Blackbox Optimization Using Non-Orthogonal Sampling and Gradient Reuse
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Solution Overview
Problem
Existing blackbox optimization techniques for machine-learning systems are inefficient and resource-intensive, particularly in hyperparameter tuning, due to the need for manual experimentation and high sampling complexity, which consumes significant computing resources and time without quickly reaching optimal parameter values.
Innovation Solution
A method involving non-orthogonal sampling distributions for perturbations, regression-based gradient estimation, and trust region techniques to reuse previously evaluated perturbations, reducing the need for repeated evaluations and optimizing parameter values more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual tuning/experimentation is used to optimize system parameters, then the system can be adjusted to improve performance, but it consumes significant human time and computing resources without efficiently reaching optimal values
Solution Approach 1:
The system performs self-optimization through automated blackbox optimization algorithms that automatically search for optimal parameter values without human intervention. The optimization algorithm independently evaluates different parameter configurations and converges to optimal values, eliminating the need for manual tuning while reducing both time and computational resource consumption.
Solution Approach 2:
The patent replaces manual mechanical tuning processes with computational optimization algorithms. Instead of human operators systematically adjusting parameters, the system uses derivative-free optimization methods that automatically explore the parameter space and identify optimal configurations through mathematical modeling and evaluation.
2Manufacturing precision
If extensive tuning and testing is performed to reach optimal parameter values, then optimization accuracy improves, but computing resources such as memory space, processor usage, and power consumption are inefficiently consumed
Solution Approach 1:
The optimization algorithm performs partial evaluations by selectively testing only the most promising parameter configurations based on previous results and mathematical models. Instead of exhaustively testing all possible combinations, the system uses surrogate models and optimization strategies to identify and evaluate only the critical subset of parameter space that is likely to contain optimal values.
Solution Approach 2:
The system dynamically changes evaluation parameters during the optimization process, adjusting the number of evaluations, sampling strategies, and model complexity based on progress and resource availability. This adaptive parameter adjustment allows the system to achieve good optimization results while consuming fewer computing resources by scaling the evaluation intensity appropriately.
3Adaptability or versatility
If blackbox optimization techniques are used to optimize complex systems, then the ability to handle systems with limited information improves, but high sampling complexity requires a substantial number of evaluations which consumes more computing resources
Solution Approach 1:
The patent introduces surrogate models or proxy evaluation functions as intermediaries between the optimization algorithm and the actual system evaluation. These surrogate models approximate the system's behavior based on limited samples, allowing the optimization algorithm to make informed decisions without requiring numerous actual system evaluations. The surrogate model acts as a mediator that reduces the sampling complexity while maintaining optimization effectiveness.
Data Source
AI summary
The present disclosure provides iterative blackbox optimization techniques that estimate the gradient of a function. According to an aspect of the present disclosure, a plurality of perturbations used at each iteration can be sampled from a non-orthogonal sampling distribution. As one example, in some implementations, perturbations that have been previously evaluated in previous iterations can be re-used at the current iteration, thereby conserving computing resources because the re-used perturbations do not need to be re-evaluated at the current iteration. In another example, in addition or alternatively to the use of previously evaluated perturbations, the perturbations evaluated at the current iteration can be sampled from a non-orthogonal sampling distribution.