Optimal parameter selection and acceleration in ADMM for multi-stage stochastic convex quadratic programs
US9760534B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 3, 2014 |
| Grant date | Sep 12, 2017 |
| Priority date | — |
| Expiry date | Dec 21, 2035 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F30/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method solves a stochastic quadratic program (StQP) for a convex set with a set of general linear equalities and inequalities by an alternating direction method of multipliers (ADMM). The method determines an optimal solution, or certifies that no solution exists. The method optimizes a step size β for the ADMM. The method is accelerated using a conjugate gradient (CG) method. The StMPC problem is decomposed into two blocks. The first block corresponds to an equality constrained QP, and the second block corresponds to a projection onto the StMPC inequalities and anticipativity constraints. The StMPC problem can be decomposed into a set of time step problems, and then iterated between the time step problems to solve the decoupled problems until convergence.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.