Distributed Optimization Concurrency Control
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Solution Overview
Problem
Optimizing large logistic regression models for ranking documents is challenging due to convergence issues when variables interact, leading to potential divergence, especially in distributed optimization settings where naive approaches may fail to predict optimal parameter values efficiently.
Innovation Solution
A system and method that utilize a repository to store training data and distribute conditions among devices, employing a concurrency counter to ensure that only a predetermined number of conditions are optimized concurrently, thereby preventing divergence and enabling efficient parallel processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If variables are optimized one at a time to eliminate divergence, then convergence is guaranteed, but optimization speed decreases
Solution Approach 1:
The patent segments the optimization process by introducing a concurrency counter that divides variables into groups that can be optimized simultaneously. Instead of optimizing all variables one at a time or all at once, the system divides them into manageable batches controlled by the concurrency parameter, allowing parallel optimization while maintaining convergence through controlled segmentation of the optimization workload.
Solution Approach 2:
The patent makes the optimization system dynamic by allowing the concurrency counter value to be adjusted based on problem characteristics and computational resources. The system dynamically adapts the degree of parallelization by modifying how many variables are optimized concurrently, enabling flexibility between safety (lower concurrency) and speed (higher concurrency) depending on the specific optimization context.
2Reliability
If a small number of weights are optimized at once to ensure stability, then divergence is prevented, but optimization efficiency decreases
Solution Approach 1:
The patent changes the parameter controlling the number of concurrently optimized variables from a fixed small value to a configurable concurrency counter that can be set to predetermined values. This parameter change allows the system to adjust the batch size of optimized variables, enabling larger batches (higher efficiency) when stability permits, while still maintaining control through the predetermined limit that prevents overwhelming the optimization process.
3Productivity
If the parameter controlling number of rules is set as high as possible to improve efficiency, then processing speed increases, but system reliability decreases
Solution Approach 1:
The patent implements feedback control through the concurrency counter mechanism. The predetermined value of the concurrency counter acts as a feedback-limited parameter that prevents the system from exceeding safe optimization thresholds. By monitoring and controlling the number of concurrently optimized variables against this predetermined limit, the system receives feedback that maintains reliability while still allowing high-speed processing within safe boundaries.
Data Source
AI summary
A method for efficiently choosing optimal weights in a distributed manner may include optimizing weights in a predefined order while preventing or reducing the likelihood that interacting weights are concurrently optimized. In this manner, divergence and deadlock during optimization may be avoided.