scholarly journals Discrepancy Games

10.37236/1948 ◽  
2005 ◽  
Vol 12 (1) ◽  
Author(s):  
Noga Alon ◽  
Michael Krivelevich ◽  
Joel Spencer ◽  
Tibor Szabó
Keyword(s):  
Game Theory ◽  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Vertex Set

We investigate a game played on a hypergraph $H=(V,E)$ by two players, Balancer and Unbalancer. They select one element of the vertex set $V$ alternately until all vertices are selected. Balancer wins if at the end of the game all edges $e\in E$ are roughly equally distributed between the two players. We give a polynomial time algorithm for Balancer to win provided the allowed deviation is large enough. In particular, it follows from our result that if $H$ is $n$-uniform and has $m$ edges, then Balancer can achieve having between $n/2-\sqrt{\ln(2m)n/2}$ and $n/2+\sqrt{\ln(2m)n/2}$ of his vertices on every edge $e$ of $H$. We also discuss applications in positional game theory.

On the complexity of alpha conversion

2007 ◽  
Vol 72 (4) ◽  
pp. 1197-1203
Author(s):  
Rick Statman
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Chordal Graphs ◽  
Vertex Coloring ◽  
Feedback Vertex Set ◽  
Vertex Set ◽  
Feedback Vertex Set Problem ◽  
The Way

AbstractWe consider three problems concerning alpha conversion of closed terms (combinators).(1) Given a combinator M find the an alpha convert of M with a smallest number of distinct variables.(2) Given two alpha convertible combinators M and N find a shortest alpha conversion of M to N.(3) Given two alpha convertible combinators M and N find an alpha conversion of M to N which uses the smallest number of variables possible along the way.We obtain the following results.(1) There is a polynomial time algorithm for solving problem (1). It is reducible to vertex coloring of chordal graphs.(2) Problem (2) is co-NP complete (in recognition form). The general feedback vertex set problem for digraphs is reducible to problem (2).(3) At most one variable besides those occurring in both M and N is necessary. This appears to be the folklore but the proof is not familiar. A polynomial time algorithm for the alpha conversion of M to N using at most one extra variable is given.There is a tradeoff between solutions to problem (2) and problem (3) which we do not fully understand.

OPTIMAL TRIANGULATIONS OF POINTS AND SEGMENTS WITH STEINER POINTS

2010 ◽  
Vol 20 (01) ◽  
pp. 89-104 ◽  
Author(s):  
BORIS ARONOV ◽  
TETSUO ASANO ◽  
STEFAN FUNKE
Keyword(s):  
Convex Hull ◽  
Polynomial Time ◽  
Natural Extension ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Optimal Location ◽  
Constant Number ◽  
Steiner Points ◽  
Vertex Set ◽  
Internal Angle

Consider a set X of points in the plane and a set E of non-crossing segments with endpoints in X. One can efficiently compute the triangulation of the convex hull of the points, which uses X as the vertex set, respects E, and maximizes the minimum internal angle of a triangle. In this paper we consider a natural extension of this problem: Given in addition a Steiner pointp, determine the optimal location of p and a triangulation of X ∪ {p} respecting E, which is best among all triangulations and placements of p in terms of maximizing the minimum internal angle of a triangle. We present a polynomial-time algorithm for this problem and then extend our solution to handle any constant number of Steiner points.

scholarly journals On the exact polynomial time algorithm for a special class of bimatrix game

2013 ◽  
Vol 54 ◽  
Author(s):  
Jonas Mockus ◽  
Martynas Sabaliauskas
Keyword(s):  
Game Theory ◽  
Linear Programming ◽  
Nash Equilibrium ◽  
Polynomial Time ◽  
Explicit Solution ◽  
Special Class ◽  
Polynomial Algorithm ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Bimatrix Game

The Strategy Elimination (SE) algorithm was proposed in [2] and implemented by a sequence of Linear Programming (LP) problems. In this paper an efficient explicit solution is developed and the convergence to the Nash Equilibrium is proven.Keywords: game theory, polynomial algorithm, Nash equilibrium.

scholarly journals On 2-Colorability Problem for Hypergraphs with P_8-free Incidence Graphs

2019 ◽  
Vol 17 (2) ◽  
pp. 257-263
Author(s):  
Ruzayn Quaddoura
Keyword(s):  
Polynomial Time ◽  
Dominating Set ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Incidence Graph ◽  
Coloring Problem ◽  
Vertex Set ◽  
Np Complete

A 2-coloring of a hypergraph is a mapping from its vertex set to a set of two colors such that no edge is monochromatic. The hypergraph 2- Coloring Problem is the question whether a given hypergraph is 2-colorable. It is known that deciding the 2-colorability of hypergraphs is NP-complete even for hypergraphs whose hyperedges have size at most 3. In this paper, we present a polynomial time algorithm for deciding if a hypergraph, whose incidence graph is P_8-free and has a dominating set isomorphic to C_8, is 2-colorable or not. This algorithm is semi generalization of the 2-colorability algorithm for hypergraph, whose incidence graph is P_7-free presented by Camby and Schaudt.

scholarly journals Recognizing generalized Sierpiński graphs

2020 ◽  
Vol 14 (1) ◽  
pp. 122-137
Author(s):  
Wilfried Imrich ◽  
Iztok Peterin
Keyword(s):  
Polynomial Time ◽  
Complete Graph ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Arbitrary Graph ◽  
Base Graph ◽  
Sierpiński Graphs ◽  
Vertex Set

Let H be an arbitrary graph with vertex set V (H) = [nH] = {l,?, nH}. The generalized Sierpi?ski graph SnH , n ? N, is defined on the vertex set [nH]n, two different vertices u = un ?u1 and v = vn ? v1 being adjacent if there exists an h? [n] such that (a) ut = vt, for t > h, (b) uh ? vh and uhvh ? E(H), and (c) ut = vh and vt = uh for t < h. If H is the complete graph Kk, then we speak of the Sierpi?ski graph Sn k . We present an algorithm that recognizes Sierpi?ski graphs Sn k in O(|V (Sn k )|1+1=n) = O(|E(Sn k )|) time. For generalized Sierpi?ski graphs SnH we present a polynomial time algorithm for the case when H belong to a certain well defined class of graphs. We also describe how to derive the base graph H from an arbitrarily given SnH .

Learning Importance of Preferences

10.29007/v68w ◽  
2018 ◽  
Author(s):  
Ying Zhu ◽  
Mirek Truszczynski
Keyword(s):  
Polynomial Time ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Scoring Rules ◽  
Np Hard ◽  
Individual Preferences

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.

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