Controllability and a Multiplier Rule for Nondifferentiable Optimization Problems

1978 ◽  
Vol 16 (5) ◽  
pp. 803-812 ◽  
Author(s):  
J. Warga
Keyword(s):  
Nondifferentiable Optimization ◽  
Optimization Problems ◽  
Multiplier Rule

scholarly journals Quasi-regularity in Optimization

1986 ◽  
Vol 41 (2) ◽  
pp. 188-192 ◽  
Author(s):  
Kung-Fu Ng ◽  
David Yost
Keyword(s):  
Banach Space ◽  
Lagrange Multiplier ◽  
Optimization Problems ◽  
General Situation ◽  
Lagrange Multiplier Rule ◽  
Infinite Dimensional ◽  
Space Setting ◽  
Multiplier Rule ◽  
Dimensional Version ◽  
Definition Of

AbstractThe notion of quasi-regularity, defined for optimization problems in Rn, is extended to the Banach space setting. Examples are given to show that our definition of quasi-regularity is more natural than several other possibilities in the general situation. An infinite dimensional version of the Lagrange multiplier rule is established.

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.

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