Inverse Temperature Optimization for Nonlinear Bayesian Updating

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Solution Overview

Problem

Existing techniques for nonlinear optimization problems face challenges in adjusting the inverse temperature effectively, leading to unsuitable effective sample sizes and subsequent issues in Bayesian updating.

Innovation Solution

An optimization apparatus and method that generate optimal variable candidates based on a belief distribution, evaluate an objective function, calculate an inverse temperature using an optimization technique to match target and effective sample sizes, calculate weights, and update the belief distribution accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If the inverse temperature is not adjusted according to the situation, then the optimization process is simple, but the effective sample size becomes unsuitable leading to problems in Bayesian updating

Engineering Contradiction:

Improveaccuracy of Bayesian updatingVSAvoidcomplexity of inverse temperature adjustment

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements feedback by calculating the effective sample size from the weights and using this information to adjust the inverse temperature. The inverse temperature optimization means calculates a new inverse temperature based on the current effective sample size and compares it with the target effective sample size, creating a closed-loop feedback system that automatically adapts the inverse temperature to maintain suitable effective sample size for accurate Bayesian updating.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent applies parameter changes by dynamically adjusting the inverse temperature parameter based on the effective sample size. The inverse temperature optimization means modifies the inverse temperature parameter in response to changes in the effective sample size, allowing the system to adapt to different situations and maintain optimal performance for Bayesian updating without requiring manual intervention.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If the inverse temperature is manually adjusted, then the effective sample size can be controlled, but the ease of operation decreases due to difficulty in knowing the suitable value

Engineering Contradiction:

Improvesuitability of effective sample sizeVSAvoidease of inverse temperature adjustment

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The patent implements self-service by enabling the system to automatically adjust the inverse temperature without requiring manual intervention. The inverse temperature optimization means autonomously calculates the appropriate inverse temperature based on the current effective sample size and target effective sample size, allowing the system to self-regulate and maintain suitable effective sample size for Bayesian updating while preserving ease of operation.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If the inverse temperature is optimized to match target and effective sample sizes, then the accuracy of optimization improves, but the calculation complexity increases

Engineering Contradiction:

Improveprecision of optimization resultVSAvoidcomplexity of optimization calculation

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent applies mechanics substitution by replacing manual or complex optimization calculations with an automated inverse temperature optimization means that uses mathematical relationships between effective sample size and inverse temperature. The system substitutes complex manual tuning with a systematic calculation approach that derives the appropriate inverse temperature from the effective sample size and target effective sample size, improving precision while managing calculation complexity through structured computation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250190035A1Optimization apparatus, optimization method, and storage medium

Publication Date: 2025.06.12 NEC CORP

AI summary

In order to attain the object of adjusting an inverse temperature used in a nonlinear optimization problem to a more suitable value, an optimization apparatus (100) includes: an optimal variable candidate generation section (101) that generates a plurality of optimal variable candidates, based on a belief distribution; an objective function evaluation section (102) that evaluates an objective function for each of the plurality of optimal variable candidates; an inverse temperature optimization section (103) that calculates, by using an optimization technique, an inverse temperature such that a target effective sample size which has been inputted and an effective sample size of a weight for the objective function are substantially equal to each other; a weight evaluation section (104) that calculates the weight for the objective function, based on the inverse temperature; and a belief distribution updating section (105) that updates the belief distribution, based on the weight, the belief distribution, and each of the optimal variable candidates.