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Universal Approximations for TSP, Steiner Tree, and Set Cover. Authors: Lujun Jia, Guolong Lin, Guevara Noubir, Rajmohan Rajaraman, Ravi Sundaram Presented by Songjian Lu. contents. Introduction Definition of UTSP,UST,USC Detail of UTSP Other result. Introduction.

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Universal approximations for tsp steiner tree and set cover

Universal Approximations forTSP, Steiner Tree, and Set Cover

Authors: Lujun Jia, Guolong Lin, Guevara Noubir,

Rajmohan Rajaraman, Ravi Sundaram

Presented by Songjian Lu


Contents
contents

  • Introduction

  • Definition of UTSP,UST,USC

  • Detail of UTSP

  • Other result


Introduction
Introduction

  • Consider a courier who delivers packages to different houses and businesses in a city every day. One challenge faced by the courier is to determine a suitable route every day, given the packages to be delivered that day.


UTSP

  • Universal traveling salesman problem

  • DEFINITION:(UTSP).An instance of UTSP is a metric space (V,d). For any cycle(tour) C containing all the vertices in V and a subset S of V, Let CS denote the unique cycle over S in which the ordering of vertices in S is consistent with their ordering in V. The stretch of C is defined as maxSV ||Cs||/||OptTrS||, where OptTrS denotes the minimum cost tour on set S. The universal traveling salesman problem is to find a tour on V with minimum stretch.


UST

  • Universal steiner trees

  • Definition:(UST) An instance of the Universal steiner tree problem is a triple <V,d,r>, where (V,d) forms a metric space, and r is a distinguished vertex in V that we refer to as the root. For any spanning tree T of V, define the stretch of T as maxSV||Ts+r||/||OptSts+r, The goal of the UST problem is to determine a spanning tree with minimum stretch.


USC

  • Universal Set Cover

  • Definition:(USC). An instance of USC is a triple <U,C,c>, where U={e1,e2,…,en} is a ground set of elements, C={S1,S2,…,Sm} is a collection of sets, and c is a cost function mapping C to Q+. We define an assignment f as a function from U to C that satisfies ef(e) for all e in U. We extend the definition of f to apply to any SU as follows: f(S)={f(e):e  S}. We next define the cost of f(S), ||f(S)||, as  SU c(x) for all x in f(S), and the stretch of an assignment f as max SU c(f(S))/Opt(S), where Opt(S) is the cost of the optimal set cover solution to S. And the goal is to compute an assignment f with minimum stretch.


Utsp detail 1
UTSP detail-1

  • Definition: ((r,,I)-PARTITION). A (r,,I)-partition of a metric space (V,d) is a partition {Si} of V such that (i) the diameter of every set Si in the partition is at most r and (ii) for every node vV, the ball Br(v) intersects at most I set in the partition, where Br(v)={uV|d(u,v)r}.

  • A (,I)-partition scheme is a procedure that computes a (r,,I)-partition for any r>0.


Utsp detail 2
UTSP detail-2

  • Lemma 1: The general metric spaces has a (r,log(n),log(n))-partition that can be computed efficiently for any r>0.

  • Lemma 2: For a doubling metric space (V,d) with doubling constant , a (r,1,3)-partition can be computed efficiently for any r>0.


Utsp detail 3
UTSP detail-3

  • Lemma 3: If the points are in k-dimensional Euclidean space, a (r,k1/2(2+),2k)-partition can be computed efficiently for any r>0, >0.

  • Lemma 5: For a growth-restricted metric space (V,d) with expansion rate c, a (r,4,c3)-partition can be computed efficiently for any r>0.


Utsp detail 5
UTSP detail-5

  • Theorem 1: Given a metric space (V,d) with n vertices and a (,I)-partitioning scheme, UTSP-ALG returns a tour with stretch O(2I log n) in polynomial time.


Utsp detail 6
UTSP detail-6

  • Corollary 1: For any metric space over n vertices, the algorithm UTSP-ALG returns a tour with stretch O(log4(n)/log(log(n))).

  • Corollary 2: For any doubling. Euclidean, or growth-restricted metric space over n vertices, UTSP-ALG return a tree with stretch O(log(n)).


Utsp detail 61
UTSP detail-6

  • UTSP-ALG runing in p time.

  • For j 1, let PSj be a maxima subset of vertices S that are pairwise separated by distance at least j-1.

  • Lemma 6: For j2, the cost of edges in Ts at level j is at most |PSj-1|jI.

  • Lemma 7: If mi 1, i=1,…,k,c 2, then 1 i  kmici  (logc(max1 i  k mi)+3) max1 i  k (mi ci)


Other result 1
Other result-1

  • Universal steiner trees

  • Theorem 2: Given a metric space (V,d) with n vertices, a root rV, and a (,I)-partitioning scheme for (V,d), a spanning tree with stretch O(2I log(n)) can be constructed in polynomial time.

  • Corollary 3: For any metric space over n vertices, UST-ALG returns a tree with stretch O(log4(n)/loglog(n)).

  • Corollary 4: For any doubling, Euclidean, or growth-restricted metric space over n vertices, UST-ALG returns a tree with stretch O(log(n)).


Other result 2
Other result-2

  • Theorem 3: There exists a set of n vertices in two-dimensional Euclidean space, for which every spanning tree has an (log(n)/loglog(n)) stretch.

  • Theorem 4: No on-line algorithm can achieve a competitive ratio which is better than (log(n)/loglog(n)) for the Steiner tree problem of n vertices in vertices in the plane, or even for n vertices in the n by n grid.


Other result 3
Other result-3

  • Theorem 5: For any USC instance with n elements, USC-ALG has a stretch of O((nln(n))1/2).

  • Theorem 6: There exists an n-element instance of USC with uniform costs for which the best stretch achievable is ( n1/2).


Homework 2
Homework-2

  • Try to find a natural problem and define it to be a universal approximation problem.



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