Capital one services, llc (20240346332). Embedding Constrained and Unconstrained Optimization Programs as Neural Network Layers simplified abstract
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 20240346332 titled 'Embedding Constrained and Unconstrained Optimization Programs as Neural Network Layers
Simplified Explanation: The patent application discusses methods for embedding optimization programs as layers in a neural network architecture, allowing the network to solve optimization problems directly.
Key Features and Innovation:
- Embedding constrained and unconstrained optimization programs in a neural network architecture.
- Solving optimization problems without the need for external software.
- Transforming optimization problems into forms suitable for neural network use.
- Invertible transformation allowing the system to learn solutions using gradient descent techniques.
- Solving optimization layers through the neural network itself.
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 eliminates the need for external software to pre-solve optimization problems before using them in a neural network, streamlining the optimization process.
Benefits:
- Improved efficiency in solving optimization problems.
- Simplified integration of optimization programs into neural network architectures.
- Enhanced accuracy in finding solutions to complex optimization problems.
Commercial Applications: Optimizing supply chain logistics, financial portfolio management, resource allocation in healthcare, and process optimization in manufacturing are potential commercial applications of this technology.
Prior Art: Prior research may include studies on optimization techniques in neural networks and the integration of different problem-solving methods in machine learning systems.
Frequently Updated Research: Stay updated on advancements in neural network optimization techniques, gradient descent algorithms, and applications of machine learning in optimization problems.
Questions about Optimization 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 challenges in implementing optimization layers in neural networks, and how can they be addressed?
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.
