Iteration complexity analysis of dual first-order methods for conic convex programming

DOI10.1080/10556788.2016.1161763zbMath1342.90135arXiv1409.1462OpenAlexW2234873940MaRDI QIDQ2815553

Ion Necoara, Andrei T. Patrascu

Publication date: 29 June 2016

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1409.1462

zbMATH Keywords

rate of convergenceaproximate primal feasible and suboptimal solutionconic convex problemdual first-order methodssmooth dual function

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Convex programming (90C25) Optimality conditions and duality in mathematical programming (90C46) Duality theory (optimization) (49N15)

Related Items (11)

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent ⋮ Generalizing the Optimized Gradient Method for Smooth Convex Minimization ⋮ On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems ⋮ A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization ⋮ Another Look at the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) ⋮ Augmented Lagrangian optimization under fixed-point arithmetic ⋮ Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming ⋮ Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization ⋮ Solving nearly-separable quadratic optimization problems as nonsmooth equations ⋮ Non-stationary First-Order Primal-Dual Algorithms with Faster Convergence Rates ⋮ On the Nonergodic Convergence Rate of an Inexact Augmented Lagrangian Framework for Composite Convex Programming

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