Risk-averse stochastic programming and distributionally robust optimization via operator splitting
From MaRDI portal
Publication:2070402
DOI10.1007/s11228-021-00600-5zbMath1484.90053OpenAlexW3198838225MaRDI QIDQ2070402
Publication date: 24 January 2022
Published in: Set-Valued and Variational Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11228-021-00600-5
ADMMsplitting methodsprogressive hedgingdistributionally robust optimizationmultistage stochastic programs
Convex programming (90C25) Stochastic programming (90C15) Robustness in mathematical programming (90C17)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- A doubly stabilized bundle method for nonsmooth convex optimization
- Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs
- Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems
- Randomized progressive hedging methods for multi-stage stochastic programming
- On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators
- Solving stochastic programming problems with risk measures by progressive hedging
- Progressive decoupling of linkages in optimization and variational inequalities with elicitable convexity or monotonicity
- Relative-error inertial-relaxed inexact versions of Douglas-Rachford and ADMM splitting algorithms
- ARock: An Algorithmic Framework for Asynchronous Parallel Coordinate Updates
- Multistage Stochastic Optimization
- A Distance For Multistage Stochastic Optimization Models
- A survey on operator splitting and decomposition of convex programs
- Explicit Convergence Rate of a Distributed Alternating Direction Method of Multipliers
- On the Numerical Solution of Heat Conduction Problems in Two and Three Space Variables
- Scenarios and Policy Aggregation in Optimization Under Uncertainty
- Lectures on Stochastic Programming
- Splitting Algorithms for the Sum of Two Nonlinear Operators
- Combining Progressive Hedging with a Frank--Wolfe Method to Compute Lagrangian Dual Bounds in Stochastic Mixed-Integer Programming
- Minimax analysis of stochastic problems
- Anderson Accelerated Douglas--Rachford Splitting
- Convex analysis and monotone operator theory in Hilbert spaces
- Numerical optimization. Theoretical and practical aspects. Transl. from the French
