Information Processing Device Solving Combination Optimization via Weight Segmentation
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Solution Overview
Problem
Existing information processing systems face challenges in efficiently solving large-scale combination optimization problems, particularly due to the exponential increase in solution states as the number of decision variables grows, known as the combination explosion problem.
Innovation Solution
The proposed information processing device incorporates a solver device and an information processing circuit that repeatedly acquire and update weight values based on first and second data, allowing the system to efficiently solve combination optimization problems by combining these weight values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of decision variables increases to handle more complex optimization problems, then the problem-solving capability improves, but the number of solution states increases exponentially causing combination explosion
Solution Approach 1:
The patent divides the weight values into two separate groups: first weight values associated with first decision variables and second weight values associated with second decision variables. This segmentation allows the system to manage large-scale optimization problems by treating different variable groups independently, updating their respective weight values based on different data sources, and combining them during problem generation, thereby avoiding the need to process all solution states simultaneously.
Solution Approach 2:
The system performs preliminary updates of weight values before generating the combination optimization problem. The first weight values are updated based on first data, and second weight values are updated based on second data, in advance of problem generation. This preliminary action prepares the weight values for efficient combination and solving, reducing the computational burden when handling large numbers of decision variables.
2Speed
If the system generates new combination optimization problems in response to frequent situation changes, then the responsiveness improves, but the calculation time required increases
Solution Approach 1:
The patent combines the first weight values and second weight values to generate the cost function for the combination optimization problem. By merging these pre-updated weight values, the system can quickly formulate new optimization problems in response to situation changes without recalculating all parameters from scratch, thus reducing calculation time while maintaining responsiveness.
Solution Approach 2:
The system updates weight values in advance based on available data before new optimization problems arise. This preliminary preparation of weight values enables the system to respond quickly to situation changes by simply combining existing weight values rather than performing full recalculations, thereby reducing loss of time while maintaining high responsiveness.
3Adaptability or versatility
If the system processes multiple types of situations with different change rates, then the system versatility improves, but the complexity of managing weight value updates increases
Solution Approach 1:
The patent segments weight values into distinct groups (first weight values and second weight values) that can be updated independently based on different data sources and update frequencies. This segmentation allows the system to handle multiple situation types with different change rates by updating only the relevant weight value groups, reducing the complexity of managing simultaneous updates across all weight values.
Solution Approach 2:
The system dynamically updates weight values based on the availability and timing of different data types. First weight values are updated based on first data acquisition timing, while second weight values are updated based on second data acquisition timing. This dynamic update approach allows the system to adapt to different situation change rates without requiring a unified update schedule, thereby reducing management complexity while maintaining versatility.
Data Source
AI summary
An information processing device according to an embodiment includes a solver device and an information processing circuit. The solver device stores first weight values and second weight values. The solver device solves a combination optimization problem minimizing a cost function including weight values obtained by combining the first weight values and the second weight values. The information processing circuit repeatedly acquires first data. The information processing circuit repeatedly acquires second data asynchronously with the timing of acquiring the first data. The information processing circuit updates the first weight values stored in the solver device on the basis of the first data, and updates a plurality of second weight values stored in the solver device on the basis of the second data.