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Optimizing biologically inspired transport networks by control

Physical Review E 2019年第3期100卷 032309-032309页

作者： Junjie Jiang Xingang Wang Ying-Cheng Lai School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA School of Physics and Information Technology Shaanxi Normal University Xi'an 710062 China Department of Physics Arizona State University Tempe Arizona 85287 USA

Transportation networks with intrinsic flow dynamics governed by the Kirchhoff's current law are ubiquitous in natural and engineering systems. There has been recent work on designing optimal transportation networks based on biological principles with the goal to minimize the total dissipation associated with the flow. Despite being biologically inspired, e.g., adaptive network design based on slime mold Physarum polycephalum, such methods generally lead to suboptimal networks due to the difficulty in finding a global or nearly global optimum of the nonconvex optimization function. Here we articulate a design paradigm that combines engineering control and biological principles to realize optimal transportation networks. In particular, we show how small control signals applied only to a fraction of edges in an adaptive network can lead to solutions that are far more optimal than those based solely on biological principles. We also demonstrate that control signals, if not properly designed, can lead to networks that are less optimal. Incorporating control principle into biology-based optimal network design has broad applications not only in biomedical science and engineering but also in other disciplines such as civil engineering for designing resilient infrastructure systems.

关键词： network flow network flow optimization optimization problems

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Markov chain approach to model intertemporal choices and coverages in air transport markets

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Physical Review E 2019年第6期100卷 062303-062303页

作者： Daniel O. Cajueiro Felipe N. Mundim João I. F. Martins Patricia A. M. Sakowski Roberto F. S. Andrade Departamento de Economia Universidade de Brasília (UnB) Campus Universitário Darcy Ribeiro 70910-900 Brasília Brazil National Institute of Science and Technology for Complex Systems (INCT-SC) 22290-180 Rio de Janeiro Brazil Administrative Council for Economic Defense (CADE) SEPN 515 70770-504 Brasília Brazil Instituto de Física Universidade Federal da Bahia 40170-115 Salvador Brazil Centre for Data and Knowledge Integration for Health (CIDACS) Instituto Gonçalo Muniz Fundação Oswaldo Cruz (FIOCRUZ) 41745-715 Salvador Brazil

We develop a framework based on discrete-time Markov chains (MCs) to model the Brazilian air transport market networks. Our results suggest that in this market, economic activity explains the centralities of the airports and the players' choices. Periods of economic prosperity affect the air transport networks and they move from a hub-and-spoke topology towards a mixed point-to-point and hub-and-spoke topology. Furthermore, we use the MC transition and stationary probabilities to build geographical subgraphs that reveal (1) the most important routes, (2) the stability of these routes, (3) the most important flights, and (4) the airlines' specific market niches.

关键词： network flow network flow optimization Traffic Transport in networks Transportation Flight networks Transportation networks Markovian processes

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Coordinating dynamical routes with statistical physics on space-time networks

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Physical Review E 2019年第4期99卷 042123-042123页

作者： Chi Ho Yeung Department of Science and Environmental Studies The Education University of Hong Kong Tai Po Hong Kong China

Coordination of dynamical routes can alleviate traffic congestion and is essential for the coming era of autonomous self-driving cars. However, dynamical route coordination is difficult and many existing routing protocols are either static or without intervehicle coordination. In this paper, we first apply the cavity approach in statistical physics to derive the theoretical behavior and an optimization algorithm for dynamical route coordination, but they become computationally intractable as the number of time segments increases. We therefore map static spatial networks to space-time networks to derive a computational feasible message-passing algorithm compatible with arbitrary system parameters; it agrees well with the analytical and algorithmic results of the conventional cavity approach and outperforms multistart greedy search in saving total travel time by as much as 15% in simulations. The study sheds light on the design of dynamical route coordination protocols and the solution to other dynamical problems via static analytical approaches on space-time networks.

关键词： network flow network flow optimization Transportation networks Disordered systems Cavity methods

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Effective traffic-flow assignment strategy on multilayer networks

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Physical Review E 2019年第1期100卷 012310-012310页

作者： Lei Gao Panpan Shu Ming Tang Wei Wang Hui Gao College of Information Science and Engineering Shandong Agricultural University Taian 271018 China Web Sciences Center University of Electronic Science and Technology of China Chengdu 610054 China School of Sciences Xi'an University of Technology Xi'an 710054 China School of Mathematical Sciences Shanghai Key Laboratory of PMMP East China Normal University Shanghai 200241 China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai 200241 China Cybersecurity Research Institute Sichuan University Chengdu 610065 China

