3 research outputs found
Optimal Communication Structures for Concurrent Computing
This research focuses on communicative solvers that run concurrently and exchange information to improve performance. This âteam of solversâ enables individual algorithms to communicate information regarding their progress and intermediate solutions, and allows them to synchronize memory structures with more âsuccessfulâ counterparts. The result is that fewer nodes spend computational resources on âstrugglingâ processes. The research is focused on optimization of communication structures that maximize algorithmic efficiency using the theoretical framework of Markov chains. Existing research addressing communication between the cooperative solvers on parallel systems lacks generality: Most studies consider a limited number of communication topologies and strategies, while the evaluation of different configurations is mostly limited to empirical testing. Currently, there is no theoretical framework for tuning communication between cooperative solvers to match the underlying hardware and software. Our goal is to provide such functionality by mapping solversâ dynamics to Markov processes, and formulating the automatic tuning of communication as a well-defined optimization problem with an objective to maximize solversâ performance metrics
Full Newton Step Interior Point Method for Linear Complementarity Problem Over Symmetric Cones
In this thesis, we present a new Feasible Interior-Point Method (IPM) for Linear Complementarity Problem (LPC) over Symmetric Cones. The advantage of this method lies in that it uses full Newton-steps, thus, avoiding the calculation of the step size at each iteration. By suitable choice of parameters we prove the global convergence of iterates which always stay in the the central path neighborhood. A global convergence of the method is proved and an upper bound for the number of iterations necessary to ïŹnd Δ-approximate solution of the problem is presented