Uporedite metode

	Nelinearno programiranje	Konveksna optimizacija	Stochastic Optimization
Oblast	Optimizacija	Optimizacija	Optimizacija
Porodica	Process / pipeline	Process / pipeline	Process / pipeline
Godina nastanka	2006	2004	1951 (SGD); 2014 (Adam)
Tvorac	Jorge Nocedal & Stephen Wright	Stephen Boyd & Lieven Vandenberghe	—
Tip	Continuous mathematical optimization	Mathematical optimization framework	Gradient-based iterative optimization
Temeljni izvor	Nocedal, J., & Wright, S. J. (2006). Numerical Optimization (2nd ed.). Springer. ISBN: 978-0-387-30303-1	Boyd, S., & Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press. ISBN: 978-0-521-83378-3	Robbins, H. & Monro, S. (1951). A Stochastic Approximation Method. Annals of Mathematical Statistics, 22(3), 400-407.
Drugi nazivi	NLP optimization, Constrained nonlinear optimization, Smooth optimization, Doğrusal olmayan programlama	Convex Programming, Disciplined Convex Programming, Dışbükey Optimizasyon, Convex Mathematical Programming	Stokastik Optimizasyon (SGD & Varyantları), stochastic gradient descent, SGD, Adam
Srodne	3	3	3
Sažetak	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.	Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets. Formalized and popularized by Stephen Boyd and Lieven Vandenberghe in their landmark 2004 textbook, the framework unifies a wide family of problems — including linear programming, quadratic programming, semidefinite programming, and second-order cone programming — under a single theoretical roof. Its defining property is that any locally optimal solution is also globally optimal, making it tractable and reliable for engineering, statistics, machine learning, and operations research.	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.