Electronic Circuit for Parallel Simulated Bifurcation Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently calculating optimal or approximate solutions for combinatorial optimization problems due to the 'combinatorial explosion', leading to exponential increases in the number of combinations and difficulties in finding precise solutions within practical periods.
Innovation Solution
An information processing system utilizing parallel and distributed processing across multiple computing servers, combined with a Simulated Bifurcation Algorithm and electronic circuits, to solve combinatorial optimization problems, including Ising Models, by updating vectors based on partial derivatives and using signum functions to convert continuous variables into discrete solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequential processing methods are used to solve combinatorial optimization problems, then calculation precision can be maintained, but the computational time becomes impractically long due to combinatorial explosion
Solution Approach 1:
The patent divides the combinatorial optimization problem into multiple sub-problems or stages, processing them in parallel through multiple computing servers. This segmentation allows the system to handle large-scale problems by breaking them down into manageable chunks that can be solved simultaneously, reducing overall computational time while maintaining solution quality through coordinated processing.
Solution Approach 2:
The patent transitions from traditional sequential one-dimensional processing to multi-dimensional parallel processing across multiple computing servers. By utilizing distributed computing architecture with multiple processing nodes working simultaneously, the system adds temporal and spatial dimensions to the computation, dramatically reducing solution time while maintaining precision through coordinated convergence of parallel processes.
2Quantity of substance
If the problem size increases in combinatorial optimization, then the solution value increases, but the number of combinations increases exponentially making optimal solution calculation infeasible
Solution Approach 1:
The patent applies segmentation by dividing large-scale combinatorial optimization problems into smaller sub-problems that can be processed in parallel. Each computing server handles a portion of the overall problem, breaking down the exponential complexity into manageable segments that can be solved independently and then combined, effectively linearizing the complexity growth relative to problem size.
Solution Approach 2:
The patent merges the computational power of multiple independent computing servers to tackle large-scale problems. By combining parallel processing capabilities across multiple nodes and coordinating their results, the system achieves linear scaling of computation power with problem size, preventing exponential complexity from becoming infeasible.
3Productivity
If more computing resources are allocated to solve combinatorial optimization problems, then solution speed increases, but system complexity and resource coordination difficulty increase
Solution Approach 1:
The patent implements a universal computing framework where multiple computing servers can be dynamically allocated and coordinated through a standardized interface. The system uses a common optimization algorithm and data structure that works across different hardware configurations, allowing flexible resource allocation without proportionally increasing system complexity. Each server performs the same multi-objective optimization function, enabling scalable parallel processing.
Data Source
AI summary
According to one embodiment, an information processing device includes a first processing circuit and a second processing circuit. The first processing circuit is configured to update a third vector based on basic equations. Each of the basic equations is a partial derivative of an objective function with respect to either of the variables in the objective function. The second processing circuit is configured to update the element of the first vector and update the element of the second vector. The element of the first vector smaller than a first value is set to the first value. The element of the first vector greater than a second value is set to the second value.