Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Двоуровнева оптимізація (Лідер-Послідовник)× | Нелінійне програмування× | |
|---|---|---|
| Галузь | Оптимізація | Оптимізація |
| Родина | Process / pipeline | Process / pipeline |
| Рік появи≠ | 1998 | 2006 |
| Автор методу≠ | Jonathan Bard | Jorge Nocedal & Stephen Wright |
| Тип≠ | Hierarchical mathematical programming | Continuous mathematical optimization |
| Основоположне джерело≠ | Bard, J. F. (1998). Practical Bilevel Optimization: Algorithms and Applications. Kluwer Academic Publishers. ISBN: 978-0-7923-5458-7 | Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1 |
| Інші назви | Stackelberg Optimization, Hierarchical Programming, Nested Optimization, İki Düzeyli Optimizasyon | NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama |
| Пов'язані | 3 | 3 |
| Підсумок≠ | Bilevel optimization is a class of mathematical programming problems in which one optimization problem is nested inside another. The upper-level (leader) problem optimizes its objective subject to constraints that include the solution of a lower-level (follower) problem. Formalized comprehensively by Jonathan Bard in 1998, the framework models hierarchical decision-making where the leader anticipates and accounts for the rational response of the follower. | Nonlinear programming (NLP) is a branch of mathematical optimization concerned with problems in which the objective function or at least one constraint is nonlinear. Formalized comprehensively by Jorge Nocedal and Stephen Wright in their seminal 2006 text, NLP encompasses gradient-based algorithms — including sequential quadratic programming (SQP), interior-point methods, and quasi-Newton approaches — for finding locally or globally optimal solutions to continuous decision problems arising across engineering, economics, and the physical sciences. |
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