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Stochastic optimization information


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.

  1. ^ Spall, J. C. (2003). Introduction to Stochastic Search and Optimization. Wiley. ISBN 978-0-471-33052-3.

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Stochastic optimization

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Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear...

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or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated...

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

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

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Bayesian optimization

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Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms. It is usually...

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Robust optimization

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Robust optimization is a field of mathematical optimization theory that deals with optimization problems in which a certain measure of robustness is sought...

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Particle swarm optimization

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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...

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Stochastic

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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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Hyperparameter optimization

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hyperparameter optimization methods. Bayesian optimization is a global optimization method for noisy black-box functions. Applied to hyperparameter optimization, Bayesian...

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Metaheuristic

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form of stochastic optimization, so that the solution found is dependent on the set of random variables generated. In combinatorial optimization, by searching...

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Stochastic gradient Langevin dynamics

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is an iterative optimization algorithm which uses minibatching to create a stochastic gradient estimator, as used in SGD to optimize a differentiable...

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Augmented Lagrangian method

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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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Evolutionary computation

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population-based trial and error problem solvers with a metaheuristic or stochastic optimization character. In evolutionary computation, an initial set of candidate...

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Inventory optimization

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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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Stochastic tunneling

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In numerical analysis, stochastic tunneling (STUN) is an approach to global optimization based on the Monte Carlo method-sampling of the function to be...

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Online machine learning

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a special case of stochastic optimization, a well known problem in optimization. In practice, one can perform multiple stochastic gradient passes (also...

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algorithm. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, simulation optimization, and atmospheric...

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Sudoku solving algorithms

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13 (4), pp 387-401. Perez, Meir and Marwala, Tshilidzi (2008) Stochastic Optimization Approaches for Solving Sudoku arXiv:0805.0697. Lewis, R. A Guide...

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Quantum annealing

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Apolloni, Bruno; Carvalho, Maria C.; De Falco, Diego (1989). "Quantum stochastic optimization". Stoc. Proc. Appl. 33 (2): 233–244. doi:10.1016/0304-4149(89)90040-9...

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Stochastic dynamic programming

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stochastic dynamic programming is a technique for modelling and solving problems of decision making under uncertainty. Closely related to stochastic programming...

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Bellman equation

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programming equation associated with discrete-time optimization problems. In continuous-time optimization problems, the analogous equation is a partial differential...

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Global optimization

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deterministic and stochastic global optimization methods A. Neumaier’s page on Global Optimization Introduction to global optimization by L. Liberti Free...

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Genetic algorithm

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value of the objective function in the optimization problem being solved. The more fit individuals are stochastically selected from the current population...

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Stochastic process

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

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Elad Hazan

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differentiable reinforcement learning called non-stochastic control, which applies online convex optimization to control. 2002–2006 - Gordon Wu fellowship...

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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...

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