18642271. Embedding Constrained and Unconstrained Optimization Programs as Neural Network Layers simplified abstract (Capital One Services, LLC)
Contents
Embedding Constrained and Unconstrained Optimization Programs as Neural Network Layers
Organization Name
Inventor(s)
Tarek Aziz Lahlou of McLean VA (US)
Christopher Larson of Washington DC (US)
Oluwatobi Olabiyi of Arlington VA (US)
Embedding Constrained and Unconstrained Optimization Programs as Neural Network Layers - A simplified explanation of the abstract
This abstract first appeared for US patent application 18642271 titled 'Embedding Constrained and Unconstrained Optimization Programs as Neural Network Layers
Simplified Explanation: This patent application discusses methods for embedding optimization programs as layers in a neural network architecture, allowing the network to learn and solve optimization problems.
Key Features and Innovation:
- Embedding constrained and unconstrained optimization programs in a neural network architecture.
- Transforming common optimization problems into forms suitable for neural network use.
- Solving optimization layers using gradient descent techniques via backpropagation.
- Allowing the neural network to learn the solution to optimization problems.
Potential Applications: This technology could be applied in various fields such as finance, engineering, logistics, and healthcare for optimizing complex systems and processes.
Problems Solved: This technology addresses the need for external software to pre-solve optimization programs before using them in a neural network architecture.
Benefits:
- Simplifies the process of solving optimization problems.
- Enables the neural network to learn and solve complex optimization tasks.
- Reduces the reliance on external software for pre-solving optimization programs.
Commercial Applications: Optimizing supply chain logistics, financial portfolio management, resource allocation in healthcare, and other industries could benefit from this technology, improving efficiency and decision-making processes.
Prior Art: Readers can explore prior research on optimization programs, neural network architectures, and gradient descent techniques to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in neural network optimization techniques, deep learning algorithms, and applications of artificial intelligence in optimization problems.
Questions about Optimization Layers in Neural Networks: 1. How does embedding optimization programs as layers in a neural network architecture improve efficiency in solving complex problems? 2. What are the potential limitations or challenges of using neural networks to solve optimization tasks?
Original Abstract Submitted
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.
