Filters








1,391 Hits in 4.7 sec

Algorithms and Adaptivity Gaps for Stochastic k-TSP

Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla, Michael Wagner
2020 Innovations in Theoretical Computer Science  
We totally resolve their open question, and even give an O(1)-approximation non-adaptive algorithm for Stoch-Reward k-TSP.  ...  Ene et al. give an O(log k)-approximation adaptive algorithm for this problem, and left open if there is an O(1)-approximation algorithm.  ...  Jian Li and Daogao Liu are supported in part by the National Natural Science Foundation of China Grant 61822203, 61772297, 61632016, 61761146003, and the Zhongguancun Haihua Institute for Frontier Information  ... 
doi:10.4230/lipics.itcs.2020.45 dblp:conf/innovations/JiangLL020 fatcat:trg6ql76wfgbtfz4wrryqlej6y

Algorithms and Adaptivity Gaps for Stochastic k-TSP [article]

Haotian Jiang, Jian Li, Daogao Liu, Sahil Singla
2019 arXiv   pre-print
We totally resolve their open question and even give an O(1)-approximation non-adaptive algorithm for this problem. We also introduce and obtain similar results for the Stoch-Cost k-TSP problem.  ...  Ene et al. give an O(log k)-approximation adaptive algorithm for this problem, and left open if there is an O(1)-approximation algorithm.  ...  Jian Li and Daogao Liu are supported in part by the National Natural Science Foundation of China Grant 61822203, 61772297, 61632016, 61761146003, and the Zhongguancun Haihua Institute for Frontier Information  ... 
arXiv:1911.02506v1 fatcat:xyifvmckgrel3accn3p442oowi

Approximation Algorithms for Stochastic k-TSP

Alina Ene, Viswanath Nagarajan, Rishi Saket, Marc Herbstritt
2018 Foundations of Software Technology and Theoretical Computer Science  
Our work presents an adaptive O(log k)-approximation algorithm for Stochastic k-TSP, along with a non-adaptive O(log 2 k)-approximation algorithm which also upper bounds the adaptivity gap by O(log 2 k  ...  We also show that the adaptivity gap of Stochastic k-TSP is at least e, even in the special case of stochastic knapsack cover.  ...  We thank Itai Ashlagi for initial discussions that lead to this problem definition.  ... 
doi:10.4230/lipics.fsttcs.2017.27 dblp:conf/fsttcs/EneNS17 fatcat:dzhptjfeunagze5gt5dzs6tlrq

Approximation Algorithms for Stochastic k-TSP [article]

Alina Ene, Viswanath Nagarajan, Rishi Saket
2016 arXiv   pre-print
We present an adaptive O( k)-approximation algorithm, and a non-adaptive O(^2k)-approximation algorithm. We also show that the adaptivity gap of this problem is between e and O(^2k).  ...  We consider the stochastic k-TSP problem where rewards at vertices are random and the objective is to minimize the expected length of a tour that collects reward k.  ...  Acknowledgment We thank Itai Ashlagi for initial discussions that lead to this problem definition.  ... 
arXiv:1610.01058v1 fatcat:7n7r3fpcdbhbjcombyxeitvku4

2020 Index IEEE Transactions on Signal Processing Vol. 68

2020 IEEE Transactions on Signal Processing  
., TSP 2020 3603-3618 Abdolee, R., see Ahmadi, M.J., TSP 2020 3808-3823 Abolhasani, M., and Rahmani, M., One-Step Prediction for Discrete Time-Varying Nonlinear Systems With Unknown Inputs and Correlated  ...  Noises; TSP  ...  Elkhalil, K., +, TSP 2020 2464-2479 A Method for Reducing the Performance Gap Between Non-Coherent and Coherent Sub-Arrays.  ... 
doi:10.1109/tsp.2021.3055469 fatcat:6uswtuxm5ba6zahdwh5atxhcsy

Adaptive annealing for chaotic optimization

Isao Tokuda, Kazuyuki Aihara, Tomomasa Nagashima
1998 Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics  
We show that the result of the chaotic simulated annealing algorithm is primarily dependent upon the global bifurcation structure of the chaotic neural networks and unlike the stochastic simulated annealing  ...  As an improved algorithm, the adaptive chaotic simulated annealing algorithm is introduced.  ...  The annealing speed of the slow CSA algorithm is set to ␤ ϭ0.001 for 20-city TSP and ␤ϭ0.0005 for 40-city TSP where other parameters are set the same as the adaptive algorithm.  ... 
doi:10.1103/physreve.58.5157 fatcat:egexn5xfkzem5dvll7ozczqccy

A hybrid metaheuristic for the vehicle routing problem with stochastic demand and duration constraints

Jorge E. Mendoza, Louis-Martin Rousseau, Juan G. Villegas
2015 Journal of Heuristics  
The GRASP component uses a set of randomized route-first, cluster-second heuristics to generate starting solutions and a variable-neighborhood descent procedure for the local search phase.  ...  To solve the resulting problem, we propose a greedy randomized adaptive search procedure (GRASP) enhanced with heuristic concentration (HC).  ...  Ω ← ∅, k ← 1 3: while kK do 4: for h ∈ H do 5: tps ktsp(h) 6 : s k ←split(tsp k , mode) 7: s k ←vnd(s k , mode) 8: s * ←update(s k , s * ) 9: for r ∈ s k do 10 : Ω ← Ω ∪ r 11: end for 12: k  ... 
doi:10.1007/s10732-015-9281-6 fatcat:pbyeua4wffcphlmlasvjr75l2m

