Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the growth of data and necessity for distributed optimization methods, solvers that work ll on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by focusing heavily on developing highly specific methods for the distributed environment. These special-purpose methods are often unable to fully leverage the competitive performance of their ll-tuned and customized single machine counterparts. Further, they are unable to easily integrate improvements that continue to be made to single machine methods. To this end,  present a framework for distributed optimization that both allows the flexibility of arbitrary solvers to be used on each (single) machine locally and yet maintains competitive performance against other state-of-the-art special-purpose distributed methods.  give strong primal–dual convergence rate guarantees for our framework that hold for arbitrary local solvers.  demonstrate the impact of local solver selection both th...
['Chenxin Ma', 'Jakub Konečný', 'Martin Jaggi', 'Virginia Smith', 'Michael I. Jordan', 'Peter Richtárik', 'Martin Takáč']
I am writing a research paper on a idea Distributed Optimization with Arbitrary Local Solvers
[{"from": "human", "value": "I am writing a research paper on a idea Distributed Optimization with Arbitrary Local Solvers"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: With the growth of data and necessity for distributed optimization methods, solvers that work ll on a single machine must be re-designed to leverage distributed computation. Recent work in this area has been limited by focusing heavily on developing highly specific methods for the distributed environment. These special-purpose methods are often unable to fully leverage the competitive performance of their ll-tuned and customized single machine counterparts. Further, they are unable to easily integrate improvements that continue to be made to single machine methods. To this end,  present a framework for distributed optimization that both allows the flexibility of arbitrary solvers to be used on each (single) machine locally and yet maintains competitive performance against other state-of-the-art special-purpose distributed methods.  give strong primal\u2013dual convergence rate guarantees for our framework that hold for arbitrary local solvers.  demonstrate the impact of local solver selection both th..."}]