Telefonaktiebolaget lm ericsson (publ) (20240119355). HIERARCHICAL ONLINE CONVEX OPTIMIZATION simplified abstract
Contents
- 1 HIERARCHICAL ONLINE CONVEX OPTIMIZATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 HIERARCHICAL ONLINE CONVEX OPTIMIZATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
HIERARCHICAL ONLINE CONVEX OPTIMIZATION
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Gary Boudreau of Kanata, Ontario (CA)
Hatem Abou-zeid of Calgary, Alberta (CA)
Juncheng Wang of Toronto, Ontario (CA)
Ben Liang of Whitby, Ontario (CA)
Min Dong of Whitby, Ontario (CA)
HIERARCHICAL ONLINE CONVEX OPTIMIZATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240119355 titled 'HIERARCHICAL ONLINE CONVEX OPTIMIZATION
Simplified Explanation
The abstract describes a method for performing online convex optimization by receiving local decision vectors and data from multiple worker nodes, performing multi-step gradient descent, determining a global decision vector and information, and sending this global information back to the worker nodes.
- Receiving local decision vectors and data from multiple worker nodes
- Performing multi-step gradient descent based on the received local decision vectors and data
- Determining a global decision vector and corresponding global information
- Sending the global decision vector and information back to the worker nodes
Potential Applications
The technology described in this patent application could be applied in various fields such as machine learning, data analysis, and optimization problems in distributed systems.
Problems Solved
This technology solves the problem of efficiently performing online convex optimization in a distributed environment with multiple worker nodes.
Benefits
The benefits of this technology include faster optimization processes, improved scalability, and better utilization of resources in distributed systems.
Potential Commercial Applications
A potential commercial application of this technology could be in cloud computing platforms where optimization tasks need to be distributed among multiple worker nodes efficiently.
Possible Prior Art
One possible prior art for this technology could be existing methods for distributed optimization in machine learning and data analysis.
Unanswered Questions
How does this method compare to existing distributed optimization techniques in terms of efficiency and scalability?
This article does not provide a direct comparison with existing distributed optimization techniques, leaving the reader to wonder about the specific advantages of this method over others.
What are the specific use cases where this method would outperform traditional optimization algorithms?
The article does not delve into specific use cases where this method would excel, leaving the reader curious about the practical applications of this technology in real-world scenarios.
Original Abstract Submitted
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.
