A smoothing technique for nondifferentiable optimization problems

2006 ◽  
pp. 1-11 ◽  
Author(s):  
A. Ben-Tal ◽  
M. Teboulle
Keyword(s):  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Smoothing Technique

scholarly journals On the acceleration of the double smoothing technique for unconstrained convex optimization problems

Optimization ◽  
2012 ◽  
Vol 64 (2) ◽  
pp. 265-288 ◽  
Author(s):  
Radu Ioan Boţ ◽  
Christopher Hendrich
Keyword(s):  
Convex Optimization ◽  
Optimization Problems ◽  
Smoothing Technique ◽  
Convex Optimization Problems ◽  
Double Smoothing

scholarly journals A modified simplex embedding method for solving convex optimization problems with a large amount of constraints

2019 ◽  
pp. 428-437
Author(s):  
А.В. Колосницын
Keyword(s):  
Convex Optimization ◽  
Execution Time ◽  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Numerical Results ◽  
Embedding Method ◽  
Convex Optimization Problems ◽  
Convex Nondifferentiable Optimization

Рассматривается метод симплексных погружений, адаптированный для решения задач выпуклой оптимизации с большим числом ограничений. Разработаны две модификации, позволяющие ускорять работу метода. Первая из них использует более экономичный способ расчета невязок ограничений, что позволяет существенно сокращать время работы алгоритма в случае большой размерности задачи. Вторая модификация основана на возможности метода определять неактивные ограничения задачи. Представлены результаты вычислительных экспериментов с использованием модифицированных версий метода симплексных погружений при решении тестовых задач квадратичной и выпуклой недифференцируемой оптимизации. A simplex embedding method adapted for solving convex optimization problems with a large amount of constraints is considered. Two modifications of the method are proposed for better performance. First of them uses a more economical approach to the residual computation for constraints, which allows one to significantly reduce the execution time of the algorithm in the case of a large amount of constraints. One of the important peculiarities of the simplex embedding method is its ability to find inactive constraints. This property of the method is used as the basis for its second modification. The numerical results obtained when solving a number of quadratic and convex nondifferentiable optimization problems show the efficiency of the proposed modifications.

A feasible direction subgradient algorithm for a class of nondifferentiable optimization problems

1979 ◽  
Author(s):  
Jacques Chatelon ◽  
Donald Hearn ◽  
Timothy J. Lowe
Keyword(s):  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Feasible Direction ◽  
Subgradient Algorithm

Lagrange Programming Neural Network for Nondifferentiable Optimization Problems in Sparse Approximation

2017 ◽  
Vol 28 (10) ◽  
pp. 2395-2407 ◽  
Author(s):  
Ruibin Feng ◽  
Chi-Sing Leung ◽  
Anthony G. Constantinides ◽  
Wen-Jun Zeng
Keyword(s):  
Neural Network ◽  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Sparse Approximation

Nondifferentiable Optimization Problems for Elliptic Systems

1985 ◽  
Vol 23 (4) ◽  
pp. 632-648 ◽  
Author(s):  
Andrzej Myśliński ◽  
Jan Sokołowski
Keyword(s):  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Elliptic Systems
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