Annealer NP Problem Automation via Template Interfaces
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Solution Overview
Problem
Conventional systems require expert users to manually formulate Non-Deterministic Polynomial-time (NP) problems, which is time-consuming and may result in inferior solutions due to the dependence on professional expertise, making it difficult for non-expert end-users to obtain optimal solutions for combinatorial optimization problems.
Innovation Solution
A system is configured with an electronic user interface that allows end-users to select templates and input data for optimization problems, using knowledge graphs to automate the construction of NP formulations and objective functions, thereby reducing the need for expert intervention and improving solution quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If expert users manually formulate NP problems using optimization solvers, then solution quality may be improved through professional expertise, but the time required for problem formulation becomes excessively long
Solution Approach 1:
The system pre-provides templates for NP problem formulations that are prepared in advance by experts. Users can directly select and fill in these pre-formed templates rather than creating formulations from scratch, significantly reducing formulation time while maintaining solution quality through the use of professionally designed template structures.
Solution Approach 2:
The system copies proven problem formulation templates from expert knowledge into the user interface. These templates replicate the structure and logic of expert-formulated problems, allowing users to instantiate similar solutions quickly without needing to recreate complex formulations themselves.
2Measurement precision
If professional experts manually formulate optimization problems, then formulation accuracy improves with expertise, but accessibility for non-expert end-users deteriorates
Solution Approach 1:
The system introduces an intermediary layer in the form of a user interface that mediates between non-expert users and complex NP problem formulations. The interface provides simplified interactions through templates and guided input, translating user needs into accurate formulations without requiring users to understand the underlying complexity.
Solution Approach 2:
The formulation process is segmented into manageable components through templates that break down complex problems into standardized sections. Each template represents a specific problem type with pre-defined structures, allowing users to fill in parameters rather than construct entire formulations, thus maintaining accuracy while simplifying operation.
3Productivity
If conventional computers are used to solve NP problems, then computational resources are accessible, but computational intractability increases for complex problems
Solution Approach 1:
The system changes the approach to computational complexity by transforming problems into standardized NP formulations through templates. This parameter transformation allows conventional computers to handle problems that would otherwise be intractable by rephrasing them in a computationally friendly format that leverages existing algorithmic solutions.
Data Source
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AI summary
According to an aspect of an embodiment, operations may include displaying a electronic user interface that includes a plurality of user-selectable options corresponding to taxonomy information for a plurality of optimization problems. The operations may further include receiving a first user input selecting a first template for a specific optimization problem of the plurality of optimization problems. The first user input may include a selection of at least one user-selectable option of the plurality of user-selectable options. The operations may further include receiving a second user input via the selected first template for the specific optimization problem and providing a call to the optimization solver machine to generate a solution for the specific optimization problem based on the received second user input. The second user input may include input data for a plurality of parameters of the specific optimization problem, specified in the selected first template.