Fujitsu Patent - Discrete Optimization Using Continuous Relaxation and Machine Learning
Summary
USPTO published patent application US20260099565A1 assigned to Fujitsu Limited. The application covers a non-transitory computer-readable medium and calculation method for discrete optimization using continuous relaxation with machine learning. The invention involves applying perturbations to discrete optimization problems and training a machine learning model to output solutions.
What changed
USPTO published Fujitsu Limited's patent application US20260099565A1 covering a computer-readable medium, calculation method, and information processing device for discrete optimization problems. The invention involves relaxing discrete variables into a continuous matrix where each element represents a solution to multiple discrete optimization problems, then training a machine learning model by applying perturbations to those problems to output solutions.
Technology companies and software developers working on optimization algorithms, combinatorial solvers, or machine learning applications may need to review this patent to assess potential freedom-to-operate implications or licensing considerations for similar approaches.
Archived snapshot
Apr 17, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
NON-TRANSITORY COMPUTER-READABLE MEDIUM, CALCULATION METHOD, AND INFORMATION PROCESSING DEVICE
Application US20260099565A1 Kind: A1 Apr 09, 2026
Assignee
Fujitsu Limited
Inventors
Yuma ICHIKAWA
Abstract
There is provided a non-transitory computer-readable medium storing a calculation program for causing a computer to execute a process. The process includes, in a cost function in a search process that performs a search by incorporating continuous relaxation into a discrete optimization problem, in which each element of a matrix obtained by relaxing discrete variables to be optimized into a continuous matrix is a solution of a plurality of discrete optimization problems, outputting a solution of a discrete optimization problem using solutions of the plurality of discrete optimization problems obtained by training a machine learning model by applying a perturbation to the plurality of discrete optimization problems.
CPC Classifications
G06F 17/11 G06N 20/00
Filing Date
2025-10-03
Application No.
19348828
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