optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic program is an optimization...

uncertainty. Closely related to stochastic programming and dynamic programming, stochastic dynamic programming represents the problem under scrutiny in...

Jean-Baptiste Robert Wets (February 1937 - April 1, 2025) is a "pioneer" in stochastic programming and a leader in variational analysis who publishes as Roger J-B...

Linear programming is a special case of mathematical programming (also known as mathematical optimization). More formally, linear programming is a technique...

elementary economics Stochastic programming – Framework for modeling optimization problems that involve uncertainty Stochastic dynamic programming – 1957 technique...

and genetic programming. A problem itself may be stochastic as well, as in planning under uncertainty. The financial markets use stochastic models to represent...

the use of EMP for disjunctive programming include scheduling problems in the chemical industry EMP SP is the stochastic extension of the EMP framework...

Robust statistics Robust decision making Robust fuzzy programming Stochastic programming Stochastic optimization Info-gap decision theory Taguchi methods...

Stochastic approximation methods are a family of iterative methods typically used for root-finding problems or for optimization problems. The recursive...

may not be a convex program. In general, whether the program is convex affects the difficulty of solving it. Stochastic programming studies the case in...

Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

include: Linear programming Quadratic programming Nonlinear programming Mixed integer programming Meta-heuristic methods Stochastic programming for multistage...

algorithm, an algorithm for solving linear programming problems, and for his other work with linear programming. In statistics, Dantzig solved two open problems...

Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or...

Optimizer) is a software package for linear programming, integer programming, nonlinear programming, stochastic programming and global optimization. LINGO is a...

In machine learning, the term stochastic parrot is a metaphor to describe the theory that large language models, though able to generate plausible language...

search, on memory, like reactive search optimization, on memory-less stochastic modifications, like simulated annealing. Local search does not provide...

Stochastic dominance is a partial order between random variables. It is a form of stochastic ordering. The concept arises in decision theory and decision...

In stochastic programming, the correlation gap is the worst-case ratio between the cost when the random variables are correlated to the cost when the random...

supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional programming. It is often described...

between Banach spaces. It is particularly suited for applications in stochastic programming and asymptotic statistics. A map φ : D → E {\displaystyle \varphi...

probabilistically constrained stochastic programming problems. These results had impact far beyond the area of mathematical programming, as they found applications...

In probability theory and related fields, a stochastic (/stəˈkæstɪk/) or random process is a mathematical object usually defined as a family of random...

syntax and keywords. It is designed specifically for representing stochastic programming problems and, through recent extensions, problems with chance constraints...

Farkas Monotropic programming Tucker, Albert W. Set-valued analysis Pompeiu–Hausdorff distance Mordukhovich, Boris Stochastic programming Variational analysis...

ISBN 0-13-638098-0. Yong, Jiongmin; Zhou, Xun Yu (1999). "Dynamic Programming and HJB Equations". Stochastic Controls : Hamiltonian Systems and HJB Equations. Springer...

of kinetic equations for probability density functions, or by using a stochastic sampling method. The method is an adaptation of the Metropolis–Hastings...

optimization Convex programming Fractional programming Integer programming Quadratic programming Nonlinear programming Stochastic programming Robust optimization...

some efforts have been done to create adaptive topologies (SPSO, APSO, stochastic star, TRIBES, Cyber Swarm, and C-PSO) By using the ring topology, PSO...

the axes of the search-space using exponentially decreasing step sizes. Stochastic optimization Matyas, J. (1965). "Random optimization". Automation and...