Conditioning of linear-quadratic two-stage stochastic optimization problems
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Publication:484135
DOI10.1007/s10107-013-0734-0zbMath1329.90092OpenAlexW1997733985MaRDI QIDQ484135
Konstantin Emich, René Henrion, Werner Römisch
Publication date: 18 December 2014
Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10107-013-0734-0
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Cites Work
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- On \(M\)-stationary points for a stochastic equilibrium problem under equilibrium constraints in electricity spot market modeling.
- The sharp Lipschitz constants for feasible and optimal solutions of a perturbed linear program
- Conditioning of convex piecewise linear stochastic programs
- Upper Lipschitz behavior of solutions to perturbed \(C^{1,1}\) programs
- Scenario reduction algorithms in stochastic programming
- Regularity and conditioning of solution mappings in variational analysis
- Some characterizations and properties of the ``distance to the ill-posedness and the condition measure of a conic linear system
- A Condition Number for Differentiable Convex Inequalities
- A Lagrangian finite generation technique for solving linear-quadratic problems in stochastic programming
- Some continuity properties of polyhedral multifunctions
- Perturbation analysis of optimization problems in banach spaces
- Ill-Conditioning and Computational Error in Interior Methods for Nonlinear Programming
- Complete Characterization of Openness, Metric Regularity, and Lipschitzian Properties of Multifunctions
- Variational Analysis
- Condition Numbers, the Barrier Method, and the Conjugate-Gradient Method
- Linear-Quadratic Programming and Optimal Control
- On the Calmness of a Class of Multifunctions
- Error Bounds for Piecewise Convex Quadratic Programs and Applications
- Second-Order Subdifferential Calculus with Applications to Tilt Stability in Optimization