Comparer des méthodes

	Programmation linéaire	Optimisation stochastique
Domaine	Optimisation	Optimisation
Famille	Process / pipeline	Process / pipeline
Année d'origine	1947	1951 (SGD); 2014 (Adam)
Auteur d'origine	George B. Dantzig	—
Type	Mathematical programming / continuous optimization	Gradient-based iterative optimization
Source fondatrice	Dantzig, G.B. (1963). Linear Programming and Extensions. Princeton University Press. ISBN: 9780691059136	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
Alias	LP, linear optimization, Doğrusal Programlama (LP)	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Apparentées	4	3
Résumé	Linear programming (LP), pioneered by George B. Dantzig in 1947, is a mathematical method for finding the best value of a linear objective function — such as minimum cost or maximum profit — subject to a set of linear inequality and equality constraints. It is the foundational technique in operations research and underlies production planning, resource allocation, logistics, diet problems, and countless other decision-making scenarios across engineering, economics, and the natural sciences.	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.