Stochastic control or stochastic optimal control is a sub field of control theory that deals with the existence of uncertainty either in observations or...
12 KB (1,683 words) - 09:57, 3 March 2023
various applications such as stochastic control, mathematical finance, and nonlinear Feynman-Kac formulae. Backward stochastic differential equations were...
5 KB (613 words) - 04:59, 19 July 2024
Hamilton–Jacobi–Bellman equation (category Stochastic control)
Optimal Control. Athena Scientific. Pham, Huyên (2009). "The Classical PDE Approach to Dynamic Programming". Continuous-time Stochastic Control and Optimization...
14 KB (2,050 words) - 17:50, 26 April 2024
In probability theory and related fields, a stochastic (/stəˈkæstɪk/) or random process is a mathematical object usually defined as a sequence of random...
162 KB (17,919 words) - 13:01, 18 August 2024
Stochastic (/stəˈkæstɪk/; from Ancient Greek στόχος (stókhos) 'aim, guess') is the property of being well-described by a random probability distribution...
28 KB (3,333 words) - 11:59, 18 August 2024
one of the fundamental principles of stochastic control theory, which states that the problems of optimal control and state estimation can be decoupled...
25 KB (4,925 words) - 05:30, 28 March 2023
Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...
50 KB (6,585 words) - 03:30, 12 August 2024
In control theory, robust control is an approach to controller design that explicitly deals with uncertainty. Robust control methods are designed to function...
8 KB (1,024 words) - 04:51, 31 July 2024
small modeling errors. Stochastic control deals with control design with uncertainty in the model. In typical stochastic control problems, it is assumed...
45 KB (6,461 words) - 20:40, 7 June 2024
Stationary process Stochastic calculus Itô calculus Malliavin calculus Semimartingale Stratonovich integral Stochastic control Stochastic differential equation...
5 KB (407 words) - 21:21, 25 August 2023
separation principle is a special case of the separation principle of stochastic control which states that even when the process and output noise sources are...
16 KB (2,791 words) - 14:15, 8 July 2024
Merton's portfolio problem (category Stochastic control)
solved by Davis and Norman in 1990. It is one of the few cases of stochastic singular control where the solution is known. For a graphical representation,...
11 KB (1,531 words) - 19:51, 24 August 2024
Stochastic optimization (SO) are optimization methods that generate and use random variables. For stochastic optimization problems, the objective functions...
12 KB (1,068 words) - 01:33, 5 August 2024
Markov decision process (category Stochastic control)
Markov decision process (MDP), also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes...
35 KB (5,214 words) - 11:52, 24 August 2024
approaches used in stochastic control theory. Regrettably the simple adaptation of the deterministic schemes for matching up to stochastic models such as...
2 KB (225 words) - 13:20, 20 June 2017
Separation principle (category Stochastic control)
controller designed to minimize a quadratic cost, is optimal for the stochastic control problem with output measurements. When process and observation noise...
5 KB (687 words) - 12:26, 25 July 2023
Stochastic control Total quality management Barlow & Irony 1992 Bergman 2009 Zabell 1992 Deming, W. Edwards (1952). Lectures on statistical control of...
19 KB (2,442 words) - 21:15, 5 July 2024
Sliding mode control SNOPT Stochastic control Trajectory optimization Ross, Isaac (2015). A primer on Pontryagin's principle in optimal control. San Francisco:...
32 KB (4,711 words) - 04:55, 19 August 2024
stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming...
23 KB (5,371 words) - 15:23, 21 March 2024
Loss function (redirect from Stochastic criterion function)
because it results in linear first-order conditions. In the context of stochastic control, the expected value of the quadratic form is used. The quadratic loss...
21 KB (2,796 words) - 18:32, 12 August 2024
Intelligent control Optimal control Dynamic programming Robust control Stochastic control System dynamics, system analysis Takens' theorem Exponential dichotomy...
5 KB (413 words) - 14:04, 5 April 2022
Partially observable Markov decision process (category Stochastic control)
Cassandra, A.R. (1998). "Planning and acting in partially observable stochastic domains". Artificial Intelligence. 101 (1–2): 99–134. doi:10.1016/S0004-3702(98)00023-X...
22 KB (3,309 words) - 00:39, 23 July 2024
Markov decision process Optional stopping theorem Prophet inequality Stochastic control Chow, Y.S.; Robbins, H.; Siegmund, D. (1971). Great Expectations:...
15 KB (2,545 words) - 09:15, 19 April 2024
In the theory of stochastic processes, filtering describes the problem of determining the state of a system from an incomplete and potentially noisy set...
13 KB (2,142 words) - 20:36, 4 July 2024
A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution...
36 KB (5,616 words) - 15:19, 12 July 2024
Stochastic resonance (SR) is a phenomenon in which a signal that is normally too weak to be detected by a sensor can be boosted by adding white noise to...
18 KB (2,150 words) - 05:05, 31 July 2024
solutions using concepts from several control areas such as robust control, optimal stochastic control, model predictive control, fuzzy logic etc. A most critical...
8 KB (970 words) - 04:20, 5 July 2023
populations. It lies at the intersection of game theory with stochastic analysis and control theory. The use of the term "mean field" is inspired by mean-field...
16 KB (2,417 words) - 22:09, 4 July 2024
Witsenhausen's counterexample (category Stochastic control)
figure below, is a deceptively simple toy problem in decentralized stochastic control. It was formulated by Hans Witsenhausen in 1968. It is a counterexample...
9 KB (1,325 words) - 07:58, 18 July 2024
In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number...
18 KB (2,726 words) - 09:29, 27 June 2024