arXiv subjects

Vladimir Shenmaier

Publications and source records attributed to Vladimir Shenmaier.

5 recordsLinked to original sources

arXiv2022

Asymptotic Optimality of the Greedy Patching Heuristic for Max TSP in Doubling Metrics

The maximum traveling salesman problem (Max~TSP) consists of finding a Hamiltonian cycle with the maximum total weight of the edges in a given complete weighted graph. We prove that, in the case when the edge weights are induced by a metric space of bounded doubling dimension, asymptotically optimal solutions of the problem can be found by the simple greedy patching heuristic. Taking as a start point a maximum-weight cycle cover, this heuristic iteratively patches pairs of its cycles into one minimizing the weight loss at each step.

cs.DS↗

arXiv2021

On the Complexity of the Geometric Median Problem with Outliers

In the Geometric Median problem with outliers, we are given a finite set of points in d-dimensional real space and an integer m, the goal is to locate a new point in space (center) and choose m of the input points to minimize the sum of the Euclidean distances from the center to the chosen points. This problem can be solved "almost exactly" in polynomial time if d is fixed and admits an approximation scheme PTAS in high dimensions. However, the complexity of the problem was an open question. We prove that, if the dimension of space is not fixed, Geometric Median with outliers is strongly NP-hard, does not admit approximation schemes FPTAS unless P=NP, and is W[1]-hard with respect to the parameter m. The proof is done by a reduction from the Independent Set problem. Based on a similar reduction, we also get the NP-hardness of closely related geometric 2-clustering problems in which it is required to partition a given set of points into two balanced clusters minimizing the cost of median clustering. Finally, we study Geometric Median with outliers in $\ell_\infty$ space and prove the same complexity results as for the Euclidean problem.

cs.CG↗

arXiv2021

Linear-Size Universal Discretization of Geometric Center-Based Problems in Fixed Dimensions

Many geometric optimization problems can be reduced to finding points in space (centers) minimizing an objective function which continuously depends on the distances from the centers to given input points. Examples are $k$-Means, Geometric $k$-Median/Center, Continuous Facility Location, $m$-Variance, etc. We prove that, for any fixed $\varepsilon>0$, every set of $n$ input points in fixed-dimensional space with the metric induced by any vector norm admits a set of $O(n)$ candidate centers which can be computed in almost linear time and which contains a $(1+\varepsilon)$-approximation of each point of space with respect to the distances to all the input points. It gives a universal approximation-preserving reduction of geometric center-based problems with arbitrary continuity-type objective functions to their discrete versions where the centers are selected from a fairly small set of candidates. The existence of such a linear-size set of candidates is also shown for any metric space of fixed doubling dimension.

cs.CG↗

arXiv2021

Efficient PTAS for the Maximum Traveling Salesman Problem in a Metric Space of Fixed Doubling Dimension

The maximum traveling salesman problem (Max TSP) consists of finding a Hamiltonian cycle with the maximum total weight of the edges in a given complete weighted graph. This problem is APX-hard in the general metric case but admits polynomial-time approximation schemes in the geometric setting, when the edge weights are induced by a vector norm in fixed-dimensional real space. We propose the first approximation scheme for Max TSP in an arbitrary metric space of fixed doubling dimension. The proposed algorithm implements an efficient PTAS which, for any fixed $\varepsilon\in(0,1)$, computes a $(1-\varepsilon)$-approximate solution of the problem in cubic time. Additionally, we suggest a cubic-time algorithm which finds asymptotically optimal solutions of the metric Max TSP in fixed and sublogarithmic doubling dimensions.

cs.DS↗

arXiv2021

An Improved Approximation for Packing Big Two-Bar Charts

Recently, we presented a new Two-Bar Charts Packing Problem (2-BCPP), in which it is necessary to pack two-bar charts (2-BCs) in a unit-height strip of minimum length. The problem is a generalization of the Bin Packing Problem and 2-D Vector Packing Problem. Earlier, we have proposed several polynomial approximation algorithms. In particular, when each 2-BC has at least one bar of height more than 1/2, we have proposed a 3/2--approximation polynomial algorithm. This paper proposes an $O(n^3)$--time 16/11--approximation algorithm for packing 2-BCs when at least one bar of each BC has a height not less than 1/2 and an $O(n^{2.5})$--time 5/4--approximation algorithm for packing non-increasing or non-decreasing 2-BCs when each 2-BC has at least one bar which height is more than 1/2, where $n$ is the number of 2-BCs.

cs.CG↗