Sequential Group Processing for Multi-Core Optimization Trials
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Solution Overview
Problem
Existing optimization problems are challenging due to the complexity of adjusting multiple input variables and the difficulty in confirming whether a solution represents the optimal solution, often requiring significant computational resources and time, especially as the number of variables increases.
Innovation Solution
A method involving a multi-core computer processor architecture that divides optimization problem variables into groups, performs parallel computational trials on each group, and updates local field values sequentially, reducing computational load and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of variables in optimization problems increases, then the complexity of the problem increases, but the computational resources and time required increase significantly
Solution Approach 1:
The patent divides the set of variables into multiple groups, where each group is processed by a separate computing core. This segmentation allows the optimization problem to be distributed across multiple processors, reducing the computational burden on any single core and enabling the system to handle larger-scale optimization problems with more variables.
Solution Approach 2:
The patent introduces a parallel processing dimension by utilizing multi-core computer processors. Instead of processing variables sequentially on a single core, the system processes multiple groups of variables simultaneously across multiple cores, adding a temporal and spatial dimension to the computation that significantly reduces overall processing time and resource requirements.
2Productivity
If traditional sequential processing methods are used, then implementation is simple, but processing time and computational load increase
Solution Approach 1:
By dividing variables into groups assigned to different computing cores, the patent enables parallel processing of optimization trials. Multiple groups are processed simultaneously rather than sequentially, dramatically improving productivity and reducing the time required to converge on optimal solutions.
Solution Approach 2:
The patent performs preliminary division of variables into groups and assigns them to specific computing cores before the optimization process begins. This preliminary organization enables immediate parallel processing without coordination overhead during the optimization trials, maximizing processing efficiency from the start.
3Measurement precision
If multiple input variables are adjusted to find optimal solutions, then solution accuracy improves, but the difficulty of confirming optimality and computational complexity increase
Solution Approach 1:
The patent segments the computational work across multiple cores, with each core independently processing a group of variables. This segmentation maintains solution accuracy by thoroughly exploring the variable space while distributing computational complexity, making it feasible to adjust and evaluate multiple input variables without overwhelming any single processing unit.
Data Source
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AI summary
A method may include obtaining variables that represent characteristics related to an optimization problem and weights that correspond to the variables. The variables may be divided into groups that each include a sub-set of the variables. The method may include obtaining a group local field matrix for each group of variables. Each local field matrix may include local field values that indicate interactions between a respective variable and the other variables as influenced by their respective weights. The method may include performing a semi-sequential trial process, which may be a stochastic process that includes performing trials with respect to the variables in which each trial determines whether to change a state of a variable. The semi-sequential trial process may include updating all of the group local field matrices based on the stochastic process results, and a solution to the optimization problem may be determined based on the results.