This thesis presents linear and convex programming based algorithms for NP-hard discrete optimization problems, mainly with applications in network design. Network design problems aim to find a minimal/maximal weighte...
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This thesis presents linear and convex programming based algorithms for NP-hard discrete optimization problems, mainly with applications in network design. Network design problems aim to find a minimal/maximal weighted subgraph satisfying given properties. The problems studied include maximum cut, buy-at-bulk network design, throughput maximization, and unrelated machine scheduling. This thesis considers different models of input uncertainty: the traditional deterministic setting, the online setting where inputs arrive over time and the stochastic setting where inputs are drawn from some probability distribution. Our approach to these problems involves solving suitable convex relaxations and then using rounding procedures to convert the fractional solutions to integer solutions. The specific contributions of this thesis include (1) approximation algorithms for a constrained variant of the maximum cut problem using the Sherali-Adams LP hierarchy; (2) online primal-dual algorithms for covering and packing with Lq norm objectives; (3) approximation algorithms for stochastic unrelated machine scheduling.
We consider the dynamic map labeling problem: given a set of rectangular labels on the map, the goal is to appropriately select visible ranges for all the labels such that no two consistent labels overlap at every sca...
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We consider the dynamic map labeling problem: given a set of rectangular labels on the map, the goal is to appropriately select visible ranges for all the labels such that no two consistent labels overlap at every scale and the sum of total visible ranges is maximized. This is also called the active range optimization (ARO) problem defined by Been et al. (2006) [2]. We propose approximation algorithms for several variants of this problem. For the simple ARO problem, we provide a 3c logn-approximation algorithm for unit-width rectangular labels if there is a c-approximation algorithm for the unit-width label placement problem in the plane;and a randomized polynomial-time O(logn log logn)-approximation algorithm for arbitrary rectangular labels. For the general ARO problem, we prove that it remains NP-complete even for congruent square labels with equal selectable scale range. Moreover, we contribute 12-approximation algorithms for both arbitrary square labels and unit-width rectangular labels, and a 6-approximation algorithm for congruent square labels, and show that the bounds are tight. (C) 2016 Elsevier B.V. All rights reserved.
We study general resource allocation problems with a diseconomy of scale. Given a finite set of commodities that request certain resources, the cost of each resource grows superlinearly with the demand for it, and our...
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ISBN:
(纸本)9781450345934
We study general resource allocation problems with a diseconomy of scale. Given a finite set of commodities that request certain resources, the cost of each resource grows superlinearly with the demand for it, and our goal is to minimize the total cost of the resources. In large systems with limited coordination, it is natural to consider local dynamics where in each step a single commodity switches its allocated resources whenever the new solution after the switch has smaller total cost over all commodities. This yields a deterministic and polynomial time algorithm with approximation factor arbitrarily close to the locality gap, i.e., the worst case ratio of the cost of a local optimal and a global optimal solution. For costs that are polynomials with non-negative coefficients and maximal degree d, we provide a locality gap for weighted problems that is tight for all values of d. For unweighted problems, the locality gap asymptotically matches the approximation guarantee of the currently best known centralized algorithm [Makarychev, Srividenko FOCS14] but only requires local knowledge of the commodities.
We study the Weighted t-Uniform Sparsest Cut (Weighted t-USC) and other related problems. In an instance of the Weighted t-USC problem, a parameter t and an undirected graph G = (V, E) with edge-weights w : E -> 1R...
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We study the Weighted t-Uniform Sparsest Cut (Weighted t-USC) and other related problems. In an instance of the Weighted t-USC problem, a parameter t and an undirected graph G = (V, E) with edge-weights w : E -> 1R(>=)0 and vertex-weights : V -> R+ are given. The goal is to find a vertex set S subset of V with vertical bar S vertical bar <= t while minimizing w(S, V\S)/eta(S), where w(S, V \ S) is the total weight of the edges with exactly one endpoint in S and eta(S) = Sigma(v is an element of S) eta(v) For this problem, we present a (0 (log t), 1 + is an element of) factor bicriteria approximation lgorithm. Our algorithm outperforms the current best algorithm when t = n(o(1)). We also present better approximation algorithms for Weighted rho-Unbalanced Cut and Min-Max k-Partitioning problems. (C) 2016 Elsevier Inc. All rights reserved.
The notion of penalty has been introduced into many combinatorial optimization models. In this paper, we consider the submodular vertex cover problems with linear and submodular penalties, which are two variants of th...
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The notion of penalty has been introduced into many combinatorial optimization models. In this paper, we consider the submodular vertex cover problems with linear and submodular penalties, which are two variants of the submodular vertex cover problem where not all the edges are required to be covered by a vertex cover, and the uncovered edges are penalized. The problem is to determine a vertex subset to cover some edges and penalize the uncovered edges such that the total cost including covering and penalty is minimized. To overcome the difficulty of implementing the primal-dual framework directly, we relax the two dual programs to slightly weaker versions. We then present two primal dual approximation algorithms with approximation ratios of 2 and 4, respectively. (C) 2016 Elsevier B.V. All rights reserved.
