Embedding constrained and unconstrained optimization programs as neural network layers
US12001961B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 12, 2022 |
| Grant date | Jun 4, 2024 |
| Priority date | — |
| Expiry date | Dec 12, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N5/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.