Robust Optimization

Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of r...

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Hlavní autori:	Ben-Tal, Aharon, El Ghaoui, Laurent, Nemirovski, Arkadi
Médium:	E-kniha Kniha
Jazyk:	English
Vydavateľské údaje:	Princeton, N.J Princeton University Press 2009
Vydanie:	1
Edícia:	Princeton Series in Applied Mathematics
Predmet:	Accuracy and precision Additive model Almost surely Approximation Approximation algorithm Best Big O notation Candidate solution Central limit theorem Chaos theory Coefficient Computational complexity theory Convex optimization Curse of dimensionality Decision problem Decision rule Degeneracy (mathematics) Diagram (category theory) Duality (optimization) Dynamic programming Feasible region For All Practical Purposes General Topics for Engineers Identity matrix Inequality (mathematics) Infimum and supremum Law of large numbers Linear dynamical system Linear inequality Linear map Linear matrix inequality Linear programming Loss function Markov chain Mathematical optimization MATHEMATICS MATHEMATICS / Applied MATHEMATICS / Linear & Nonlinear Programming Mathematische Optimierung Maxima and minima Norm (mathematics) Normal distribution NP-hardness Optimal control Optimization problem Pairwise Parameter Parametric family Probability Probability distribution Quantity Relative interior Robust control Robust decision-making Robust optimization Robustes Verfahren Sensitivity analysis Simple set Singular value Slack variable Special case Spherical model Spline (mathematics) Stochastic Stochastic optimization Stochastic programming Strong duality Theorem Theorie Time complexity Uncertainty Upper and lower bounds Variable (mathematics) Virtual displacement Weak duality With high probability worst and average case Parametric family Identity matrix Inequality (mathematics) Stochastic Approximation algorithm Additive model Duality (optimization) Loss function Convex optimization Chaos theory Degeneracy (mathematics) Slack variable Best Accuracy and precision For All Practical Purposes 0O Strong duality With high probability Approximation Sensitivity analysis Decision problem Spherical model Almost surely Robust decision-making Decision rule Stochastic programming Robust control Curse of dimensionality Infimum and supremum Robust optimization Feasible region Big O notation Normal distribution Special case Stochastic optimization Mathematical optimization Time complexity Uncertainty Singular value Diagram (category theory) Maxima and minima Linear inequality Probability distribution Upper and lower bounds Spline (mathematics) Virtual displacement Markov chain Linear dynamical system Linear map Computational complexity theory Law of large numbers Linear matrix inequality Relative interior Theorem Dynamic programming Pairwise Coefficient Weak duality Probability worst and average case Linear programming Quantity Candidate solution Variable (mathematics) NP-hardness Simple set Central limit theorem Norm (mathematics) Optimal control Parameter Optimization problem
ISBN:	9780691143682, 0691143684, 9781400831050, 1400831059
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