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Optimizing top-k retrieval:submodularity analysis and search strategies

Frontiers of Computer Science 2016年第3期10卷 477-487页

作者： Chaofeng SHA Keqiang WANG Dell ZHANG Xiaoling WANG Aoying ZHOU School of Computer Science Shanghai Key Laboratory of Intelligent Information ProcessingFudan University Shanghai Key Laboratory of Trustworthy Computing East China Normal University Department of Computer Science and Information Systems BirkbeckUniversity of London

The key issue in top-fc retrieval,finding a set of fc documents(from a large document collection) that can best answer a user's query,is to strike the optimal balance between relevance and *** this paper,we study the top-fc retrieval problem in the framework of facility location analysis and prove the submodularity of that objective function which provides a theoretical approximation guarantee of factor 1--for the(best-first) greedy search ***,we propose a two-stage hybrid search strategy which first obtains a high-quality initial set of top-fc documents via greedy search,and then refines that result set iteratively via local *** on two large TREC benchmark datasets show that our two-stage hybrid search strategy approach can supersede the existing ones effectively and efficiently.

关键词： top-k retrieval diversification submodular function maximization

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An Approximation Algorithm for the h-Hop Independently submodular maximization Problem and Its Applications

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第3期31卷 1216-1229页

作者： Xu, Wenzheng Xie, Hongbin Wang, Chenxi Liang, Weifa Jia, Xiaohua Xu, Zichuan Zhou, Pan Wu, Weigang Chen, Xiang Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Dalian Univ Technol Sch Software Dalian 116024 Peoples R China Huazhong Univ Sci Technol Sch Cyber Sci & Engn Hubei Engn Res Ctr Big Data Secur Wuhan 430074 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China

This study is motivated by the maximum connected coverage problem (MCCP), which is to deploy a connected UAV network with given K UAVs in the top of a disaster area such that the number of users served by the UAVs is maximized. The deployed UAV network must be connected, since the received data by a UAV from its served users need to be sent to the Internet through relays of other UAVs. Motivated by this application, in this paper we study a more generalized problem - the h-hop independently submodular maximization problem, where the MCCP problem is one of its special cases with h = 4. We propose a 1-1/epsilon/2h+3-approximation algorithm for the h-hop independently submodular maximization problem, where e is the base of the natural logarithm. Then, one direct result is a 1-1/epsilon/11-approximate solution to the MCCP problem with h = 4, which significantly improves its currently best 1-1/epsilon/32-approximate solution. We finally evaluate the performance of the proposed algorithm for the MCCP problem in the application of deploying UAV networks, and experimental results show that the number of users served by deployed UAVs delivered by the proposed algorithm is up to 12.5% larger than those by existing algorithms.

关键词： UAV communication networks maximum connected coverage problem connected sensor coverage problem submodular function maximization approximation algorithms

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Maximizing Sequence-submodular functions and Its Application to Online Advertising

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MANAGEMENT SCIENCE 2021年第10期67卷 6030-6054页

作者： Alaei, Saeed Makhdoumi, Ali Malekian, Azarakhsh Google Res Mountain View CA 94043 USA Duke Univ Fuqua Sch Business Durham NC 27708 USA Univ Toronto Rotman Sch Management Toronto ON M5S 3E6 Canada

Motivated by applications in online advertising, we consider a class of maximization problems where the objective is a function of the sequence of actions and the running duration of each action. For these problems, we introduce the concepts of sequence-submodularity and sequence-monotonicity, which extend the notions of submodularity and monotonicity from functions defined over sets to functions defined over sequences. We establish that if the objective function is sequence-submodular and sequence-nondecreasing, then there exists a greedy algorithm that achieves 1 - 1/e of the optimal solution. We apply our algorithm and analysis to two applications in online advertising: online ad allocation and query rewriting. We first show that both problems can be formulated as maximizing nondecreasing sequence-submodular functions. We then apply our framework to these two problems, leading to simple greedy approaches with guaranteed performances. In particular, for the online ad allocation problem, the performance of our algorithm is 1 - 1/e, which matches the best known existing performance, and for the query rewriting problem, the performance of our algorithm is 1 - 1/e1-1/e, which improves on the best known existing performance in the literature.

关键词： submodular function maximization sequence submodularity applications to online advertisement online ad allocation query rewriting

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Maximizing a class of submodular utility functions

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MATHEMATICAL PROGRAMMING 2011年第1-2期128卷 149-169页

作者： Ahmed, Shabbir Atamtuerk, Alper Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Georgia Inst Technol Sch Ind & Syst Engn Atlanta GA 30332 USA

Given a finite ground set N and a value vector a is an element of R-N, we consider optimization problems involving maximization of a submodular set utility function of the form h(S) = f(Sigma(i is an element of S)a(i)), S subset of N, where f is a strictly concave, increasing, differentiable function. This utility function appears frequently in combinatorial optimization problems when modeling risk aversion and decreasing marginal preferences, for instance, in risk-averse capital budgeting under uncertainty, competitive facility location, and combinatorial auctions. These problems can be formulated as linear mixed 0-1 programs. However, the standard formulation of these problems using submodular inequalities is ineffective for their solution, except for very small instances. In this paper, we perform a polyhedral analysis of a relevant mixed-integer set and, by exploiting the structure of the utility function h, strengthen the standard submodular formulation significantly. We show the lifting problem of the submodular inequalities to be a submodular maximization problem with a special structure solvable by a greedy algorithm, which leads to an easily-computable strengthening by subadditive lifting of the inequalities. Computational experiments on expected utility maximization in capital budgeting show the effectiveness of the new formulation.

