Optimization Infeasibility Diagnosis via Constraint Conflict Extraction
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Solution Overview
Problem
Users face difficulties in understanding the cause of inability to obtain a feasible solution in optimization problems, as existing technologies struggle to present this information in a user-friendly format.
Innovation Solution
An information processing apparatus and method that select an excluded element set from a tentative element set, determine the feasibility of a tentative mathematical model, extract conflicting constraint conditions, convert them into user-understandable names, and output these names to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the computer specifies a constraint condition causing inability to obtain a feasible solution, then the cause can be identified, but the user cannot understand the cause due to the mathematical expression form
Solution Approach 1:
The patent introduces an intermediary conversion mechanism that transforms the mathematical expression of constraint conditions into human-readable names. The system includes a conversion unit that acts as a mediator between the mathematical model (infeasibility cause) and the user (information consumer), converting formal mathematical constraints into understandable descriptions without losing the essential information about what causes infeasibility.
Solution Approach 2:
The patent changes the presentation parameter of constraint conditions from mathematical expressions to named descriptions. By altering the form of information presentation while maintaining the underlying mathematical meaning, the system makes the infeasibility cause accessible to users without changing the computational logic or the actual constraint conditions themselves.
2Reliability
If the tentative mathematical model is determined to be infeasible, then the infeasibility can be detected, but the conflicting constraint conditions are difficult to extract and present
Solution Approach 1:
The patent extracts conflicting constraint conditions from the mathematical model when infeasibility is detected. The extraction unit identifies and isolates the specific constraint conditions that cause infeasibility, separating them from the rest of the mathematical model. This extracted information is then converted into readable names and presented to the user, making the complex infeasibility analysis accessible and actionable.
Solution Approach 2:
The system provides feedback to the user about why the optimization problem has no feasible solution. By detecting infeasibility and extracting the conflicting constraints, the system returns specific information to the user explaining the problem, enabling them to understand and potentially modify their optimization problem to achieve feasibility.
Data Source
AI summary
An information processing apparatus selects an excluded element set that is a subset of a tentative element set, from the tentative element set that is at least a part of a solution target element set constituting an optimization problem as a solution target, determines whether or not a tentative mathematical model that is a mathematical model in which an element set excluding the excluded element set from the tentative element set and a constraint condition are formulated, is feasible, in a case where the tentative mathematical model is determined to be infeasible, extracts a list of constraint conditions conflicting with each other from the tentative mathematical model, converts each constraint condition of the extracted list into a name of the constraint condition, and outputs the name of the constraint condition obtained by conversion.