Hybrid Processor Penalty Parameter Dynamics
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Solution Overview
Problem
Existing methods for solving constrained quadratic models are inefficient in balancing the optimization of objective functions with constraint satisfaction, often requiring multiple iterations with varying penalty values and lacking dynamic adjustment of penalty parameters.
Innovation Solution
A hybrid computing system employing a digital processor and a quantum processor uses a method to iteratively update penalty parameters based on feasibility results, increasing penalties for infeasible constraints and decreasing them when all constraints are met, to direct the search space towards feasibility, with the quantum processor refining solutions through quantum annealing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple iterations with varying penalty values are used to solve constrained quadratic models, then constraint satisfaction improves, but computational time and iterations increase
Solution Approach 1:
The patent implements dynamic adjustment of penalty parameters during the optimization process. The penalty parameters are updated iteratively based on constraint violation measurements, allowing the system to adaptively balance objective function optimization and constraint satisfaction without requiring multiple separate iterations with manually varied penalty values. This dynamic approach resolves the contradiction by making the penalty parameters responsive to actual constraint satisfaction status in real-time.
Solution Approach 2:
The system employs feedback mechanisms where constraint violation results from each iteration are used to adjust penalty parameters for subsequent iterations. The penalty parameters are increased when constraints are violated and decreased when constraints are satisfied, creating a closed-loop control system that automatically guides the optimization toward feasible solutions while minimizing unnecessary iterations.
2Ease of operation
If penalty parameters are statically set, then computational simplicity is maintained, but constraint satisfaction and solution quality deteriorate
Solution Approach 1:
The patent transforms static penalty parameters into dynamic ones that automatically adjust based on constraint satisfaction status. The system maintains computational simplicity by using straightforward update rules (increasing penalties for violations, decreasing for satisfaction) while achieving superior constraint satisfaction compared to static approaches. This resolves the contradiction by implementing adaptive behavior through simple, automated adjustment mechanisms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves computational efficiency by dynamically adjusting penalty parameters and ensuring constraint satisfaction, potentially leading to better quality solutions with fewer iterations.
Implementation Method 1
the quantum processor refining solutions through quantum annealing
Implementation Method 2
Quantum computers can provide speedup for certain classes of computational problems such as computational problems simulating quantum physics
Data Source
AI summary
Systems and methods for operation of a computing system to direct a search space for an optimization problem are described. One or more processors initialize an optimization algorithm, and iteratively until a termination criteria is met: receive a sample solution from the optimization algorithm, evaluate quality and feasibility of the sample solution, and where the sample solution is feasible and has the best quality so far, freeze one or more penalty parameters for a set number of iterations. Where the sample solution is not feasible or does not have the best quality so far, the one or more penalty parameters are updated based on a finite state machine, the updated one or more penalty parameters are returned to the optimization algorithm, the optimization algorithm is incremented, the termination criteria is evaluated, and when the termination criteria is met, one or more sample solutions are returned.