This paper introduces a general modeling framework for multi-type maximal covering location problem in which the position of facilities different normed spaces are simultaneously decided to maximize demand generated by set points. From need intertwining decisions discrete and continuous sets, hybridized is considered some types be located finite sets others spaces. A natural non-linear model pr...

This paper presents a Federated Learning (FL) algorithm that allows the decentralization of all FL solutions employ model-averaging procedure. The proposed proves to be capable attaining faster convergence rates and no performance loss against starting centralized implementation with reduced communication overhead compared existing consensus-based solutions. To this end, Multi-Hop consensus pro...

We consider structure preserving numerical schemes for the Ostrovsky equation, which describes gravity waves under the influence of Coriolis force. This equation has two associated invariants: an energy function and the L norm. It is widely accepted that structure preserving methods such as invariants-preserving and multi-symplectic integrators generally yield qualitatively better numerical res...

This paper will address an innovative bio-inspired algorithm able to incrementally grow decision graphs in multiple directions for discrete multi-objective optimisation. The algorithm takes inspiration from the slime mould Physarum Polycephalum, an amoeboid organism that in its plasmodium state extends and optimizes a net of veins looking for food. The algorithm is here used to solve multi-obje...

Nonminimum Phase Discrete Time Models Yuping Gu and Masayoshi Tomizuka Department of Mechanical Engineering University of California at Berkeley, Berkeley, CA 94720-1740 [email protected] [email protected] Abstract This paper is concerned with performance enhancement of tracking control systems by multi-rate control. The feedback controller is updated at the same rate as the sampling r...