Human navigation has been a topic of interest in spatial cognition from the past few decades. It has been experimentally observed that humans accomplish the task of way-finding a destination in an unknown environment by recognizing landmarks. Investigations using network analytic techniques reveal that humans, when asked to way-find their destination, learn the top ranked nodes of a network. In this paper we report a study simulating the strategy used by humans to recognize the centers of a network. We show that the paths obtained from our simulation has the same properties as the paths obtained in human based experiment. The simulation thus performed leads to a novel way of path-finding in a network. We discuss the performance of our method and compare it with the existing techniques to find a path between a pair of nodes in a network.
The well-known least squares semidefinite programming (LSSDP) problem seeks the nearest adjustment of a given symmetric matrix in the intersection of the cone of positive semidefinite matrices and a set of linear constraints, and it captures many applications in diversing fields. The task of solving large-scale LSSDP with many linear constraints, however, is numerically challenging. This paper mainly shows the applicability of the classical alternating direction method (ADM) for solving LSSDP and convinces the efficiency of the ADM approach. We compare the ADM approach with some other existing approaches numerically, and we show the superiority of ADM for solving large-scale LSSDP.