A difference-of-convex programming approach with parallel branch-and-bound for sentence compression via a hybrid extractive model

Niu, Yi-Shuai; Yu, You; Xu, Wenxu; Ding, Wentao; Hu, Junpeng; Yao, S. F.

doi:10.1007/s11590-020-01695-9

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Difference-of-convex (DC) programming has been an active area of research in nonconvex and nonsmooth optimization [19,20,49,44,27,1,42]. They arise in extensive applications such as sparsity learning [55,28], clustering [23], molecular conformation [25], trust region subproblem [45,7], portfolio optimization [48,47,38], control theory [36], natural language processing [40], image denoising [56], mixed-integer optimization [22,35,31,39] and eigenvalue complementarity problem [24,37,34,33], to name a few; see [27] and the references therein for a comprehensive introduction about DC programming and its applications.…”

Section: Introductionmentioning

confidence: 99%

A Variable Metric and Nesterov Extrapolated Proximal DCA with Backtracking for A Composite DC Program

You¹,

Niu²

2022

Preprint

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In this paper, we consider a composite difference-of-convex (DC) program, whose objective function is the sum of a smooth convex function with Lipschitz continuous gradient, a proper closed and convex function, and a continuous concave function. This problem has many applications in machine learning and data science. The proximal DCA (pDCA), a special case of the classical DCA, as well as two Nesterov-type extrapolated DCA -ADCA

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Section: Introductionmentioning

confidence: 99%

A Variable Metric and Nesterov Extrapolated Proximal DCA with Backtracking for A Composite DC Program

You¹,

Niu²

2022

Preprint

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“…These methods are based on continuous representation techniques for integer set, exact penalty theorem, DCA and branch-and-bound. There are various applications of this kind of approaches including sentence compression [41], scheduling [21], network optimization [50], cryptography [43] and finance [20,46] etc. Recently, Y.S.…”

Section: Introductionmentioning

confidence: 99%

Parallel DC Cutting Plane Algorithms for Mixed Binary Linear Program

Niu

Liu

2019

Advances in Intelligent Systems and Computing

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In this paper, we propose a cutting plane algorithm based on DC (Difference-of-Convex) programming and DC cut for globally solving Mixed-Binary Linear Program (MBLP). We first use a classical DC programming formulation via the exact penalization to formulate MBLP as a DC program, which can be solved by DCA algorithm. Then, we focus on the construction of DC cuts, which serves either as a local cut (namely type-I DC cut) at feasible local minimizer of MBLP, or as a global cut (namely type-II DC cut) at infeasible local minimizer of MBLP if some particular assumptions are verified. Otherwise, the constructibility of DC cut is still unclear, and we propose to use classical global cuts (such as the Lift-and-Project cut) instead. Combining DC cut and classical global cuts, a cutting plane algorithm, namely DCCUT, is established for globally solving MBLP. The convergence theorem of DCCUT is proved. Restarting DCA in DCCUT helps to quickly update the upper bound solution and to introduce more DC cuts for lower bound improvement. A variant of DCCUT by introducing more classical global cuts in each iteration is proposed, and parallel versions of DCCUT and its variant are also designed which use the power of multiple processors for better performance. Numerical simulations of DCCUT type algorithms comparing with the classical cutting plane algorithm using Lift-and-Project cuts are reported. Tests on some specific samples and the MIPLIB 2017 benchmark dataset demonstrate the benefits of DC cut and good performance of DCCUT algorithms.

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“…Our approach for solving this DC programming problem is based on the branch-and-bound algorithm [31], [32], which is widely used in solving non-convex problems. The branchand-bound method is a global optimization approach, by which the optimal solution can be obtained [23], [33].…”

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confidence: 99%

Resource Allocation for D2D Cellular Networks With QoS Constraints: A DC Programming- Based Approach

et al. 2022

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Device-to-device (D2D) communications provide efficient ways to increase spectrum utilization ratio with reduced power consumption for proximity wireless applications. In this paper, we investigate resource allocation strategies for D2D communications underlaying cellular networks. To be specific, we study the centralized resource allocation algorithm for controlling transmit powers of the underlying D2D pairs in order to maximize the weighted sum-rate while guaranteeing the quality of service (QoS) requirements for both D2D pairs and cellular users (CUs). A novel DC (difference of convex function) programming-based method, called alternative DC (ADC) programming, is introduced to effectively solve this complicated resource allocation problem. Through updating each D2D pair's power alternatively, the QoS requirement for each D2D pair can be solvable and incorporated systematically to the introduced ADC programming framework. The simulation results show that the introduced ADC programming achieves the highest weighted sum-rate compared to the state-of-the-art methods while ensuring that the QoS of each D2D pair and CU are satisfied.

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A difference-of-convex programming approach with parallel branch-and-bound for sentence compression via a hybrid extractive model

Cited by 4 publications

References 20 publications

A Variable Metric and Nesterov Extrapolated Proximal DCA with Backtracking for A Composite DC Program

A Variable Metric and Nesterov Extrapolated Proximal DCA with Backtracking for A Composite DC Program

Parallel DC Cutting Plane Algorithms for Mixed Binary Linear Program

Resource Allocation for D2D Cellular Networks With QoS Constraints: A DC Programming- Based Approach

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