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10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)

作者： Tripathi, Shruti Jain, Vinod Kumar PDPM Indian Inst Informat Technol Design & Mfg Ja Comp Sci & Engn Discipline Jabalpur Madhya Pradesh India

ISBN: (纸本)9781538659069

In the large-scale RFID system involving passive tags, tag collisions are preordained, as each tag is unaware of any other tags in the system. Therefore an efficient anti-collision algorithm is a key aspect which greatly affects the identification efficiency. Adaptive Binary splitting (ABS) has been one of the benchmark protocols in this regard. But this protocol performed binary splitting to resolve collisions, which takes a considerable number of collision cycles to identify a large number of tags. To resolve this, a modified version of ABS has been proposed, where instead of performing binary splitting, an M-ary splitting of tags has been done using the tree splitting approach. The value of M has been chosen in the range of 3 to 10 and the results are analyzed. The analysis helps to empirically determine the best value of M among all the variants of ABS at M >= 2. The simulation results show that the variant at M = 3 gives a better result in terms of the number of collision cycles and transmitted bits overhead while pertaining a similar identification delay as the ABS protocol.

关键词： Radio frequency identification tag anti-collision splitting algorithm mobile tag tree variants

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A primal-dual method of partial inverses for composite inclusions

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OPTIMIZATION LETTERS 2014年第8期8卷 2271-2284页

作者： Alghamdi, Maryam A. Alotaibi, Abdullah Combettes, Patrick L. Shahzad, Naseer King Abdulaziz Univ Sci Fac Girls Dept Math Jeddah 21491 Saudi Arabia King Abdulaziz Univ Dept Math Jeddah 21859 Saudi Arabia Univ Paris 06 Univ Sorbonne Lab Jacques Louis Lions UMR 7598 F-75005 Paris France

Spingarn's method of partial inverses has found many applications in nonlinear analysis and in optimization. We show that it can be employed to solve composite monotone inclusions in duality, thus opening a new range of applications for the partial inverse formalism. The versatility of the resulting primal-dual splitting algorithm is illustrated through applications to structured monotone inclusions and optimization.

关键词： Convex optimization Duality Method of partial inverses Monotone operator splitting algorithm

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An Opportunistic, Fast, and Distributed Subchannel and User-Pairing algorithm for OFDMA

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IEEE TRANSACTIONS ON COMMUNICATIONS 2012年第3期60卷 767-778页

作者： Karthik, A. Mehta, Neelesh B. Indian Inst Sci IISc Dept Elect Commun Engn Bangalore Karnataka India

Channel-aware assignment of subchannels to users in the downlink of an OFDMA system requires extensive feedback of channel state information (CSI) to the base station. Since bandwidth is scarce, schemes that limit feedback are necessary. We develop a novel, low feedback, distributed splitting-based algorithm called SplitSelect to opportunistically assign each subchannel to its most suitable user. SplitSelect explicitly handles multiple access control aspects associated with CSI feedback, and scales well with the number of users. In it, according to a scheduling criterion, each user locally maintains a scheduling metric for each subchannel. The goal is to select, for each subchannel, the user with the highest scheduling metric. At any time, each user contends for the subchannel for which it has the largest scheduling metric among the unallocated subchannels. A tractable asymptotic analysis of a system with many users is central to SplitSelect's simple design. Extensive simulation results demonstrate the speed with which subchannels and users are paired. The net data throughput, when the time overhead of selection is accounted for, is shown to be substantially better than several schemes proposed in the literature. We also show how fairness and user prioritization can be ensured by suitably defining the scheduling metric.

关键词： OFDMA subcarrier allocation downlink fading channels distributed algorithms splitting algorithm multiuser diversity frequency-domain scheduling feedback

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Multi-Linear Pseudo-PageRank for Hypergraph Partitioning

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JOURNAL OF SCIENTIFIC COMPUTING 2024年第1期99卷 7-7页

作者： Chen, Yannan Li, Wen Chang, Jingya South China Normal Univ Sch Math Sci Guangzhou 510631 Peoples R China Guangdong Univ Technol Sch Math & Stat Guangzhou 510520 Peoples R China

Motivated by the PageRank model for graph partitioning, we develop an extension of PageRank for partitioning uniform hypergraphs. Starting from adjacency tensors of uniform hypergraphs, we establish the multi-linear pseudo-PageRank (MLPPR) model, which is formulated as a multi-linear system with nonnegative constraints. The coefficient tensor of MLPPR is a kind of Laplacian tensors of uniform hypergraphs, which are almost as sparse as adjacency tensors since no dangling corrections are incorporated. Furthermore, all frontal slices of the coefficient tensor of MLPPR are M-matrices. Theoretically, MLPPR has a solution, which is unique under mild conditions. An error bound of the MLPPR solution is analyzed when the Laplacian tensor is slightly perturbed. Computationally, by exploiting the structural Laplacian tensor, we propose a tensor splitting algorithm, which converges linearly to a solution of MLPPR. Finally, numerical experiments illustrate that MLPPR is effective and efficient for hypergraph partitioning problems.

