Calculation Device Vector Update Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing calculation devices face challenges in efficiently solving optimization problems, particularly the Ising problem, with multiple inequality constraints, as they often require complex calculations and large memory capacity, and struggle with non-convex objective functions and large-scale optimizations.
Innovation Solution
A calculation device is designed with a processing procedure that updates three vectors repeatedly, using a processor and memory to perform updates of the first, second, and third vectors, allowing for efficient optimization by updating the first vector using the second and third vectors, and handling inequality constraints without requiring the calculation of Hessian matrices or inverse matrices, enabling parallel and pipeline calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing calculation devices are used to solve optimization problems with multiple inequality constraints, then the problems can be solved, but the calculation complexity increases and memory capacity requirements become large
Solution Approach 1:
The patent segments the optimization problem into three distinct vector updates (first vector, second vector, third vector), each handling specific aspects of the constraint satisfaction. This segmentation allows the complex optimization problem with multiple inequality constraints to be broken down into manageable computational steps, reducing overall calculation complexity while maintaining reliability
Solution Approach 2:
The patent transforms the optimization problem by changing parameters from traditional Hessian matrix calculations to a three-vector update system. This parameter transformation eliminates the need for complex second-order derivative calculations, significantly reducing computational complexity while preserving the ability to solve non-convex optimization problems
2Measurement precision
If traditional methods are used for optimization with inequality constraints, then accurate solutions can be obtained, but large memory capacity is required
Solution Approach 1:
The patent extracts and eliminates the need for Hessian matrix storage and inversion operations from the optimization process. By removing these memory-intensive components and replacing them with the three-vector update method, the system maintains solution accuracy while significantly reducing memory capacity requirements
Solution Approach 2:
The patent uses iterative copying and updating of the three vectors across multiple processing steps. Instead of storing large Hessian matrices, the system repeatedly updates compact vector representations, achieving accurate solutions through iterative refinement with minimal memory usage
3Adaptability or versatility
If complex optimization problems are solved using existing devices, then comprehensive constraints are handled, but calculation speed decreases
Solution Approach 1:
The patent implements continuous iterative updates of the three vectors where each update builds upon the previous state. This continuous action allows the system to handle multiple inequality constraints comprehensively while maintaining high calculation speed through efficient, uninterrupted processing cycles rather than repeated complex matrix operations
Solution Approach 2:
The patent introduces dynamic adaptation through the three-vector update mechanism that automatically adjusts to different constraint conditions. The system dynamically modifies the vectors based on constraint satisfaction status, enabling versatile handling of various optimization problems while maintaining computational efficiency through adaptive rather than static processing
Data Source
AI summary
According to one embodiment, a calculation device includes a processing device configured to perform a processing procedure. The processing procedure includes a first update of a first vector, a second update of a second vector, and a third update of a third vector. The first update includes updating the first vector using the second vector and the third vector. The second update includes updating the second vector using the first vector. The processing device is configured to output at least one of the first vector obtained after repeating the processing procedure or a function of the first vector obtained after the repeating the processing procedure.