scholarly journals Computing the Period of an Ehrhart Quasi-Polynomial

10.37236/1931 ◽  
2005 ◽  
Vol 12 (1) ◽  
Author(s):  
Kevin Woods

If $P\subset {\Bbb R}^d$ is a rational polytope, then $i_P(t):=\#(tP\cap {\Bbb Z}^d)$ is a quasi-polynomial in $t$, called the Ehrhart quasi-polynomial of $P$. A period of $i_P(t)$ is ${\cal D}(P)$, the smallest ${\cal D}\in {\Bbb Z}_+$ such that ${\cal D}\cdot P$ has integral vertices. Often, ${\cal D}(P)$ is the minimum period of $i_P(t)$, but, in several interesting examples, the minimum period is smaller. We prove that, for fixed $d$, there is a polynomial time algorithm which, given a rational polytope $P\subset{\Bbb R}^d$ and an integer $n$, decides whether $n$ is a period of $i_P(t)$. In particular, there is a polynomial time algorithm to decide whether $i_P(t)$ is a polynomial. We conjecture that, for fixed $d$, there is a polynomial time algorithm to compute the minimum period of $i_P(t)$. The tools we use are rational generating functions.

Author(s):  
Jorma Jormakka ◽  
Sourangshu Ghosh

The paper describes a method of solving some stochastic processes using generating functions. A general theorem of generating functions of a particular type is derived. A generating function of this type is applied to a stochastic process yielding polynomial time algorithms for certain partitions. The method is generalized to a stochastic process describing a rather general linear transform. Finally, the main idea of the method is used in deriving a theoretical polynomial time algorithm to the knapsack problem.


10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.


2002 ◽  
Vol 50 (8) ◽  
pp. 1935-1941 ◽  
Author(s):  
Dongning Li ◽  
Yong Ching Lim ◽  
Yong Lian ◽  
Jianjian Song

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