Generalization in Deep RL for TSP Problems via Equivariance and Local Search [article]

Wenbin Ouyang, Yisen Wang, Paul Weng, Shaochen Han
2021 arXiv   pre-print
In order to validate the whole approach, we empirically evaluate our proposition on random and realistic TSP problems against relevant state-of-the-art deep RL methods.  ...  Deep reinforcement learning (RL) has proved to be a competitive heuristic for solving small-sized instances of traveling salesman problems (TSP), but its performance on larger-sized instances is insufficient  ...  During training, stochastic CL chooses a TSP size in R = {10, 11, . . . , 50} for each epoch e according to Equation 9 and 10,Algorithm 5 REINFORCE exploiting stochastic CL, equivariance, and smoothed  ... 
arXiv:2110.03595v1 fatcat:6vqjdta3fbflnavjcv74xzng4y

Generalization of Machine Learning for Problem Reduction: A Case Study on Travelling Salesman Problems [article]

Yuan Sun, Andreas Ernst, Xiaodong Li, Jake Weiner
2020 arXiv   pre-print
In this paper, we examine the generalization capability of a machine learning model for problem reduction on the classic traveling salesman problems (TSP).  ...  We consider three scenarios where training and test instances are different in terms of: 1) problem characteristics; 2) problem sizes; and 3) problem types.  ...  This adaptation is nontrivial, because problem-specific features and sampling methods have to be designed for TSPs.  ... 
arXiv:2005.05847v1 fatcat:a4baarlbrzfntpwumoow4fq6bm

Page 3633 of Mathematical Reviews Vol. , Issue 2000e [page]

2000 Mathematical Reviews  
Using this al- gorithm and some additional lower bound arguments, we devise a 9.5-approximation for k-delivery TSP with arbitrary finite k.  ...  We also present a 2-approximation algorithm for the case k = oo. “We then initiate the study of dynamic variants of k-delivery TSP that model problems in industrial robotics and other ap- plications.  ... 

Approximation Algorithms for Optimal Decision Trees and Adaptive TSP Problems

Anupam Gupta, Viswanath Nagarajan, R. Ravi
2017 Mathematics of Operations Research  
For this problem, we give the first poly-logarithmic approximation, and show that this algorithm is best possible unless we can improve the approximation guarantees for the well-known group Steiner tree  ...  We settle the approximability of this problem by giving a tight O( m)-approximation algorithm. We also consider a more substantial generalization, the Adaptive TSP problem.  ...  Introduction Consider the following two adaptive covering optimization problems: -Adaptive TSP under stochastic demands.  ... 
doi:10.1287/moor.2016.0831 fatcat:mr5gdx3jbfapvd7fyg3r5dkqsm

Machine Learning Meliorates Computing and Robustness in Discrete Combinatorial Optimization Problems

Fushing Hsieh, Kevin Fujii, Cho-Jui Hsieh
2016 Frontiers in Applied Mathematics and Statistics  
An unsupervised machine learning algorithm, called Data Mechanics (DM), is applied to find optimal permutations on row and column axes such that the permuted matrix reveals coupled deterministic and stochastic  ...  Can machine learning algorithms extract such information content and make combinatorial optimizing tasks more efficient?  ...  Upon such a coarse scale, we derive an K × K distance matrix by treating K clusters as K supercities, and solve the corresponding optimal solution of TSP problem.  ... 
doi:10.3389/fams.2016.00020 fatcat:jhnrs6itunfmtk3gvwn7cn23oq

Active planning for underwater inspection and the benefit of adaptivity

Geoffrey A Hollinger, Brendan Englot, Franz S Hover, Urbashi Mitra, Gaurav S Sukhatme
2012 The international journal of robotics research  
Such analysis allows the use of robust, non-adaptive planning algorithms that perform competitively with adaptive algorithms.  ...  We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from an exponential to a constant factor by changing the  ...  the Concorde TSP Library.  ... 
doi:10.1177/0278364912467485 fatcat:ksznirv22nd5dazct4blb7fzqu

Approximation Algorithms for P2P Orienteering and Stochastic Vehicle Routing Problem [article]

Shalabh Vidyarthi, Kaushal K Shukla
2015 arXiv   pre-print
We present an approximation algorithm for the non-adaptive variant of the P2P Stochastic orienteering.  ...  We consider the P2P orienteering problem on general metrics and present a (2+ϵ) approximation algorithm.  ...  [5] consider the stochastic orienteering problem and present a constant factor approximation algorithm for the best non-adaptive policy.  ... 
arXiv:1501.06515v1 fatcat:2gtwdd7bincqnejs4mgjp36vgu

A Hybrid Monte Carlo Local Branching Algorithm for the Single Vehicle Routing Problem with Stochastic Demands

Walter Rei, Michel Gendreau, Patrick Soriano
2010 Transportation Science  
We present a new algorithm that uses both local branching and Monte Carlo sampling in a multi-descent search strategy for solving 0-1 integer stochastic programming problems.  ...  This procedure is applied to the single vehicle routing problem with stochastic demands. Computational results show the usefulness of this new approach to solve hard instances of the problem.  ...  The SAA method was adapted for the case of stochastic programs with integer recourse by Ahmed and Shapiro [1] . Recently, Linderoth and al.  ... 
doi:10.1287/trsc.1090.0295 fatcat:lwhbhey5ondpnlu3stl4qtxpta
« Previous Showing results 1 — 15 out of 1,391 results