Parallel Optimizer Initialization via Machine Learning
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Solution Overview
Problem
Conventional iterative optimization techniques are highly sensitive to initializations and may converge slowly or to local optima, especially when environmental changes occur, such as in autonomous vehicle control systems.
Innovation Solution
A computer-implemented method using a trained machine learning model to generate multiple initializations for parallel optimizers, which perform iterative optimization techniques in parallel to generate and select improved optimization results, ultimately producing a control signal for systems like autonomous vehicles or robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single optimizer is used with conventional initialization methods, then the system complexity is low, but the optimization reliability deteriorates due to sensitivity to initializations and tendency to converge to local optima
Solution Approach 1:
The patent divides the optimization process into multiple independent optimizer instances, each handling a different initialization scenario. This segmentation allows parallel exploration of multiple optimization paths simultaneously, improving the probability of finding global optima while maintaining manageable complexity through modular architecture
Solution Approach 2:
The patent introduces a new dimension by using machine learning models to generate multiple diverse initializations before optimization. This transforms the conventional single-point initialization approach into a multi-point initialization strategy, adding a dimensional layer of diversity that helps escape local optima and improves overall optimization reliability
2Reliability
If multiple initializations are generated using machine learning models, then the optimization reliability improves, but the computational cost and time consumption increase
Solution Approach 1:
The patent performs preliminary actions by using machine learning models to generate multiple high-quality initializations before the actual optimization process. This pre-computation of diverse initializations allows the optimization algorithms to start from advantageous positions, reducing the time needed to converge to good solutions while maintaining high reliability
Solution Approach 2:
The patent creates multiple copies of the optimization process with different initializations. By running parallel optimizer instances that replicate the optimization algorithm but start from different ML-generated initializations, the system efficiently explores multiple solution spaces simultaneously, improving reliability without linearly increasing total computational time
3Ease of operation
If previously generated solutions are used for initialization, then the ease of operation is high, but the adaptability to environmental changes deteriorates
Solution Approach 1:
The patent introduces dynamics by replacing static previously-generated solutions with dynamic ML-generated initializations. The machine learning models adaptively create initializations based on current system state and environmental conditions, allowing the optimization process to dynamically respond to changes while maintaining ease of operation through automated initialization
Solution Approach 2:
The patent changes the parameters of initialization by using machine learning models to generate diverse initializations based on current environmental conditions. This transforms the initialization process from using fixed previously-generated solutions to using adaptively-generated parameters that reflect current system state, thereby improving adaptability while maintaining operational ease
Data Source
AI summary
One embodiment of a method for controlling a system includes generating a plurality of initializations using a trained machine learning model, performing a plurality of instances of an iterative technique based on the plurality of initializations to generate a plurality of results, generating a control signal based on one or more results included in the plurality of results, and transmitting the control signal to the system to cause the system to perform one or more operations.