关键词： Expected utility maximization Combinatorial auctions Competitive facility location submodular function maximization Polyhedra

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Maximizing Charging Efficiency With Fresnel Zones

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第1期23卷 612-629页

作者： Lin, Chi Hao, Shibo Dai, Haipeng Yang, Wei Wang, Lei Wu, Guowei Zhang, Qiang Dalian Univ Technol Sch Software Technol Dalian 116024 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaonin Dalian 116621 Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China Dalian Univ Technol Sch Comp Sci & Technol Dalian 116024 Peoples R China

Benefitting from the discovery of wireless power transfer (WPT) technology, the wireless rechargeable sensor network (WRSN) has become a promising way for lifetime extension for wireless sensor networks. In practical WRSN scenarios, obstacles can be found almost everywhere. Most state-of-the-art researches believe that obstacles will always degrade signal strength, and omit the influence of obstacles for simplifying the computation process. However, overlooking the positive impacts of obstacles on signal propagation is inconsistent with the intrinsic features of electromagnetic waves. To address this issue, in this paper, we explore the wireless signal propagation process and provide a theoretical charging model to enhance the charging efficiency by leveraging obstacles. Through utilizing the concept of the Fresnel Zone model, we re-formalize the wireless charging model and discretize the charging area and charging time to determine the best charging locations as well as charging duration. We model the charging Efficiency maximization with Obstacles (EMO) problem as a submodular function maximization problem and propose a cost-efficient algorithm to solve it. Finally, test-bed experiments and extensive simulations are both conducted to verify that our schemes outperform baseline algorithms by $33.46\%$33.46% on average in charging efficiency improvement.

关键词： Wireless sensor networks Costs Wireless communication Mathematical models Mobile computing Fresnel reflection Inductive charging Wireless rechargeable sensor networks Fresnel Zones charging energy maximization submodular function maximization

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Delay-Power-Rate-Distortion Optimization of Video Representations for Dynamic Adaptive Streaming

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2018年第7期28卷 1648-1664页

作者： Li, Chenglin Toni, Laura Zou, Junni Xiong, Hongkai Frossard, Pascal Swiss Fed Inst Technol EPFL Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland UCL Elect & Elect Dept London WC1E 7JE England Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

Dynamic adaptive streaming addresses user heterogeneity by providing multiple encoded representations at different rates and/or resolutions for the same video content. For delay-sensitive applications, such as live streaming, there is however a stringent requirement on the encoding delay, and usually the encoding power (or rate) budget is also limited by the computational (or storage) capacity of the server. It is therefore important, yet challenging, to optimally select the source coding parameters for each encoded representation in order to minimize the resource consumption while maintaining a high quality of experience for the users. To address this, we propose an optimization framework with an optimal representation selection problem for delay, power, and rate constrained adaptive video streaming. Then, by the optimal selection of source coding parameters for each selected representation, we maximize the overall expected user satisfaction, subject not only to the encoding rate constraint, but also to the delay and power constraints at the server. We formulate the proposed optimization problem as an integer linear program formulation to provide the performance upper bound, and as a submodular maximization problem with two knapsack constraints to develop a practically feasible algorithm. Simulation results show that the proposed weighted rate and power cost benefit greedy algorithm is able to achieve a near-optimal performance with very low time complexity. In addition, it can strike the best tradeoff both between the rate and power cost, and between the algorithm's performance and the delay requirements proposed by delay sensitive applications.

关键词： Dynamic adaptive video streaming representation selection delay-power-rate-distortion live video submodular function maximization

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MAXIMIZING NON-MONOTONE submodular functionS

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SIAM JOURNAL ON COMPUTING 2011年第4期40卷 1133-1153页

作者： Feige, Uriel Mirrokni, Vahab S. Vondrak, Jan Weizmann Inst Sci Dept Comp Sci & Appl Math IL-76100 Rehovot Israel Google Res New York NY 10011 USA IBM Almaden Res Ctr San Jose CA 95120 USA

submodular maximization generalizes many important problems including Max Cut in directed and undirected graphs and hypergraphs, certain constraint satisfaction problems, and maximum facility location problems. Unlike the problem of minimizing submodular functions, the problem of maximizing submodular functions is NP-hard. In this paper, we design the first constant-factor approximation algorithms for maximizing nonnegative (non-monotone) submodular functions. In particular, we give a deterministic local-search 1/3-approximation and a randomized 2/5-approximation algorithm for maximizing nonnegative submodular functions. We also show that a uniformly random set gives a 1/4-approximation. For symmetric submodular functions, we show that a random set gives a 1/2-approximation, which can also be achieved by deterministic local search. These algorithms work in the value oracle model, where the submodular function is accessible through a black box returning f(S) for a given set S. We show that in this model, a (1/2 + epsilon)-approximation for symmetric submodular functions would require an exponential number of queries for any fixed epsilon > 0. In the model where f is given explicitly (as a sum of nonnegative submodular functions, each depending only on a constant number of elements), we prove NP-hardness of (5/6 + epsilon)-approximation in the symmetric case and NP-hardness of (3/4 + epsilon)-approximation in the general case.

