Neural Comput - Linear coordinate-descent message passing for quadratic optimization.

Tópicos

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Resumo

In this letter, we propose a new message-passing algorithm for quadratic optimization. The design of the new algorithm is based on linear coordinate descent between neighboring nodes. The updating messages are in a form of linear functions as compared to the min-sum algorithm of which the messages are in a form of quadratic functions. As a result, the linear coordinate-descent (LiCD) algorithm transmits only one parameter per message as opposed to the min-sum algorithm, which transmits two parameters per message. We show that when the quadratic matrix is walk-summable, the LiCD algorithm converges. By taking the LiCD algorithm as a subroutine, we also fix the convergence issue for a general quadratic matrix. The LiCD algorithm works in either a synchronous or asynchronous message-passing manner. Experimental results show that for a general graph with multiple cycles, the LiCD algorithm has comparable convergence speed to the min-sum algorithm, thereby reducing the number of parameters to be transmitted and the computational complexity.

Resumo Limpo

letter propos new messagepass algorithm quadrat optim design new algorithm base linear coordin descent neighbor node updat messag form linear function compar minsum algorithm messag form quadrat function result linear coordinatedesc licd algorithm transmit one paramet per messag oppos minsum algorithm transmit two paramet per messag show quadrat matrix walksumm licd algorithm converg take licd algorithm subroutin also fix converg issu general quadrat matrix licd algorithm work either synchron asynchron messagepass manner experiment result show general graph multipl cycl licd algorithm compar converg speed minsum algorithm therebi reduc number paramet transmit comput complex

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