246 research outputs found
On a generalization of iterated and randomized rounding
We give a general method for rounding linear programs that combines the
commonly used iterated rounding and randomized rounding techniques. In
particular, we show that whenever iterated rounding can be applied to a problem
with some slack, there is a randomized procedure that returns an integral
solution that satisfies the guarantees of iterated rounding and also has
concentration properties. We use this to give new results for several classic
problems where iterated rounding has been useful
New Inapproximability Bounds for TSP
In this paper, we study the approximability of the metric Traveling Salesman
Problem (TSP) and prove new explicit inapproximability bounds for that problem.
The best up to now known hardness of approximation bounds were 185/184 for the
symmetric case (due to Lampis) and 117/116 for the asymmetric case (due to
Papadimitriou and Vempala). We construct here two new bounded occurrence CSP
reductions which improve these bounds to 123/122 and 75/74, respectively. The
latter bound is the first improvement in more than a decade for the case of the
asymmetric TSP. One of our main tools, which may be of independent interest, is
a new construction of a bounded degree wheel amplifier used in the proof of our
results
Combinatorial Optimization
Combinatorial Optimization is an active research area that developed from the rich interaction among many mathematical areas, including combinatorics, graph theory, geometry, optimization, probability, theoretical computer science, and many others. It combines algorithmic and complexity analysis with a mature mathematical foundation and it yields both basic research and applications in manifold areas such as, for example, communications, economics, traffic, network design, VLSI, scheduling, production, computational biology, to name just a few. Through strong inner ties to other mathematical fields it has been contributing to and benefiting from areas such as, for example, discrete and convex geometry, convex and nonlinear optimization, algebraic and topological methods, geometry of numbers, matroids and combinatorics, and mathematical programming. Moreover, with respect to applications and algorithmic complexity, Combinatorial Optimization is an essential link between mathematics, computer science and modern applications in data science, economics, and industry
The Geometric Maximum Traveling Salesman Problem
We consider the traveling salesman problem when the cities are points in R^d
for some fixed d and distances are computed according to geometric distances,
determined by some norm. We show that for any polyhedral norm, the problem of
finding a tour of maximum length can be solved in polynomial time. If
arithmetic operations are assumed to take unit time, our algorithms run in time
O(n^{f-2} log n), where f is the number of facets of the polyhedron determining
the polyhedral norm. Thus for example we have O(n^2 log n) algorithms for the
cases of points in the plane under the Rectilinear and Sup norms. This is in
contrast to the fact that finding a minimum length tour in each case is
NP-hard. Our approach can be extended to the more general case of quasi-norms
with not necessarily symmetric unit ball, where we get a complexity of
O(n^{2f-2} log n).
For the special case of two-dimensional metrics with f=4 (which includes the
Rectilinear and Sup norms), we present a simple algorithm with O(n) running
time. The algorithm does not use any indirect addressing, so its running time
remains valid even in comparison based models in which sorting requires Omega(n
\log n) time. The basic mechanism of the algorithm provides some intuition on
why polyhedral norms allow fast algorithms.
Complementing the results on simplicity for polyhedral norms, we prove that
for the case of Euclidean distances in R^d for d>2, the Maximum TSP is NP-hard.
This sheds new light on the well-studied difficulties of Euclidean distances.Comment: 24 pages, 6 figures; revised to appear in Journal of the ACM.
(clarified some minor points, fixed typos
Local Search for the Resource Constrained Assignment Problem
The resource constrained assignment problem (RCAP) is to find a minimal cost cycle partition in a directed graph such that a resource constraint is fulfilled. The RCAP has its roots in an application that deals with the covering of a railway timetable by rolling stock vehicles. Here, the resource constraint corresponds to maintenance constraints for rail vehicles. Moreover, the RCAP generalizes several variants of vehicle routing problems. We contribute a local search algorithm for this problem that is derived from an exact algorithm which is similar to the Hungarian method for the standard assignment problem. Our algorithm can be summarized as a k-OPT heuristic, exchanging k arcs of an alternating cycle of the incumbent solution in each improvement step. The alternating cycles are found by dual arguments from linear programming. We present computational results for instances from our railway application at Deutsche Bahn Fernverkehr AG as well as for instances of the vehicle routing problem from the literature
On a generalization of iterated and randomized rounding
We give a general method for rounding linear programs that combines the commonly used iterated rounding and randomized rounding techniques. In particular, we show that whenever iterated rounding can be applied to a problem with some slack, there is a randomized procedure that returns an integral solution that satisfies the guarantees of iterated rounding and also has concentration properties. We use this to give new results for several classic problems such as rounding column-sparse LPs, makespan minimization on unrelated machines, degree-bounded spanning trees and multi-budgeted matchings
A Constant-Factor Approximation for Directed Latency in Quasi-Polynomial Time
We give the first constant-factor approximation for the Directed Latency
problem in quasi-polynomial time. Here, the goal is to visit all nodes in an
asymmetric metric with a single vehicle starting at a depot to minimize the
average time a node waits to be visited by the vehicle. The approximation
guarantee is an improvement over the polynomial-time -approximation
[Friggstad, Salavatipour, Svitkina, 2013] and no better quasi-polynomial time
approximation algorithm was known.
To obtain this, we must extend a recent result showing the integrality gap of
the Asymmetric TSP-Path LP relaxation is bounded by a constant [K\"{o}hne,
Traub, and Vygen, 2019], which itself builds on the breakthrough result that
the integrality gap for standard Asymmetric TSP is also a constant [Svensson,
Tarnawsi, and Vegh, 2018]. We show the standard Asymmetric TSP-Path integrality
gap is bounded by a constant even if the cut requirements of the LP relaxation
are relaxed from to
for some constant . We also give a better approximation
guarantee in the special case of Directed Latency in regret metrics where the
goal is to find a path minimize the average time a node waits in excess
of , i.e.
Reformulation and decomposition of integer programs
In this survey we examine ways to reformulate integer and mixed integer programs. Typically, but not exclusively, one reformulates so as to obtain stronger linear programming relaxations, and hence better bounds for use in a branch-and-bound based algorithm. First we cover in detail reformulations based on decomposition, such as Lagrangean relaxation, Dantzig-Wolfe column generation and the resulting branch-and-price algorithms. This is followed by an examination of Benders’ type algorithms based on projection. Finally we discuss in detail extended formulations involving additional variables that are based on problem structure. These can often be used to provide strengthened a priori formulations. Reformulations obtained by adding cutting planes in the original variables are not treated here.Integer program, Lagrangean relaxation, column generation, branch-and-price, extended formulation, Benders' algorithm
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