关键词： approximation algorithms combinatorial optimization submodular functions submodular function maximization local search algorithms information-theoretic lower bounds

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Informative path planning as a maximum traveling salesman problem with submodular rewards

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DISCRETE APPLIED MATHEMATICS 2015年第1期186卷 112-127页

作者： Jawaid, Syed Talha Smith, Stephen L. Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

In this paper we extend the classic problem of finding the maximum weight Hamiltonian cycle in a graph to the case where the objective is a submodular function of the edges. We consider a greedy algorithm and a 2-matching based algorithm, and we show that they have approximation factors of TIT, and max{2/3(2+kappa), 2/3(1 - kappa)} respectively, where kappa is the curvature of the submodular function. Both algorithms require a number of calls to the submodular function that is cubic to the number of vertices in the graph. We then present a method to solve a multi-objective optimization consisting of both additive edge costs and submodular edge rewards. We provide simulation results to empirically evaluate the performance of the algorithms. Finally, we demonstrate an application in monitoring an environment using an autonomous mobile sensor, where the sensing reward is related to the entropy reduction of a given set of measurements. (C) 2015 Elsevier B.V. All rights reserved.

关键词： submodular function maximization Traveling salesman problem Informative path planning

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Toward Rendering-Latency Reduction for Composable Web Services via Priority-Based Object Caching

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IEEE TRANSACTIONS ON MULTIMEDIA 2018年第7期20卷 1864-1875页

作者： Hu, Han Li, Yuanlong Wen, Yonggang Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore

Web services serve as the cornerstone of the Internet for rendering webpages. The initial rendering latency of webpages, which depends on a subset of critical objects required by the webpage, is a key metric for web services. In this work, we propose to identify this set of critical objects systematically with the goal of caching them at a higher priority to reduce the initial rendering time. We first conduct a measurement study on a mainstream content delivery network provider, the results of which suggest that not all currently cached objects are critical and that only a small portion of the critical objects are cached. Thus, we model the critical-object aware caching scheme as a constrained optimization problem. Using the stochastic optimization framework, we decompose the problem into a set of one-shot optimization problems, which are proved to be NP-hard. We then develop two greedy algorithms with different computational complexity but the same performance bound. Finally, we integrate the resulting approximation algorithms into an online algorithm. Through trace-based simulations, we verify that our proposed algorithm can reduce service latency and network traffic by ensuring a higher cache hit ratio.

关键词： Web page caching submodular function maximization

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QoE-Driven Mobile Edge Caching Placement for Adaptive Video Streaming

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IEEE TRANSACTIONS ON MULTIMEDIA 2018年第4期20卷 965-984页

作者： Li, Chenglin Toni, Laura Zou, Junni Xiong, Hongkai Frossard, Pascal Ecole Polytech Fed Lausanne Signal Proc Lab LTS4 CH-1015 Lausanne Switzerland UCL Elect & Elect Dept London WC1E 7JE England Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China

Caching at mobile edge servers can smooth temporal traffic variability and reduce the service load of base stations in mobile video delivery. However, the assignment of multiple video representations to distributed servers is still a challenging question in the context of adaptive streaming, since any two representations from different videos or even from the same video will compete for the limited caching storage. Therefore, it is important, yet challenging, to optimally select the cached representations for each edge server in order to effectively reduce the service load of base station while maintaining a high quality of experience (QoE) tbr users. To address this, we study a QoE-driven mobile edge caching placement optimization problem for dynamic adaptive video streaming that properly takes into account the different rate-distortion (R-D) characteristics of videos and the coordination among distributed edge servers. Then, by the optimal caching placement of representations for multiple videos, we maximize the aggregate average video distortion reduction of all users while minimizing the additional cost of representation downloading from the base station, subject not only to the storage capacity constraints of the edge servers, but also to the transmission and initial startup delay constraints of the users. We formulate the proposed optimization problem as an integer linear program to provide the performance upper bound, and as a submodular maximization problem with a set of knapsack constraints to develop a practically feasible cost benefit greedy algorithm. The proposed algorithm has polynomial computational complexity and a theoretical lower bound on its performance. Simulation results further show that the proposed algorithm is able to achieve a near-optimal performance with very low time complexity. Therefore, the proposed optimization framework reveals the caching performance upper bound for general adaptive video streaming systems, while the proposed algo

关键词： Mobile edge caching adaptive video streaming wireless video delivery video-on-demand submodular function maximization

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