Statistical performance of subgradient step-size update rules in Lagrangian relaxations of chance-constrained optimization models
From MaRDI portal
Publication:6588762
DOI10.1007/978-3-031-47859-8_26MaRDI QIDQ6588762
Bismark Singh, Charlotte Ritter
Publication date: 16 August 2024
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Decomposition algorithms for optimizing multi-server appointment scheduling with chance constraints
- A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support
- Nonanticipative duality, relaxations, and formulations for chance-constrained stochastic programs
- Conditional subgradient optimization -- theory and applications
- Relaxations and approximations of chance constraints under finite distributions
- An adaptive model with joint chance constraints for a hybrid wind-conventional generator system
- Lagrangian relaxation based heuristics for a chance-constrained optimization model of a hybrid solar-battery storage system
- Approximating two-stage chance-constrained programs with classical probability bounds
- On mixing sets arising in chance-constrained programming
- Chance-Constrained Programming
- Scalable Heuristics for a Class of Chance-Constrained Stochastic Programs
- Scenarios and Policy Aggregation in Optimization Under Uncertainty
- Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods
- A generalization of Polyak's convergence result for subgradient optimization
- Lagrangian Solution Techniques and Bounds for Loosely Coupled Mixed-Integer Stochastic Programs
- Validation of subgradient optimization
- Convergence Analysis of Deflected Conditional Approximate Subgradient Methods
- Data Mining and Knowledge Discovery Handbook
This page was built for publication: Statistical performance of subgradient step-size update rules in Lagrangian relaxations of chance-constrained optimization models
