Generic Optimization Core with Derivative Modules
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Solution Overview
Problem
Existing machine learning optimization techniques, such as stochastic coordinate descent, face challenges in efficient implementation due to the variety of functional forms in objective functions, limiting their applicability and requiring specialized code for each optimization problem.
Innovation Solution
A core module for generic optimization is provided, along with derivative modules for computing first and second derivatives, enabling the system to perform stochastic coordinate descent iterations regardless of the functional form, allowing for efficient training of arbitrary machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If specialized code is written for each optimization problem, then optimization performance is improved, but system complexity and development time increase
Solution Approach 1:
The patent implements a universal optimization framework where a single coordinate descent engine can handle multiple optimization problems (logistic regression, linear regression, support vector machines, etc.) by accepting different objective functions and their corresponding derivative modules. This eliminates the need for separate specialized code for each problem type while maintaining optimization performance.
Solution Approach 2:
The optimization system is segmented into independent modular components: the core coordinate descent engine, objective function modules, and derivative modules. Each component has a specific responsibility and can be independently developed, tested, and combined. This modular architecture reduces system complexity while enabling high performance across multiple problems.
2Adaptability or versatility
If a generic optimization framework is used, then adaptability to different problems is improved, but optimization speed may decrease
Solution Approach 1:
The system achieves both generality and speed by dynamically changing parameters based on the specific problem. The coordinate descent engine adapts its behavior based on the provided objective function and derivative modules, optimizing the convergence rate for each specific problem type while maintaining a unified framework. This allows the generic framework to perform as fast as specialized implementations.
3Reliability
If multiple specialized implementations are maintained, then problem-specific optimization is improved, but maintenance burden and error potential increase
Solution Approach 1:
By implementing a universal coordinate descent engine that handles all optimization problems through a unified interface, the system reduces maintenance burden. Bug fixes and performance improvements need to be made only in the core engine rather than in multiple specialized implementations, while maintaining problem-specific optimization accuracy through carefully designed derivative modules.
Solution Approach 2:
The system uses template-based copying where the core optimization logic is replicated in a parameterized form that can be instantiated for different problems. This ensures consistency across problem types while allowing customization through derivative modules, reducing errors from manual replication.
Data Source
AI summary
Methods are provided for implementing training of a machine learning model in a processing system, together with systems for performing such methods. A method includes providing a core module for effecting a generic optimization process in the processing system, and in response to a selective input, defining a set of derivative modules, for effecting computation of first and second derivatives of selected functions ƒ and g in the processing system, to be used with the core module in the training operation. The method further comprises performing, in the processing system, the generic optimization process effected by the core module using derivative computations effected by the derivative modules.