The Chordal Vertex Deletion (ChVD) problem asks to delete a minimum number of vertices from an input graph to obtain a chordal graph. In this paper we develop a polynomial kernel for ChVD under the parameterization by...
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The Chordal Vertex Deletion (ChVD) problem asks to delete a minimum number of vertices from an input graph to obtain a chordal graph. In this paper we develop a polynomial kernel for ChVD under the parameterization by the solution size. Using a new Erdos-Posa-type packing/covering duality for holes in nearly chordal graphs, we present a polynomial-time algorithm that reduces any instance (G, k) of ChVD to an equivalent instance with poly(k) vertices. The existence of a polynomial kernel answers an open problem posed by Marx in 2006 [ D. Marx, "Chordal Deletion Is Fixed-Parameter Tractable," in Graph-Theoretic Concepts in Computer Science, Lecture Notes in Comput. Sci. 4271, Springer, 2006, pp. 37-48]. To obtain the kernelization, we develop the first poly(opt)-approximation algorithm for ChVD, which is of independent interest. In polynomial time, it either decides that G has no chordal deletion set of size k, or outputs a solution of size O(k(4) log(2) k).
In the Minimum Weight Partial Connected Set Cover problem, we are given a finite ground set , an integer , a collection of subsets of , and a connected graph on vertex set , the goal is to find a minimum weight subcol...
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In the Minimum Weight Partial Connected Set Cover problem, we are given a finite ground set , an integer , a collection of subsets of , and a connected graph on vertex set , the goal is to find a minimum weight subcollection of which covers at least elements of and induces a connected subgraph in . In this paper, we derive a "partial cover property" for the greedy solution of the Minimum Weight Set Cover problem, based on which we present (a) for the weighted version under the assumption that any pair of sets in with nonempty intersection are adjacent in (the Minimum Weight Partial Connected Vertex Cover problem falls into this range), an approximation algorithm with performance ratio , and (b) for the cardinality version under the assumption that any pair of sets in with nonempty intersection are at most -hops away from each other (the Minimum Partial Connected -Hop Dominating Set problem falls into this range), an approximation algorithm with performance ratio , where , is the Harmonic number, and is the performance ratio for the Minimum Quota Node-Weighted Steiner Tree problem.
We consider a joint resource partition and scheduling problem. We are given m identical cores and discrete resources of total size k. We need to partition the resources among these cores. A set of jobs must be process...
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We consider a joint resource partition and scheduling problem. We are given m identical cores and discrete resources of total size k. We need to partition the resources among these cores. A set of jobs must be processed non-preemptively on these cores after the resource partition. The processing time of a job on a core depends on the size of resources allocated to that corresponding core. The resource allocation scheme is static, i.e., we cannot change the amount of resources that was allocated to a core during the whole scheduling. Hassidim et al. (2013) investigated this problem with a general processing time function, i.e., the processing time of a job is an arbitrary function of the level of resources allocated to that core. They provided an algorithm with approximation ratio of 36. In this paper, we improve the approximation ratio to 8 by presenting a new resource partition scheme. Next, we consider a special model where the core's speed is proportional to its allocated resource, then we present two algorithms with improved approximation ratios. (C) 2016 Elsevier B.V. All rights reserved.
The two-dimensional strip packing problem (2D-SPP) involves packing a set R = { r 1,..., r n } of n rectangular items into a strip of width 1 and unbounded height, where each rectangular item r i has width 0 < w i ...
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The two-dimensional strip packing problem (2D-SPP) involves packing a set R = { r 1,..., r n } of n rectangular items into a strip of width 1 and unbounded height, where each rectangular item r i has width 0 < w i ≤ 1 and height 0 < h i ≤ 1. The objective is to find a packing for all these items, without overlaps or rotations, that minimizes the total height of the strip used. 2D-SPP is strongly NP -hard and has practical applications including stock cutting, scheduling, and reducing peak power demand in smart-grids. This thesis considers a special case of 2D-SPP in which the set of rectangular items R has three distinct rectangle sizes or types. We present a new OPT + 5/3 polynomial-time approximation algorithm, where OPT is the value of an optimum solution. This algorithm is an improvement over the previously best OPT + 2 polynomial-time approximation algorithm for the problem.
A vertex subset C of a connected graph G is called a connected k-path vertex cover (CVCPk) if every path on k vertices contains at least one vertex from C, and the subgraph of G induced by C is connected. This concept...
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A vertex subset C of a connected graph G is called a connected k-path vertex cover (CVCPk) if every path on k vertices contains at least one vertex from C, and the subgraph of G induced by C is connected. This concept originated in the field of security and supervisory control. This paper studies the minimum (weight) CVCPk problem. We first show that the minimum weight CVCPk problem can be solved in time O(n) when the graph is a tree, and can be solved in time O(m) when the graph is a uni-cyclic graph whose unique cycle has length r, where n is the number of vertices. Making use of the algorithm on trees, we present a k-approximation algorithm for the minimum (cardinality) CVCPk problem under the assumption that the graph has girth at least k. An example is given showing that performance ratio k is asymptotically tight for our algorithm. (C) 2015 Elsevier B.V. All rights reserved.
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