Hierarchical Online Convex Optimization for Dynamic Networks
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Solution Overview
Problem
Existing online convex optimization frameworks fail to effectively handle dynamic problems with non-separable global cost functions in heterogeneous networks, where information timeliness and computation capacity vary, leading to high communication overhead and performance degradation.
Innovation Solution
The Hierarchical Online Convex Optimization (HiOCO) framework allows for multi-step gradient descent at both the master and worker nodes, leveraging timely local and delayed global information to minimize global costs, even with non-separable cost functions and communication delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If centralized algorithms are used for non-separable global cost minimization, then optimization performance is improved, but communication overhead and delay increase
Solution Approach 1:
The patent segments the centralized optimization problem into distributed sub-problems at worker nodes. Each worker performs local gradient computations independently on its own data, and only exchanges necessary aggregated information with the master node, thereby reducing communication overhead and delay while maintaining optimization performance.
Solution Approach 2:
The patent introduces a hierarchical dimension to the optimization architecture, with a master node coordinating multiple worker nodes. This hierarchical structure allows parallel processing at the worker level while maintaining centralized coordination, effectively adding a spatial dimension that reduces communication bottlenecks.
2Loss of time
If distributed online optimization frameworks are used, then communication overhead is reduced, but they are confined to separable global cost functions
Solution Approach 1:
The master node serves as an intermediary that enables distributed workers to handle non-separable cost functions. Workers compute local gradients based on their own data and receive coordination information from the master, which aggregates and redistributes necessary global information, allowing distributed processing of non-separable problems without requiring full data exchange.
3Measurement precision
If worker nodes have timely local information, then local decision quality is improved, but computation capacity is limited
Solution Approach 1:
The patent segments the computational workload by function: worker nodes perform local gradient computations on timely local data, while the master node handles global coordination and aggregation. This segmentation allows workers to leverage their timely information without being burdened by heavy computation tasks requiring global data.
Solution Approach 2:
The master node performs multiple functions including aggregating gradients from workers, computing global updates, and coordinating distributed optimization. This multi-functionality allows the system to leverage both local timeliness at workers and centralized computational power at the master.
Data Source
AI summary
A method for performing online convex optimization is provided. The method includes receiving, from two or more worker nodes, a local decision vector and local data corresponding to each of the two or more worker nodes. The method includes performing a multi-step gradient descent based on the local decision vector and the local data received from the two or more worker nodes. Performing the multi-step gradient descent includes determining a global decision vector and corresponding global information. The method includes sending, to each of the two or more worker nodes, the global decision vector and corresponding global information.