An efficient flow assignment strategy is of great importance to alleviate traffic congestion on multilayer networks. In this work, by considering the roles of nodes' local structures on the microlevel, and the different transporting speeds of layers in the macrolevel, an effective traffic-flow assignment strategy on multilayer networks is proposed. Both numerical and semianalytical results indicate that our proposed flow assignment strategy can reasonably redistribute the traffic flow of the low-speed layer to the high-speed layer. In particular, preferentially transporting the packets through small-degree nodes on the high-speed layer can enhance the traffic capacity of multilayer networks. We also find that the traffic capacity of multilayer networks can be improved by increasing the network size and the average degree of the high-speed layer. For a given multilayer network, there is a combination of optimal macrolevel parameter and optimal microlevel parameter with which the traffic capacity can be maximized. It is verified that real-world network topology does not invalidate the results. The semianalytical predictions agree with the numerical simulations.

关键词： Collective behavior in networks Continuous phase transition network flow optimization Multilayer & multiplex networks Transportation networks Betweenness Monte Carlo methods

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Convex and network flow optimization for Structured Sparsity

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JOURNAL OF MACHINE LEARNING RESEARCH 2011年第9期12卷 2681-2720页

作者： Mairal, Julien Jenatton, Rodolphe Obozinski, Guillaume Bach, Francis Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Ecole Normale Super INRIA ENS CNRS UMR 8548 Lab Informat INRIA SIERRA Project Team F-75214 Paris 13 France

We consider a class of learning problems regularized by a structured sparsity-inducing norm defined as the sum of l(2)- or l(infinity)-norms over groups of variables. Whereas much effort has been put in developing fast optimization techniques when the groups are disjoint or embedded in a hierarchy, we address here the case of general overlapping groups. To this end, we present two different strategies: On the one hand, we show that the proximal operator associated with a sum of l(infinity)-norms can be computed exactly in polynomial time by solving a quadratic min-cost flow problem, allowing the use of accelerated proximal gradient methods. On the other hand, we use proximal splitting techniques, and address an equivalent formulation with non-overlapping groups, but in higher dimension and with additional constraints. We propose efficient and scalable algorithms exploiting these two strategies, which are significantly faster than alternative approaches. We illustrate these methods with several problems such as CUR matrix factorization, multi-task learning of tree-structured dictionaries, background subtraction in video sequences, image denoising with wavelets, and topographic dictionary learning of natural image patches.

关键词： convex optimization proximal methods sparse coding structured sparsity matrix factorization network flow optimization alternating direction method of multipliers

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New Multi-Commodity flow Formulations for the Generalized Pooling Problem

New Multi-Commodity Flow Formulations for the Generalized Po...

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10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM)

作者： Cheng, Xin Tang, Kai Li, Xiang Queens Univ Dept Chem Engn Kingston ON K7L 3N6 Canada

The generalized pooling problem is involved in many planning and scheduling problems in the petrochemical industry. Compared to the standard pooling problem where the blenders (or pools) are not allowed to be connected to one another, the generalized pooling problem has a more complex network structure and allows more types of problem formulations. The state-of-the-art generalized pooling formulations adopt a multi-commodity flow (MCF) strategy that was first proposed by Alfaki and Haugland (2013a) and proved to be stronger than the classical p-formulation. This paper proposes two new MCF formulations for the generalized pooling problem, using mixing and split fractions of blenders rather than the commodity flow fractions. The case study results show that, for some cases, the proposed formulations perform better than the existing MCF formulations, but none of the formulations dominates others for all cases. The results also show that formulations which have similar sizes and similarly tight linear programming relaxations may have dramatically different performance. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Pooling problem Multi-commodity flow Global optimization Blending network flow optimization

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Influence of the degree of a complex network on heat conduction

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Physical Review E 2018年第2期98卷 022115-022115页

作者： Kezhao Xiong Chunhua Zeng Zonghua Liu Baowen Li Department of Physics East China Normal University Shanghai 200062 People's Republic of China Department of Mechanical Engineering University of Colorado Boulder Colorado 80309 USA Institute of Physical and Engineering Science Kunming University of Science and Technology Kunming 650500 People's Republic of China Department of Physics Nanjing University Nanjing 210093 People's Republic of China

Devices made of nanotubes and nanowires networks are of great interest for applications and have caught increasing attention in recent years. In this work, we study heat conduction in a network model with nodes being atoms and links being one-dimensional chains of atoms. It is found that heat conduction in the complex network is fundamentally different from that of regular lattices. It depends very sensitively on the average degrees of complex networks and the degrees of nodes that are attached to the two heat baths. For example, when the two heat source nodes have the same degree k0, the heat flux reaches a maximum at an optimized value of k0 and decreases with the increase of the average degree 〈k〉. In other words, the source nodes with optimal degree k0 and the sparse network are more favorable to heat flux. Thermal rectification effect is found when the two heat source nodes have different degrees or the network model has multiple heat source nodes. Theoretical analysis is provided to explain the numerical results.