关键词： PageRank Multi-linear system Existence Uniqueness Perturbation analysis splitting algorithm Convergence Hypergraph Laplacian tensor Hypergraph partitioning Network analysis

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Comparison of tree-based methods for prognostic stratification of survival data

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2003年第3期28卷 323-341页

作者： Radespiel-Tröger, M Rabenstein, T Schneider, HT Lausen, B Univ Erlangen Nurnberg Dept Med Informat Biometry & Epidemiol D-91054 Erlangen Germany Univ Erlangen Nurnberg Dept Med 1 D-91054 Erlangen Germany

Tree-based methods can be used to generate rules for prognostic classification of patients that are expressed as logical combinations of covariate values. Several splitting algorithms have been proposed for generating trees from survival data. However, the choice of an appropriate algorithm is difficult and may also depend on clinical considerations. By means of a prognostic study of patients with gallbladder stones and of a simulation study, we compare the following splitting algorithms: log-rank statistic adjusted for measurement scale with (AP) and without (AU) pruning, exponential log-likelihood loss (EP), Kaplan-Meier (KP) distance of survival curves, unadjusted log-rank statistic (LP), martingale residuals (MP), and node impurity (ZP). With the exception of the AU algorithm (based on a Bonferroni-adjusted p-value driven stopping rule), trees are pruned using the measure of split-complexity, and optimally-sized trees are selected using cross-validation. The integrated Brier score is used for the evaluation of predictive models. According to the results of our simulation study and of the clinical example, we conclude that the AU, AP, EP, and LP algorithm may yield superior predictive accuracy. The choice among these four algorithms may be based on the required parsimonity and on medical considerations. (C) 2003 Elsevier B.V. All rights reserved.

关键词： recursive partitioning tree censored data splitting algorithm split-complexity Brier score measurement scale adjustment

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A strongly convergent algorithm for solving common variational inclusion with application to image recovery problems

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APPLIED NUMERICAL MATHEMATICS 2022年 173卷 239-248页

作者： Suparatulatorn, Raweerote Chaichana, Khuanchanok Chiang Mai Univ Fac Sci Dept Math Chiang Mai 50200 Thailand Chiang Mai Univ Adv Res Ctr Computat Simulat Chiang Mai 50200 Thailand

In this paper, we present an accelerated approach for addressing the common variational inclusion problem in real Hilbert spaces that incorporates several techniques. Under conventional and appropriate assumptions, the algorithm's strong convergence theorem is established, and the applicability and benefits of the innovative approach for image recovery problems are demonstrated. (C) 2021 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： splitting algorithm Common variational inclusion problem Inertial technique Image recovery problem Process innovation

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Bregman Proximal Linearized ADMM for Minimizing Separable Sums Coupled by a Difference of Functions

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2024年第2期203卷 1622-1658页

作者： Pham, Tan Nhat Dao, Minh N. Eberhard, Andrew Sultanova, Nargiz Federat Univ Australia Ctr New Energy Transit Res Ballarat Vic 3353 Australia RMIT Univ Sch Sci Melbourne Vic 3000 Australia Federat Univ Australia Ctr Smart Analyt Ballarat Vic 3353 Australia

In this paper, we develop a splitting algorithm incorporating Bregman distances to solve a broad class of linearly constrained composite optimization problems, whose objective function is the separable sum of possibly nonconvex nonsmooth functions and a smooth function, coupled by a difference of functions. This structure encapsulates numerous significant nonconvex and nonsmooth optimization problems in the current literature including the linearly constrained difference-of-convex problems. Relying on the successive linearization and alternating direction method of multipliers (ADMM), the proposed algorithm exhibits the global subsequential convergence to a stationary point of the underlying problem. We also establish the convergence of the full sequence generated by our algorithm under the Kurdyka-& Lstrok;ojasiewicz property and some mild assumptions. The efficiency of the proposed algorithm is tested on a robust principal component analysis problem and a nonconvex optimal power flow problem.

