Optimization Platform Sub-Region Selection for Convergence
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Solution Overview
Problem
Target value optimization problems face challenges due to non-convergence issues, especially with increased granularity and complexity in objective functions and constraint spaces, making it difficult to determine unique optimal points and compromising solution accuracy.
Innovation Solution
An optimization platform that identifies an optimal region in the optimization space by dividing it into sub-regions based on constraints and input data, selecting the sub-region with the shortest distance from the target value, and using a vector from this region as an initial parameter to execute the optimization problem, thereby improving convergence and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the optimization space is divided into finer sub-regions to improve solution accuracy, then the precision of finding the optimal point improves, but the computational complexity and time required increase significantly
Solution Approach 1:
The optimization space is divided into multiple sub-regions, and optimization is performed independently in each sub-region. This segmentation allows the system to achieve high solution accuracy by focusing on local optima while managing computational complexity through distributed processing of smaller sub-problems rather than attempting to solve the entire optimization space as a single complex problem.
Solution Approach 2:
The system performs preliminary actions by identifying and dividing the optimization space into sub-regions before executing the full optimization process. This preliminary segmentation prepares the problem structure in advance, enabling more efficient convergence to optimal solutions by reducing the search space for each optimization iteration.
2Reliability
If traditional optimization methods are used to ensure thorough exploration of the optimization space, then solution reliability improves, but the convergence time increases and real-time optimization becomes difficult
Solution Approach 1:
By segmenting the optimization space into multiple sub-regions, the system can perform parallel optimization across different regions simultaneously. This maintains solution reliability by thoroughly exploring the entire space while significantly reducing convergence time through parallel processing, enabling real-time or near-real-time optimization capabilities.
Solution Approach 2:
The system performs optimization on a subset of sub-regions in each iteration rather than exhaustively processing the entire optimization space. This partial action approach maintains solution reliability by progressively converging to the optimal point while reducing the time required for each optimization cycle, enabling real-time application.
3Manufacturing precision
If the optimization problem is solved with high granularity to improve accuracy, then the precision of the optimal path determination improves, but the resources consumed increase
Solution Approach 1:
The optimization space is segmented into multiple sub-regions, allowing high-precision optimization to be performed locally in each region with reduced computational resources. This segmentation enables the system to achieve high optimal path precision by focusing computational effort on specific sub-regions rather than uniformly processing the entire space, thereby conserving computational resources.
Solution Approach 2:
The system applies different optimization strategies and resource allocations to different sub-regions based on their local characteristics. This local quality approach allows high computational precision to be applied where needed while using fewer resources in other regions, optimizing the trade-off between optimal path precision and computational resource consumption.
Data Source
AI summary
A device receives an optimization problem and a target value and input data for the optimization problem, and specifies constraints for the optimization problem based on the input data. The device identifies an optimization space for the optimization problem based on the constraints and the input data, and divides the optimization space into sub-regions based on the constraints and the input data. The device performs optimizations of a set of the sub-regions, and determines a respective distance of each sub-region, of the set of the sub-regions, from the target value. The device selects a particular sub-region that is a shortest distance from the target value, and selects a vector from the particular sub-region. The device executes the optimization problem using the vector as an initial parameter and to generate results, and utilizes the results to recommend one or more decisions or modify a process.