Among various variants of the traveling salesman problem (TSP), the s-t-path graph TSP has the special feature that we know the exact integrality ratio, 3/2, and an approximation algorithm matching this ratio. In this...
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Among various variants of the traveling salesman problem (TSP), the s-t-path graph TSP has the special feature that we know the exact integrality ratio, 3/2, and an approximation algorithm matching this ratio. In this paper, we go below this threshold: we devise a polynomialtime algorithm for the s-t-path graph TSP with approximation ratio 1.497. Our algorithm can be viewed as a refinement of the 3/2 -approximation algorithm in [A. Seb\H o and J. Vygen, Combinatorica, 34 (2014), pp. 597--629], but we introduce several completely new techniques. These include a new type of ear-decomposition, an enhanced ear induction that reveals a novel connection to matroid union, a stronger lower bound, and a reduction of general instances to instances in which s and t have small distance (which works for general metrics).
Research in VLSI placement, an NP-hard problem, has branched in two different directions. The first one employs iterative heuristics with many tunable parameters to produce a near-optimal solution but without theoreti...
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Research in VLSI placement, an NP-hard problem, has branched in two different directions. The first one employs iterative heuristics with many tunable parameters to produce a near-optimal solution but without theoretical guarantee on its quality. The other one considers placement as a graph-embedding problem and designs approximation algorithms with provable bounds on the quality of the solution. In this article, we aim at unifying the above two directions. First, we extend the existing approximation algorithms for graph embedding in 1D and 2D grid to those for hypergraphs, which typically model circuits to be placed on a FPGA. We prove an approximation bound of O(d root logn log log n) for 1D, that is, linear arrangement and O(d log nlog log n) for the 2D grid, where d is the maximum degree of hyperedges and n, the number of vertices in the hypergraph. Next, we propose an efficient method based on linear arrangement of the CLBs and the notion of space-filling curves for placing the configurable logic blocks (CLBs) of a netlist on island-style FPGAs with an approximation guarantee of O((4)root log n root kd log log n), where k is the number of nets. For the set of FPGA placement benchmarks, the running time is near linear in the number of CLBs thus allowing for scalability towards large circuits. We obtained a 33x speed-up, on average, with only 1.31x degradation in the quality of the solution compared to that produced by the popular FPGA tool VPR, thereby demonstrating the suitability of this very fast method for FPGA placement, with a provable performance guarantee.
Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data ...
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Maximizing a submodular function has a wide range of applications in machine learning and data mining. One such application is data summarization whose goal is to select a small set of representative and diverse data items from a large dataset. However, data items might have sensitive attributes such as race or gender, in this setting, it is important to design fairness-aware algorithms to mitigate potential algorithmic bias that may cause over- or under- representation of particular groups. Motivated by that, we propose and study the classic non-monotone submodular maximization problem subject to novel group fairness constraints. Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker. We develop the first constant-factor approximation algorithms for this problem. We also extend the basic model to incorporate an additional global size constraint on the total number of selected items.
Recently, wireless power transfer technology (WPT) has attracted considerable attention and become a promising technology to prolong the lifetime of wireless sensor networks (WSNs) by providing perpetual energy to sen...
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Recently, wireless power transfer technology (WPT) has attracted considerable attention and become a promising technology to prolong the lifetime of wireless sensor networks (WSNs) by providing perpetual energy to sensors. However, electromagnetic radiation (EMR) incurred by WPT is largely overlooked in most of the existing literature. In this article, we first propose and study the radiation constrained fair charging problem for WPT, i.e., maximizing the minimum utility of sensors by adjusting the power of wireless chargers with no EMR intensity at any location in the field exceeding a given threshold R-t. To address this problem, we first adopt an area discretization method to transform it from nonlinear to linear. Then, we propose four algorithms to deal with the reformulated problem, i.e., 1/3 and 1/4 approximation algorithms, Primal-Dual algorithm, and area division algorithm. In particular, the area division algorithm is not only fully distributed but also provably achieves an approximation ratio of (1 - epsilon). Further, we conduct extensive simulations and build a field testbed to verify our theoretical findings. Our simulation results show that the approximation ratios of the proposed algorithms hold;the Primal-Dual and area division algorithms have comparable performance of the optimal results and outperform baseline algorithms obviously.
The resource constraints and accuracy requirements for Internet of Things (IoT) memory chips need threedimensional (3D) monolithic integrated circuits, of which the increasing stack layers (currently more than 176) al...
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The resource constraints and accuracy requirements for Internet of Things (IoT) memory chips need threedimensional (3D) monolithic integrated circuits, of which the increasing stack layers (currently more than 176) also cause excessive energy consumption and increasing wire length. In this paper, we propose and describe the channel modeling for wireless 3D chips based on ray-tracing. However, due to the reflection and refraction characteristics in each layer, the complex and diverse wireless paths in 3D chips add great difficulty to the channel characterization. To facilitate the modeling in massive layer wireless 3D chips based on ray-tracing, both boundary-less and boundary-constrained wireless 3D chips models are proposed based on ray-tracing, of which the channel gain can be obtained by a computational efficient approximate algorithm. These wireless 3D models with approximation algorithm can well characterize the wireless 3D chip channel in aspect of complete reflection and refraction characteristics, and avoid massive wired connections, high power consumption of cross-layer communication and high-complexity of 3D chips channel characterization. Numerical results show that: (1) The difference rate between the two models is lower than 0.001% (signal transmit through 20 layers);(2) the channel gain decreases sharply if refract time increases;and (3) the approximate algorithm can achieve an acceptable accuracy (error rate lower than 0.1%).
