Asymmetric distance location model

AbstractThe Asymmetric Distance-Constrained Vehicle Routing Problem (ADVRP) is NP-hard as it is a natural extension of the NP-hard Vehicle Routing Problem. In ADVRP problem, each customer is visited exactly once by a vehicle; every tour starts and ends at a depot; and the traveled distance by each vehicle is not allowed to exceed a predetermined limit. We propose a hybrid metaheuristic algorithm combining the Randomized Variable Neighborhood Search (RVNS) and the Tabu Search (TS) to solve the problem. The combination of multiple neighborhoods and tabu mechanism is used for their capacity to escape local optima while exploring the solution space. Furthermore, the intensification and diversification phases are also included to deliver optimized and diversified solutions. Extensive numerical experiments and comparisons with all the state-of-the-art algorithms show that the proposed method is highly competitive in terms of solution quality and computation time, providing new best solutions for a number of instances.

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Excellent enantio-selective enclathration of (2R,3S)-3-methyl-2-pentanol in channel-like cavity of 3-epideoxycholic acid, interpreted by the four-location model for chiral recognition

Chemical Communications ◽

10.1039/b309755b ◽

2003 ◽

pp. 2872 ◽

Cited By ~ 20

Author(s):

Kazuaki Kato ◽

Kazuaki Aburaya ◽

Yasuhito Miyake ◽

Kazuki Sada ◽

Norimitsu Tohnai ◽

...

Keyword(s):

Chiral Recognition ◽

Location Model

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The information complexity of learning tasks, their structure and their distance

Information and Inference A Journal of the IMA ◽

10.1093/imaiai/iaaa033 ◽

2021 ◽

Author(s):

Alessandro Achille ◽

Giovanni Paolini ◽

Glen Mbeng ◽

Stefano Soatto

Keyword(s):

Kolmogorov Complexity ◽

Large Scale ◽

Parametric Model ◽

Training Dataset ◽

Optimization Scheme ◽

Learning Tasks ◽

Asymmetric Distance ◽

Special Cases ◽

Scale Models ◽

Real World Datasets

Abstract We introduce an asymmetric distance in the space of learning tasks and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity and Shannon and Fisher information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in deep learning.

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