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

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

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

generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from...

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

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

Random optimization (RO) is a family of numerical optimization methods that do not require the gradient of the optimization problem and RO can hence be...

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

neural networks, stochastic optimization, genetic algorithms, and genetic programming. A problem itself may be stochastic as well, as in planning under...

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

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

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

optimization models can be either deterministic—with every set of variable states uniquely determined by the parameters in the model – or stochastic—with...

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

autoencoders, and stochastic optimization. It allows for the efficient computation of gradients through random variables, enabling the optimization of parametric...

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

solving constrained optimization problems. They have similarities to penalty methods in that they replace a constrained optimization problem by a series...

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

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

possible. Local search is a sub-field of: Metaheuristics Stochastic optimization Optimization Fields within local search include: Hill climbing Simulated...

strategy for numerical optimization. Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization of non-linear or non-convex...

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

Kingma, Diederik P.; Ba, Jimmy (2017-01-29), Adam: A Method for Stochastic Optimization, arXiv:1412.6980 Xie, Zeke; Yuan, Li; Zhu, Zhanxing; Sugiyama,...

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...

Derivative-free optimization (sometimes referred to as blackbox optimization) is a discipline in mathematical optimization that does not use derivative...

Continuum-Armed Bandit Problem. SIAM J. of Control and Optimization. 1995. Besbes, O.; Gur, Y.; Zeevi, A. Stochastic multi-armed-bandit problem with non-stationary...

also published several books, including Online Stochastic Combinatorial Optimization, Hybrid Optimization, and Constraint-Based Local Search. Van Hentenryck...

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

approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance-constrained optimization problems based...

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