Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическое программирование× | Стохастическая оптимизация× | |
|---|---|---|
| Область | Оптимизация | Оптимизация |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 1957 | 1951 (SGD); 2014 (Adam) |
| Автор метода≠ | Richard Bellman | — |
| Тип≠ | Exact combinatorial optimization via recursive decomposition | Gradient-based iterative optimization |
| Основополагающий источник≠ | Bellman, R. (1957). Dynamic Programming. Princeton University Press. ISBN: 978-0-691-07951-6 | Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407. DOI ↗ |
| Другие названия≠ | DP, Bellman's Principle of Optimality, Recursive Optimization, Dinamik Programlama | Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam |
| Связанные | 3 | 3 |
| Сводка≠ | Dynamic Programming (DP) is an exact optimization technique introduced by Richard Bellman in 1957 for solving multi-stage decision problems. It decomposes a complex problem into simpler, overlapping subproblems, solves each subproblem once, and stores the results to avoid redundant computation. Grounded in the Principle of Optimality, DP guarantees globally optimal solutions whenever the problem exhibits overlapping subproblems and optimal substructure. | Stochastic optimization is a family of iterative methods that minimize an objective function by computing gradients on randomly sampled subsets of data — mini-batches — rather than on the entire dataset at once. Pioneered by Robbins and Monro in 1951 as stochastic approximation, the approach became the standard engine for training large-scale machine-learning models through variants such as SGD with momentum, AdaGrad, RMSProp, and Adam. |
| ScholarGateНабор данных ↗ |
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