scholarly journals The $n$-Card Problem, Stochastic Matrices, and the Extreme Principle

10.37236/2444 ◽  
2012 ◽  
Vol 19 (2) ◽  
Author(s):  
Justin H.C. Chan ◽  
Jonathan Jedwab

The $n$-card problem is to determine the minimal intervals $[u,v]$ such that for every $n \times n$ stochastic matrix $A$ there is an $n \times n$ permutation matrix $P$ (depending on $A$) such that tr$(PA) \in [u,v]$. This problem is closely related to classical mathematical problems from industry and management, including the linear assignment problem and the travelling salesman problem. The minimal intervals for the $n$-card problem are known only for $n \le 4$.We introduce a new method of analysis for the $n$-card problem that makes repeated use of the Extreme Principle. We use this method to answer a question posed by Sands (2011), by showing that $[1,2]$ is a solution to the $n$-card problem for all $n \ge 2$. We also show that each closed interval of length $\frac{n}{n-1}$ contained in $[0,2)$ is a solution to the $n$-card problem for all $n \ge 2$.

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Weifeng Liu ◽  
Jie Zhou ◽  
Meng Guo

This paper presents the topology-aware two-phase I/O (TATP), which optimizes the most popular collective MPI-IO implementation of ROMIO. In order to improve the hop-bytes metric during the file access, topology-aware two-phase I/O employs the Linear Assignment Problem (LAP) for finding an optimal assignment of file domain to aggregators, an aspect which is not considered in most two-phase I/O implementations. The distribution is based on the local data stored by each process, and its main purpose is to reduce the total hop-bytes of the I/O collective operation. Therefore, the global execution time can be improved. In most of the considered scenarios, topology-aware two-phase I/O obtains important improvements when compared with the original two-phase I/O implementations.


2013 ◽  
Vol 65 (4) ◽  
pp. 673-678 ◽  
Author(s):  
Mohammad S. Sabbagh ◽  
Sayyed R. Mousavi ◽  
Yasin Zamani

1994 ◽  
Vol 6 (3) ◽  
pp. 341-356 ◽  
Author(s):  
A. L. Yuille ◽  
J. J. Kosowsky

In recent years there has been significant interest in adapting techniques from statistical physics, in particular mean field theory, to provide deterministic heuristic algorithms for obtaining approximate solutions to optimization problems. Although these algorithms have been shown experimentally to be successful there has been little theoretical analysis of them. In this paper we demonstrate connections between mean field theory methods and other approaches, in particular, barrier function and interior point methods. As an explicit example, we summarize our work on the linear assignment problem. In this previous work we defined a number of algorithms, including deterministic annealing, for solving the assignment problem. We proved convergence, gave bounds on the convergence times, and showed relations to other optimization algorithms.


Computing ◽  
1987 ◽  
Vol 39 (2) ◽  
pp. 165-174 ◽  
Author(s):  
J. B. G. Frenk ◽  
M. van Houweninge ◽  
A. H. G. Rinnooy Kan

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