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{{Short description|Ratio in Mathematical Optimization}} ...ence|doi=10.1137/1.9781611973075.88 |chapter=Correlation Robust Stochastic Optimization |title=Proceedings of the Twenty-First Annual ACM-SIAM Symposium on Discret ...

...at.uc.pt/~lnv/idfo/|accessdate=2014-01-18 |series= MPS-SIAM Book Series on Optimization| publisher=SIAM|location=Philadelphia}}</ref> ...mple the gradient gives the direction of steepest ascent. Derivative-based optimization is efficient at finding local optima for continuous-domain smooth single-mo ...

...ster]],<ref>{{Cite journal | doi = 10.1214/aoms/1177728976| title = On the Stochastic Matrices Associated with Certain Queuing Processes| journal = [[The Annals ...ete-time Markov chain on a countable state space <math>S</math> having a [[Stochastic matrix|transition probability matrix]] <math>P</math> with elements <math>p ...

'''Chance-constrained portfolio selection''' is an approach to [[portfolio optimization|portfolio selection]] under [[loss aversion]]. ...ilistic constrained optimization problems," Numerical Algebra, Control and Optimization, 2, No. 4, 767-778. [https://www.aimsciences.org/article/doi/10.3934/naco.2 ...

{{Short description|Optimization algorithm}} ...y suited to large-scale population models, adaptive modeling, simulation [[optimization]], and [[atmospheric model]]ing. Many examples are presented at the SPSA we ...

...ent''' is an [[Iterative algorithm|iterative]] [[Mathematical optimization|optimization]] [[algorithm]] for finding a [[local minimum]] of a [[differentiable funct ...di Nemirovsky and David Yudin. Problem Complexity and Method Efficiency in Optimization. John Wiley & Sons, 1983</ref> ...

...ic analysis by identifying variables which contribute most to a predefined optimization goal. This includes also the evaluation of robustness, i.e. the sensitivity ...ns, variance based sensitivity analysis quantifies the contribution of the optimization variables for a possible improvement of the model responses. In contrast to ...

'''Chance Constrained Programming (CCP)''' is a [[mathematical optimization]] approach used to handle problems under uncertainty. It was first introduc ...the use of [[probability]] and confidence levels to handle uncertainty in optimization problems. It distinguishes between single and joint chance constraints: ...

{{Short description|Optimization and sampling technique}} [[File:Non-Convex Objective Function.gif|thumb|SGLD can be applied to the optimization of non-convex objective functions, shown here to be a sum of Gaussians.]] ...

{{short description|Tuning parameter (hyperparameter) in optimization}} ...ed to as '''gain'''.<ref>{{cite journal |first=Bernard |last=Delyon |title=Stochastic Approximation with Decreasing Gain: Convergence and Asymptotic Theory |jour ...

...ult and expensive to evaluate. Usually, the underlying simulation model is stochastic, so that the objective function must be estimated using statistical estimat ...Energy'' 113 (2014): 1043–1058.</ref> or 'simulation-based multi-objective optimization' used when more than one objective is involved. ...

...ees on the number of required iterations, for several important classes of optimization problems. ...lon>0</math>). This is known as the ''oracle complexity'' of this class of optimization problems: Namely, the number of iterations such that on one hand, there is ...

{{Short description|Family of optimization algorithms}} ...methods that treat the objective as an infinite sum, as in the classical [[Stochastic approximation]] setting. ...

...er uncertainty, and on the other hand, [[Modern portfolio theory|portfolio optimization]] involving multiple investment choices having rate-of-return uncertainty.< ==Dynamic policy optimization== ...

.../ref> This result was developed within a research project about [[Bayesian optimization]] algorithms. ...tricted, depending on the characteristics of the problem, a specific local optimization method can be used. ...

...nd bPOE for common probability distributions with application to portfolio optimization and density estimation |journal=Annals of Operations Research |volume=299 | [[Category:Stochastic processes]] ...

...he Stochastic Rotation Problem: A Generalization of Faustmann's Formula to Stochastic Forest Growth |journal=Journal of Economic Dynamics and Control |volume=22 [[Category:Mathematical optimization]] ...

...or obtaining solutions to [[robust optimization]] and [[chance-constrained optimization]] problems based on a sample of the [[constraint (mathematics)|constraint]] ...ry justifies the use of [[randomization]] in robust and chance-constrained optimization. ...

...<ref name="Wirch">{{cite web|title=Distortion Risk Measures: Coherence and Stochastic Dominance|author=Julia L. Wirch|author2=Mary R. Hardy|url=http://pascal.ise # ''Monotone'' with respect to first order [[stochastic dominance]]. ...

.../><ref>{{cite arXiv |last1=Ross |first1=I. M. |title=Unscented Trajectory Optimization |date=2024-05-04 |eprint=2405.02753 |last2=Proulx |first2=R. J. |last3=Karp ...ochastic Differential Dynamic Programming for Robust Low-Thrust Trajectory Optimization Problems |conference=2018 AIAA Guidance, Navigation, and Control Conference ...