Convex Optimization and the Dual Problem
Diagonal preconditioning for first order primal-dual algorithms in convex optimization
... of the algorithms. We define the relative error of the objec- tive function as δ k = |E ∗ − E k |/|E ∗ |, where E ∗ refers to the optimal value and E k is the value of the current iterate. Depending of the particular ...
8
A comparison of alternative c-conjugate dual problems in infinite convex optimization
... Lagrange dual problems, it is possible to build a new dual one, which is called Fenchel-Lagrange dual ...new dual problem was coined by Boµ and Wanka in [23] in a nite dimen- sional ...
21
A Universal Primal-Dual Convex Optimization Framework
... ; Department of Statistics and Operations Research, UNC, USA [email protected] Abstract We propose a new primal-dual algorithmic framework for a prototypical con- strained convex optimization ...
9
A primal dual algorithm framework for convex saddle point optimization
... Full list of author information is available at the end of the article Abstract In this study, we introduce a primal-dual prediction-correction algorithm framework for convex optimization problems ...
16
Recipes for Building the Dual of Conic Optimization Problem
... Pembentukan dual dari masalah primal untuk Optimisasi Konik (OK) adalah suatu langkah yang sangat penting untuk menentukan solusi ...masalah dual dalam kasus-kasus ...pembentukan dual dalam hal ini ...
15
THE CONVERGENCE OF BFGS METHOD UNDER STRONGLY CONVEX OPTIMIZATION PROBLEM
... The most well-known optimization technique for unconstrained problems is Newton method. It is effective and robust. The second derivative need to be calculated analytically and supplied to the algorithm by the ...
14
Coupled-decompositions: exploiting primal–dual interactions in convex optimization problems
... in convex optimization problems, being primal and dual decompositions the two classical ...centralized optimization schemes with advantages over the existing techniques in the ...
18
Primal-Dual Active-Set Methods for Convex Quadratic Optimization with Applications
... dating dual variables until all variables become ...linear optimization, ...and dual- procedure, are involved in the algorithm ...the dual-procedure is invoked which either terminates the ...
160
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization
... constrained convex problems by choosing an appropriate h. Within convex optimization, coordinate descent methods have recently become increasingly popular in the literature ...the convex ...
19
A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization
... to dual gradient-type ...the dual function or smooth the dual function using proximity terms [50, ...the dual domain is unbounded, which leads to a difficulty to estimate the worst-case ...
39
L1 regularized logistic regression requires solving a convex optimization problem
... a convex optimization ...solving convex optimization problems do not scale well enough to handle the large datasets encountered in many practical ...solving convex optimization ...
8
Primal Recovery from Consensus-Based Dual Decomposition for Distributed Convex Optimization
... Abstract Dual decomposition has been successfully employed in a variety of dis- tributed convex optimization problems solved by a network of computing and communicating ...the dual ...
26
Distributed constrained convex optimization and consensus via dual decomposition and proximal minimization
... constrained convex optimization and consensus via dual decomposition and proximal minimization Alessandro Falsone, Kostas Margellos, Simone Garatti, Maria Prandini Abstract— We consider a general ...
6
On the Convex Feasibility Problem
... In the last decades a number of papers were written on the subject and results concerning some theoretical aspects and particularly algorithms for solving this problem were obtained. The interest for the ...
14
An Optimization Algorithm for Multi-objective Optimization Problem by Using Envelope-dual Method
... Envelope Dual Method (EDM) is utilized to solve the optimization problem with multi-objective, multi- constraints and multi-variable for the large complex engineering system optimization ...
10
Stochastic Convex Optimization
... 4 Learnable, but not with Empirical Minimizer The results of the Section 2.2 suggest that perhaps Lipschitz continuity is enough for obtaining guarantees on stochastic convex optimization using a more ...
9
Stochastic Convex Optimization
... online convex opti- mization have been derived under very general ...stochastic convex optimization problem is learnable ...strongly convex problems are learnable using empirical ...
9
Online Convex Optimization
... online convex optimization which, as we shall see, encompasses some of the online learning problems we have seen so far as special ...(offline) convex optimization problem involves a ...
6
On vector matrix game and symmetric dual vector optimization problem
... symmetric dual problem for a nonlinear vector optimization problem is ...symmetric dual problem and its related vector matrix ...
8