Distributed Node Search for Large-Scale Energy Function Optimization
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Solution Overview
Problem
Existing methods for solving large-scale combinatorial optimization problems using Neumann computers are inefficient, leading to compromised solution finding performance.
Innovation Solution
The problem is divided into subproblems and distributed across multiple nodes, each searching for partial solutions using various search algorithms, with nodes sharing and updating solutions to improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the problem is solved using a single Neumann computer with traditional algorithms, then the computational approach is simple, but the solution finding performance is compromised
Solution Approach 1:
The patent divides the combinatorial optimization problem into multiple subproblems and assigns them to different nodes in a distributed system. Each node independently searches for solutions to its assigned subproblems, enabling parallel processing and improving overall solution finding performance while maintaining manageable complexity at each node level.
Solution Approach 2:
The patent combines multiple independent search processes across distributed nodes into a unified solution framework. Nodes share and integrate their search results, allowing the system to achieve superior solution finding performance that exceeds what any single node could accomplish alone, while coordinating through standardized communication protocols.
2Adaptability or versatility
If the problem scale increases, then the problem can handle more complex scenarios, but the solution finding performance deteriorates
Solution Approach 1:
The patent segments large-scale problems into smaller subproblems that can be distributed across multiple nodes. This segmentation allows the system to handle increasingly complex scenarios by adding more nodes, with each node managing a manageable portion of the overall problem, thereby maintaining solution finding performance even as problem scale increases.
Solution Approach 2:
The patent transitions from a single-machine sequential processing model to a multi-node distributed parallel processing model. This dimensional change from one-dimensional sequential execution to multi-dimensional parallel execution enables the system to scale efficiently, handling larger problems without proportionally increasing solution time.
3Productivity
If nodes operate independently without sharing solutions, then the system structure is simple, but the chances of finding optimal solutions decrease
Solution Approach 1:
The patent implements feedback mechanisms where nodes share their search results and solutions with other nodes in the distributed system. This information feedback allows nodes to learn from each other's findings, refine their search strategies, and improve overall solution quality, preventing the loss of valuable solution information that would occur in completely independent operations.
Solution Approach 2:
The patent introduces communication protocols and coordination mechanisms as intermediaries between independent nodes. These intermediaries facilitate efficient information exchange and solution sharing, enabling nodes to benefit from collective intelligence while maintaining their operational independence, thus preventing information loss without creating direct complex inter-node dependencies.
Data Source
AI summary
An information processing system searching for a solution to a problem represented by an energy function including a plurality of state variables, the information processing system includes a first node and second node. The first node searches for a first partial solution represented by a first state variable group, among the plurality of state variables, corresponding to a first subproblem assigned to the first node, the first subproblem being generated by dividing the problem. The first node holds a plurality of first solutions including a first solution corresponding to the problem, the first solution reflecting the first partial solution, and transmits one or more first solution among the plurality of first solutions. The second node updates, based on one or more first solution received from the first node, at least part of a plurality of second solution held in the second node.