Everything about Stochastic programming
Framework for modeling optimization problems that involve uncertainty
In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic program is an optimization problem in which some or all problem parameters are uncertain, but follow known probability distributions. This framework contrasts with deterministic optimization, in which all problem parameters are assumed to be known exactly. The goal of stochastic programming is to find a decision which both optimizes some criteria chosen by the decision maker, and appropriately accounts for the uncertainty of the problem parameters. Because many real-world decisions involve uncertainty, stochastic programming has found applications in a broad range of areas ranging from finance to transportation to energy optimization. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Stochastic programmingA 5-minute orientation
- Skim a recent paper: Continuous-Time Stochastic Policy Iteration of Adaptive Dynamic ProgrammingInfluential work since 2022
- Pick up a classic book: Markov Decision ProcessesThe most-reprinted book on the subject
- Read the landmark paper: Robust Stochastic Approximation Approach to Stochastic ProgrammingCited 1,209 times
- Go sideways: Scenario optimizationA closely related topic
- Go sideways: Dynamic discrete choiceA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Robust Stochastic Approximation Approach to Stochastic Programming
Scenario reduction in stochastic programming
A Stochastic Programming Model
Stochastic dual dynamic integer programming
Distributionally Robust Stochastic Programming
Multi-horizon stochastic programming
Stochastic programming
Discrete Stochastic Programming
🆕 Influential since 2022
Continuous-Time Stochastic Policy Iteration of Adaptive Dynamic Programming
Stochastic Dual Dynamic Programming and Its Variants: A Review
Distributionally Robust Two-Stage Stochastic Programming
DeepStochLog: Neural Stochastic Logic Programming
Stochastic dual dynamic programming for multistage stochastic mixed-integer nonlinear optimization
📚 Books worth reading
Markov Decision ProcessesMartin L. Puterman · 1994
Stochastic models in operations researchDaniel P. Heyman, Matthew J. Sobel · 1982- 📕Stochastic simulation and applications in finance with MATLAB programsHuu Tue Huynh · 2008
- 📕Time and LogicLeonard Bolc, Andrzej Szałas · 2019
Lectures on Stochastic ProgrammingAlexander Shapiro, Darinka Dentcheva · 2009
Stochastic programmingGAMM/IFIP-Workshop on "Stochastic Optimization: Numerical Methods and Technical Applications" (2nd 1993 Hochschule der Bundeswehr München), Kurt Marti · 1995- 📕Markov processes and control theoryH. Langer, Volker Nollau · 1989
Introduction to Stochastic Dynamic ProgrammingSheldon M. Ross · 1983
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Scenario optimization The scenario approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance-constrained optimization…
- Dynamic discrete choice Dynamic discrete choice (DDC) models, also known as discrete choice models of dynamic programming, model an agent's choices over discrete options that…
- Wiener process In mathematics, the Wiener process (or Brownian motion, due to its historical connection with the physical process of the same name) is a real-valued continuous…
- Robust optimization Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought…
- Stochastic optimization Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions…
- Markov decision process In decision theory, a Markov decision process (MDP) is a mathematical model for sequential decision making when outcomes are uncertain. It is a type of…
- Drift plus penalty In the mathematical theory of probability, the drift-plus-penalty method is used for optimization of queueing networks and other stochastic systems. The…
- Stochastic approximation Stochastic approximation methods are a family of iterative methods typically used for root-finding problems or for optimization problems. The recursive…