US Patent Application 18347386. Optimization of Parameters of a System, Product, or Process simplified abstract
Contents
Optimization of Parameters of a System, Product, or Process
Organization Name
Inventor(s)
Daniel Reuben Golovin of Pittsburgh PA (US)
Benjamin Solnik of Pittsburgh PA (US)
Subhodeep Moitra of Pittsburgh PA (US)
David W. Sculley, Ii of Cambridge MA (US)
Optimization of Parameters of a System, Product, or Process - A simplified explanation of the abstract
This abstract first appeared for US patent application 18347386 titled 'Optimization of Parameters of a System, Product, or Process
Simplified Explanation
- The patent application is about computing systems and methods for optimizing adjustable parameters of a system. - The system uses black-box optimization techniques to suggest new parameter values for evaluation. - The iterative suggestion and evaluation process aims to improve the overall performance of the system. - An objective function is used to evaluate the performance of the system based on one or more metrics. - The patent application introduces a new black-box optimization technique called "Gradientless Descent" that is faster and more clever than random search. - Gradientless Descent retains most of the favorable qualities of random search.
Original Abstract Submitted
The present disclosure provides computing systems and associated methods for optimizing one or more adjustable parameters (e.g. operating parameters) of a system. In particular, the present disclosure provides a parameter optimization system that can perform one or more black-box optimization techniques to iteratively suggest new sets of parameter values for evaluation. The iterative suggestion and evaluation process can serve to optimize or otherwise improve the overall performance of the system, as evaluated by an objective function that evaluates one or more metrics. The present disclosure also provides a novel black-box optimization technique known as “Gradientless Descent” that is more clever and faster than random search yet retains most of random search's favorable qualities.
