics and Applications to Constrained Optimization (Q3195290) (← links)
• Some multivariate risk indicators: Minimization by using a Kiefer–Wolfowitz approach to the mirror stochastic algorithm (Q3224136) (← links)
• Structured Sparsity: Discrete and Convex Approaches (Q3460840) (← links)
• Using the ?-subgradient method to solve the dual and the primal mathematical programming problems (Q3771978) (← links)
• Constrained ?-subgradient method for simultaneous solution of the primal and dual problems of convex programming (Q4005346) (← links)