This article is about iterative methods. For the modeling (and optimization) of decisions under uncertainty, see stochastic programming. For the context of control theory, see stochastic control.
Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints. Stochastic optimization methods also include methods with random iterates. Some stochastic optimization methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization.[1]
Stochastic optimization methods generalize deterministic methods for deterministic problems.
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Spall, J. C. (2003). Introduction to Stochastic Search and Optimization. Wiley. ISBN 978-0-471-33052-3.
and 27 Related for: Stochastic optimization information
Stochasticoptimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear...
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generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from...
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by using another overlaying optimizer, a concept known as meta-optimization, or even fine-tuned during the optimization, e.g., by means of fuzzy logic...
neural networks, stochastic optimization, genetic algorithms, and genetic programming. A problem itself may be stochastic as well, as in planning under...
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form of stochasticoptimization, so that the solution found is dependent on the set of random variables generated. In combinatorial optimization, by searching...
is an iterative optimization algorithm which uses minibatching to create a stochastic gradient estimator, as used in SGD to optimize a differentiable...
solving constrained optimization problems. They have similarities to penalty methods in that they replace a constrained optimization problem by a series...
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optimization models can be either deterministic—with every set of variable states uniquely determined by the parameters in the model – or stochastic—with...
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a special case of stochasticoptimization, a well known problem in optimization. In practice, one can perform multiple stochastic gradient passes (also...
algorithm. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, simulation optimization, and atmospheric...
13 (4), pp 387-401. Perez, Meir and Marwala, Tshilidzi (2008) StochasticOptimization Approaches for Solving Sudoku arXiv:0805.0697. Lewis, R. A Guide...
Apolloni, Bruno; Carvalho, Maria C.; De Falco, Diego (1989). "Quantum stochasticoptimization". Stoc. Proc. Appl. 33 (2): 233–244. doi:10.1016/0304-4149(89)90040-9...
stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming...
programming equation associated with discrete-time optimization problems. In continuous-time optimization problems, the analogous equation is a partial differential...
deterministic and stochastic global optimization methods A. Neumaier’s page on Global Optimization Introduction to global optimization by L. Liberti Free...
value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population...
In probability theory and related fields, a stochastic (/stəˈkæstɪk/) or random process is a mathematical object usually defined as a sequence of random...
differentiable reinforcement learning called non-stochastic control, which applies online convex optimization to control. 2002–2006 - Gordon Wu fellowship...
Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the problem to be optimized and RO can hence...