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

{\displaystyle p} minimises the supremum risk. Robust optimization is an approach to solve optimization problems under uncertainty in the knowledge of...

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

Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently...

Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute...

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...

which study infinite-dimensional optimization problems are calculus of variations, optimal control and shape optimization. Semi-infinite programming David...

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

in continuous optimization and is best known for his work on the ellipsoid method, modern interior-point methods and robust optimization. Nemirovski earned...

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...

Info-gap decision theory seeks to optimize robustness to failure under severe uncertainty, in particular applying sensitivity analysis of the stability...

problem being optimized, which means DE does not require the optimization problem to be differentiable, as is required by classic optimization methods such...

of optimization methods that sample from a hypersphere surrounding the current position. Random optimization is a related family of optimization methods...

convex optimization problems in parallel based on exchange of information among controllers. MPC is based on iterative, finite-horizon optimization of a...

problems with chance constraints, integrated chance constraints and robust optimization problems. It can generate the deterministic equivalent version of...

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

portfolio optimization Copula based methods Principal component-based methods Deterministic global optimization Genetic algorithm Portfolio optimization is usually...

forward optimization is a method used in finance to determine the optimal parameters for a trading strategy and to determine the robustness of the strategy...

outcome. It is one of the most important models in robust decision making in general and robust optimization in particular. It is also known by a variety of...

Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. For large numbers of local optima, SA...

In the field of mathematical optimization, stochastic programming is a framework for modeling optimization problems that involve uncertainty. A stochastic...

Minimization". Low-rank Matrix Optimization Symposium, SIAM Conference on Optimization. G. Tang; A. Nehorai (2011). "Robust principal component analysis...

cases, online optimization can be used, which is different from other approaches such as robust optimization, stochastic optimization and Markov decision...

as probabilistic, possibilistic, information gap decision theory, robust optimization, and interval analysis. An alternating current power-flow model is...

s_{m}\leftrightarrow m} ) are given before the optimization, for example, using feature matching techniques, then the optimization only needs to estimate the transformation...

encompass many areas of computer science, such as robust programming, robust machine learning, and Robust Security Network. Formal techniques, such as fuzz...

uncertainty Robust decision-making, an iterative decision analytics framework Robust optimization, a field of mathematical optimization theory Robust statistics...

useful to evaluate characteristics of optimization algorithms, such as convergence rate, precision, robustness and general performance. Here some test...

search (RS) is a family of numerical optimization methods that do not require the gradient of the optimization problem, and RS can hence be used on functions...

bilateral contracts): IGDT: Information Gap Decision Theory RO: Robust optimization CVaR: Conditional value at risk FSD: First-order Stochastic Dominance...