We prove almost optimal hardness for MAX k-CSPR. In MAX k-CSPR, we are given a set of constraints, each of which depends on at most k variables. Each variable can take any value from 1, 2,. .., R. The goal is to find ...
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We prove almost optimal hardness for MAX k-CSPR. In MAX k-CSPR, we are given a set of constraints, each of which depends on at most k variables. Each variable can take any value from 1, 2,. .., R. The goal is to find an assignment to variables that maximizes the number of satisfied constraints. We show that, for any k >= 2 and R >= 16, it is NP-hard to approximate MAX k- CSPR to within factor k(O(k))(logR)(k/2) =Rk-1. In the regime where 3 <= k = o(logR= log logR), this ratio improves upon Chan's O(k/Rk-2) factor NP-hardness of approximation of MAX k-CSPR (J. ACM 2016). Moreover, when k = 2, our result matches the best known hardness result of Khot, Kindler, Mossel and O'Donnell (SIAM J. Comp. 2007). We remark here that NPhardness of an approximation factor of 2(O(k)) log(kR)/Rk-1 is implicit in the (independent) work of Khot and Saket (ICALP 2015), which is better than our ratio for all k >= 3. In addition to the above hardness result, by extending an algorithm for MAX 2- CSPR by Kindler, Kolla and Trevisan (SODA 2016), we provide an Omega(logR/Rk-1)-approximation algorithm for MAX k-CSPR. Thanks to Khot and Saket's result, this algorithm is tight up to a factor of O(k(2)) when k <= R-O(1). In comparison, when 3 <= k is a constant, the previously best known algorithm achieves an O(k/Rk-1)-approximation for the problem, which is a factor of O(k logR) from the inapproximability ratio in contrast to our gap of O(k(2)).
Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best-known algorithm for k-means in Euclidean space with...
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Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best-known algorithm for k-means in Euclidean space with a provable guarantee is a simple local search heuristic yielding an approximation guarantee of 9+epsilon, a ratio that is known to be tight with respect to such methods. We overcome this barrier by presenting a new primal-dual approach that allows us to (1) exploit the geometric structure of k-means and (2) satisfy the hard constraint that at most k clusters are selected without deteriorating the approximation guarantee. Our main result is a 6.357-approximation algorithm with respect to the standard linear programming (LP) relaxation. Our techniques are quite general, and we also show improved guarantees for k-median in Euclidean metrics and for a generalization of k-means in which the underlying metric is not required to be Euclidean.
This paper investigates a bounded mixed batch scheduling problem with job release dates and rejection. The machine processes a batch containing several jobs that their number does not exceed the machine capacity. For ...
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This paper investigates a bounded mixed batch scheduling problem with job release dates and rejection. The machine processes a batch containing several jobs that their number does not exceed the machine capacity. For the case with a certain release date, we present a polynomial-time exact algorithm. For the case with a constant number of release dates, a pseudo-polynomial-time exact algorithm is proposed. For the general problem, we provide a 2-approximation algorithm and a polynomial-time approximation scheme. (c) 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
We combine two fundamental optimization problems related to the construction of phylogenetic trees called maximum rooted triplets consistency and minimally resolved supertree into a new problem, which we call q-maximu...
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We combine two fundamental optimization problems related to the construction of phylogenetic trees called maximum rooted triplets consistency and minimally resolved supertree into a new problem, which we call q-maximum rooted triplets consistency (q-MAXRTC). It takes as input a set R of rooted, binary phylogenetic trees with three leaves each and asks for a phylogenetic tree with exactly q internal nodes that contains the largest possible number of trees from R. We prove that q-MAXRTC is NP-hard to approximate within a constant, develop polynomial-time approximation algorithms for different values of q, and show experimentally that representing a phylogenetic tree by one having much fewer nodes typically does not destroy too much branching information. To demonstrate the algorithmic advantage of using trees with few internal nodes, we also propose a new algorithm for computing the rooted triplet distance that is faster than the existing algorithms when restricted to such trees.
We consider the problem of finding a low-cost allocation and ordering of tasks between a team of robots in a d-dimensional, uncertain, landscape, and the sensitivity of this solution to changes in the cost *** algorit...
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We consider the problem of finding a low-cost allocation and ordering of tasks between a team of robots in a d-dimensional, uncertain, landscape, and the sensitivity of this solution to changes in the cost *** algorithms have been shown to give a 2-approximation to the MIN$UM allocation problem. By analysing such an auction algorithm, we obtain intervals on each cost, such that any fluctuation of the costs within these intervals will result in the auction algorithm outputting the same solution.& COPY;2023 Published by Elsevier Ltd.
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