Data-driven decision making in power systems with probabilistic guarantees: Theory and applications of chance-constrained optimization
Uncertainties from deepening penetration of renewable energy resources have posed
critical challenges to the secure and reliable operations of future electric grids. Among …
critical challenges to the secure and reliable operations of future electric grids. Among …
On safe tractable approximations of chance constraints
A Nemirovski - European Journal of Operational Research, 2012 - Elsevier
A natural way to handle optimization problem with data affected by stochastic uncertainty is
to pass to a chance constrained version of the problem, where candidate solutions should …
to pass to a chance constrained version of the problem, where candidate solutions should …
Robust optimization
Robust Optimization Page 1 Page 2 Robust Optimization Page 3 Princeton Series in Applied
Mathematics Series Editors: Ingrid Daubechies (Princeton University); Weinan E (Princeton …
Mathematics Series Editors: Ingrid Daubechies (Princeton University); Weinan E (Princeton …
[图书][B] Introduction to stochastic programming
JR Birge, F Louveaux - 2011 - books.google.com
The aim of stochastic programming is to find optimal decisions in problems which involve
uncertain data. This field is currently developing rapidly with contributions from many …
uncertain data. This field is currently developing rapidly with contributions from many …
Data-driven chance constrained stochastic program
In this paper, we study data-driven chance constrained stochastic programs, or more
specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a …
specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a …
Convex approximations of chance constrained programs
A Nemirovski, A Shapiro - SIAM Journal on Optimization, 2007 - SIAM
We consider a chance constrained problem, where one seeks to minimize a convex
objective over solutions satisfying, with a given close to one probability, a system of …
objective over solutions satisfying, with a given close to one probability, a system of …
[图书][B] Global optimization in action: continuous and Lipschitz optimization: algorithms, implementations and applications
JD Pintér - 1995 - books.google.com
In science, engineering and economics, decision problems are frequently modelled by
optimizing the value of a (primary) objective function under stated feasibility constraints. In …
optimizing the value of a (primary) objective function under stated feasibility constraints. In …
Selected topics in robust convex optimization
A Ben-Tal, A Nemirovski - Mathematical Programming, 2008 - Springer
Robust Optimization is a rapidly developing methodology for handling optimization
problems affected by non-stochastic “uncertain-but-bounded” data perturbations. In this …
problems affected by non-stochastic “uncertain-but-bounded” data perturbations. In this …
A robust optimization perspective on stochastic programming
In this paper, we introduce an approach for constructing uncertainty sets for robust
optimization using new deviation measures for random variables termed the forward and …
optimization using new deviation measures for random variables termed the forward and …
From CVaR to uncertainty set: Implications in joint chance-constrained optimization
We review and develop different tractable approximations to individual chance-constrained
problems in robust optimization on a variety of uncertainty sets and show their interesting …
problems in robust optimization on a variety of uncertainty sets and show their interesting …