关键词： network flow optimization Thermodynamics

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Optimizing the supply chain of biomass and biogas for a single plant considering mass and energy losses

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期262卷 744-758页

作者： Jensen, Ida Graested Munster, Marie Pisinger, David Tech Univ Denmark DTU Management Engn Prod TorvetBldg 426 DK-2800 Lyngby Denmark

The share of renewable energy in the Danish energy sector is increasing and the goal is that biogas production should reach a production level of 17 petajoules (PJ) in 2020 according to the Danish Energy Agency. However, this goal is currently not reachable due to lack of investments in biogas plants. In this paper, a mixed integer programming (MIP) model for finding the optimal production and investment plan for a biogas supply chain is presented to ensure better economy for the full chain hopefully stimulating future investments in biogas. The model makes "use of step-wise linear functions to represent capital and operational expenditures at the biogas plant;considers the chain from the farmer to the end market;and includes changes of mass and energy content along the chain by modeling the losses and gains for all processes in the chain. Biomass inputs are scheduled on a weekly basis whereas energy outputs are scheduled on an hourly basis to better capture the changes of energy prices and potentially take advantage of these changes. The model is tested on a case study with co-digestion of straw, sugar beet and manure, considering natural gas, heat, and electricity as end products. The model finds a production and investment plan for a predefined location of the plant within half an hour of central processing unit (CPU) time. The resulting project turns out to be profitable and gives a production plan for each process, which underlines the possibilities of optimizing the processes in a biogas project. (C) 2017 Elsevier B.V. All rights reserved.

关键词： OR in energy Supply chain optimization Biomass and bioenergy supply chains network flow optimization

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Hierarchical Multilabel Classification with Optimal Path Prediction

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NEURAL PROCESSING LETTERS 2017年第1期45卷 263-277页

作者： Sun, Zhengya Zhao, Yangyang Cao, Dong Hao, Hongwei Chinese Acad Sci Inst Automat Beijing Peoples R China

We consider multilabel classification problems where the labels are arranged hierarchically in a tree or directed acyclic graph (DAG). In this context, it is of much interest to select a well-connected subset of nodes which best preserve the label dependencies according to the learned models. Top-down or bottom-up procedures for labelling the nodes in the hierarchy have recently been proposed, but they rely largely on pairwise interactions, thus susceptible to get stuck in local optima. In this paper, we remedy this problem by directly finding a small number of label paths that can cover the desired subgraph in a tree/DAG. To estimate the high-dimensional label vector, we adopt the advantages of partial least squares techniques which perform simultaneous projections of the feature and label space, while constructing sound linear models between them. We then show that the optimal label prediction problem with hierarchy constraints can be reasonably transformed into the optimal path prediction problem with the structured sparsity penalties. The introduction of path selection models further allows us to leverage the efficient network flow solvers with polynomial time complexity. The experimental results validate the promising performance of the proposed algorithm in comparison to the state-of-the-art algorithms on both tree- and DAG-structured data sets.

关键词： Hierarchical classification Multilabel classification network flow optimization

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Tracklet Association by Online Target-Specific Metric Learning and Coherent Dynamics Estimation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2017年第3期39卷 589-602页

作者： Wang, Bing Wang, Gang Chan, Kap Luk Wang, Li Nanyang Technol Univ Sch Elect & Elect Engn 50 Nanyang Ave Singapore 639798 Singapore

In this paper, we present a novel method based on online target-specific metric learning and coherent dynamics estimation for tracklet (track fragment) association by network flow optimization in long-term multi-person tracking. Our proposed framework aims to exploit appearance and motion cues to prevent identity switches during tracking and to recover missed detections. Furthermore, target-specific metrics (appearance cue) and motion dynamics (motion cue) are proposed to be learned and estimated online, i.e., during the tracking process. Our approach is effective even when such cues fail to identify or follow the target due to occlusions or object-to-object interactions. We also propose to learn the weights of these two tracking cues to handle the difficult situations, such as severe occlusions and object-to-object interactions effectively. Our method has been validated on several public datasets and the experimental results show that it outperforms several state-of-the-art tracking methods.

关键词： Multi-object tracking tracklet association target-specific metric learning motion dynamics network flow optimization

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