Loop Variable Optimization for Path Computation
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Solution Overview
Problem
Existing methods for computing optimal paths using loop variables face challenges in reducing computational load and time, especially when handling complex nonlinear functions and multiple paths.
Innovation Solution
A system and method that adjust the number of iterations to optimize loop variables and implement a post-process to change loop variables based on specific thresholds, thereby reducing computational load and time while computing multiple objective moving paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of iterations is increased to optimize loop variables for computing optimal paths, then the precision of the computed path is improved, but the computational load and time increase
Solution Approach 1:
The patent applies preliminary action by performing a post-processing step after the main iteration process. The post-processing adjusts loop variables based on threshold criteria without requiring additional full iterations, thereby improving path precision while avoiding the time cost of increasing iteration count.
Solution Approach 2:
The patent changes parameters by introducing threshold-based adjustment criteria for loop variables in the post-processing phase. Instead of increasing iteration count, it modifies loop variable values directly based on whether they exceed certain thresholds, achieving precision improvement through parameter adjustment rather than iterative refinement.
2Productivity
If multiple loop variables are optimized through multiple iterations to compute multiple optimal paths, then the quantity of computed paths is improved, but the computational load increases
Solution Approach 1:
The patent performs preliminary optimization of loop variables through a single iteration process, then uses post-processing to generate multiple optimal paths by adjusting loop variables based on thresholds. This approach produces multiple paths without requiring multiple full iterations, reducing computational energy consumption.
Solution Approach 2:
The patent creates multiple optimal paths by copying and adjusting the base solution obtained from the iteration process. The post-processing generates variant paths by modifying loop variables according to threshold criteria, producing multiple solutions from a single computational foundation rather than running multiple independent optimizations.
Data Source
AI summary
A system for computing an optimal path includes an arithmetic unit. The arithmetic unit is configured to compute an objective moving path from one of a plurality of nodes to another one of the plurality of nodes. Each of a plurality of link variables corresponds to a path connecting any two adjacent nodes of the plurality of nodes. Each of the plurality of link variables has a value and a direction. Each of a plurality of loop variables corresponds to a closed path connecting at least any three nodes of the plurality of nodes. Each of the plurality of loop variables has a value and a direction. A reference path variable corresponds to any moving path and has a value and a direction. The arithmetic unit computes the plurality of link variables based on the reference path variable and the plurality of loop variables. The arithmetic unit uses the plurality link variables to compute a plurality of objective function values corresponding, respectively, to a plurality of moving paths. The arithmetic unit computes a moving path corresponding to a minimum objective function value, which is the smallest value among the plurality of objective function values, as the objective moving path.