Graph-Based Constraint Relaxation History Visualization
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Solution Overview
Problem
Users face challenges in effectively presenting and managing the history of relaxation of constraint conditions in optimization problems, as existing systems do not adequately display or utilize this information for decision-making.
Innovation Solution
An information processing device and method that acquires and displays constraint relaxation history information as a graph structure, allowing users to visualize and manage the relaxation of constraint conditions, including the ability to merge and compare nodes representing different relaxation settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If constraint relaxation history information is accumulated for user reference, then the completeness and detail of historical data is improved, but the complexity of data management and presentation increases
Solution Approach 1:
The patent segments the constraint relaxation history information into distinct nodes representing individual relaxation settings. Each node contains specific attributes (constraint condition, relaxation degree, timing) that can be independently managed and displayed, making the overall complex information structure more manageable and accessible.
Solution Approach 2:
The patent transforms the flat, linear history data into a graph structure with multiple dimensions including nodes (individual settings), edges (relationships between settings), and hierarchical levels. This dimensional transformation allows complex historical data to be visualized and managed more effectively through spatial relationships.
2Loss of information
If detailed constraint relaxation history is stored for future reference, then the information completeness is improved, but the difficulty of presenting and analyzing the data increases
Solution Approach 1:
The patent employs graph visualization to transform complex tabular history data into a spatial representation where nodes and edges reveal patterns, relationships, and trends that are difficult to detect in traditional formats. This dimensional change makes the data both complete and analyzable.
Solution Approach 2:
The patent creates visual copies of the constraint relaxation history in the form of graph structures that preserve all original information while presenting it in an intuitively understandable format. Users can interact with these visual copies to analyze the data without altering the original detailed records.
3Adaptability or versatility
If multiple constraint relaxation settings are tracked, then the comprehensiveness of optimization analysis is improved, but the complexity of managing and comparing settings increases
Solution Approach 1:
The patent segments multiple constraint relaxation settings into discrete, identifiable nodes within the graph structure. Each node represents a specific relaxation setting with its own attributes, allowing users to manage, select, and compare individual settings systematically rather than dealing with undifferentiated bulk data.
Solution Approach 2:
The patent merges multiple constraint relaxation settings into a unified graph structure that preserves the individual characteristics of each setting while showing their relationships. This combination allows comprehensive analysis of multiple settings without the management complexity of separate systems.
Data Source
AI summary
The information processing device 1X mainly includes an acquisition means 16X and a history display means 161X. The acquisition means 16X acquires constraint relaxation history information indicating a history relating to settings of relaxation of a constraint condition in an optimization problem. The history display means 161X displays, on a display device, a graph structure in which each of the settings included in the history is represented by a node, based on the constraint relaxation history information.