关键词： Alternating direction method of multipliers (ADMM) Bregman distance Composite optimization problem Difference of functions splitting algorithm Linear constraints Linearization Multi-block structure Nonconvex optimization

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Distributed forward-backward methods for ring networks

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2023年第3期86卷 845-870页

作者： Aragon-Artacho, Francisco J. Malitsky, Yura Tam, Matthew K. Torregrosa-Belen, David Univ Alicante Dept Math Alicante 03690 Spain Linkoping Univ Dept Math S-58183 Linkoping Sweden Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia

In this work, we propose and analyse forward-backward-type algorithms for finding a zero of the sum of finitely many monotone operators, which are not based on reduction to a two operator inclusion in the product space. Each iteration of the studied algorithms requires one resolvent evaluation per set-valued operator, one forward evaluation per cocoercive operator, and two forward evaluations per monotone operator. Unlike existing methods, the structure of the proposed algorithms are suitable for distributed, decentralised implementation in ring networks without needing global summation to enforce consensus between nodes.

关键词： Monotone operator Monotone inclusion splitting algorithm Forward-backward algorithm Distributed optimisation

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BEST APPROXIMATION FROM THE KUHN-TUCKER SET OF COMPOSITE MONOTONE INCLUSIONS

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NUMERICAL FUNCTIONAL ANALYSIS AND OPTIMIZATION 2015年第12期36卷 1513-1532页

作者： Alotaibi, Abdullah Combettes, Patrick L. Shahzad, Naseer King Abdulaziz Univ Dept Math Jeddah 21413 Saudi Arabia Univ Paris 06 Sorbonne Univ Lab Jacques Louis Lions UMR 7598 F-75005 Paris France

Kuhn-Tucker points play a fundamental role in the analysis and the numerical solution of monotone inclusion problems, providing in particular both primal and dual solutions. We propose a class of strongly convergent algorithms for constructing the best approximation to a reference point from the set of Kuhn-Tucker points of a general Hilbertian composite monotone inclusion problem. Applications to systems of coupled monotone inclusions are presented. Our framework does not impose additional assumptions on the operators present in the formulation, and it does not require knowledge of the norm of the linear operators involved in the compositions or the inversion of linear operators.

关键词： Best approximation Duality Haugazeau Monotone operator Primal-dual algorithm splitting algorithm Strong convergence

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About the Incorporation of Topological Prescriptions in CNNs for Medical Image Semantic Segmentation

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2024年第4期66卷 419-446页

作者： Lambert, Zoe Le Guyader, Carole Normandie Univ INSA Rouen Normandie UR 3226 LMI F-76000 Rouen France ENDSUM CEREMA F-76121 Le Grand Quevilly France

Incorporating prior knowledge into a segmentation task, whether it be under the form of geometrical constraints (area/volume penalisation, convexity enforcement, etc.) or of topological constraints (to preserve the contextual relations between objects, to monitor the number of connected components), proves to increase accuracy in medical image segmentation. In particular, it allows to compensate for the issue of weak boundary definition, of imbalanced classes, and to be more in line with anatomical consistency even though the data do not explicitly exhibit those features. This observation underpins the introduced contribution that aims, in a hybrid setting, to leverage the best of both worlds that variational methods and supervised deep learning approaches embody: (a) versatility and adaptability in the mathematical formulation of the problem to encode geometrical/topological constraints, (b) interpretability of the results for the former formalism, while (c) more efficient and effective processing models, (d) ability to become more proficient at learning intricate features and executing more computationally intensive tasks, for the latter one. To be more precise, a unified variational framework involving topological prescriptions in the training of convolutional neural networks through the design of a suitable penalty in the loss function is provided. These topological constraints are implicitly enforced by viewing the segmentation procedure as a registration task between the processed image and its associated ground truth under incompressibility conditions, thus making them homeomorphic. A very preliminary version (Lambert et al., in Calatroni, Donatelli, Morigi, Prato, Santacesaria (eds) Scale space and variational methods in computer vision, Springer, Berlin, 2023, pp. 363-375) of this work has been published in the proceedings of the Ninth International Conference on Scale Space and Variational Methods in Computer Vision, 2023. It contained neither all the theo

关键词： Nested variational/deep learning-based methods Joint segmentation/registration Nonlinear elasticity Incompressibility Nonconvex optimisation splitting algorithm

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