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274 results on '"Bagirov, A. M."'

2. Limited Memory Bundle Method for Clusterwise Linear Regression

Author

Karmitsa, Napsu, Bagirov, Adil M., Taheri, Sona, Joki, Kaisa, Valavanis, Kimon P., Series Editor, Antsaklis, P., Advisory Editor, Borne, P., Advisory Editor, Carelli, R., Advisory Editor, Fukuda, T., Advisory Editor, Gans, N.R., Advisory Editor, Harashima, F., Advisory Editor, Martinet, P., Advisory Editor, Monaco, S., Advisory Editor, Negenborn, R.R., Advisory Editor, Pascoal, António, Advisory Editor, Schmidt, G., Advisory Editor, Sobh, T.M., Advisory Editor, Tzafestas, C., Advisory Editor, Tuovinen, Tero, editor, Periaux, Jacques, editor, and Neittaanmäki, Pekka, editor

Published
2022
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4. Discrete Gradient Methods

Author

Bagirov, Adil M., Taheri, Sona, Karmitsa, Napsu, Bagirov, Adil M., editor, Gaudioso, Manlio, editor, Karmitsa, Napsu, editor, Mäkelä, Marko M., editor, and Taheri, Sona, editor

Published
2020
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6. Introduction

Author

Bagirov, Adil M., Gaudioso, Manlio, Karmitsa, Napsu, Mäkelä, Marko M., Taheri, Sona, Bagirov, Adil M., editor, Gaudioso, Manlio, editor, Karmitsa, Napsu, editor, Mäkelä, Marko M., editor, and Taheri, Sona, editor

Published
2020
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15. An Approximate ADMM for Solving Linearly Constrained Nonsmooth Optimization Problems with Two Blocks of Variables

Author

Bagirov, Adil M., Taheri, Sona, Bai, Fusheng, Wu, Zhiyou, Leugering, Günter, Managing Editor, Hintermueller, Michael, Managing Editor, Starovoitov, Victor Nikolayevich, Associate Editor, Kenmochi, Nobuyuki, Associate Editor, Hoppe, Ronald W., Associate Editor, Chen, Zhiming, Associate Editor, Hoffmann, Karl-Heinz, Honorary Editor, Hosseini, Seyedehsomayeh, editor, Mordukhovich, Boris S., editor, and Uschmajew, André, editor

Published
2019
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24. Single - particle correlations in events with the total disintegration of nuclei

Author

Suleimanov, M. K., Abdinov, O. B., Anoshin, A. I., Bagirov, A. M., Bogdanowicz, J., and Kuznetsov, A. A.

Subjects
High Energy Physics - Experiment
Abstract

New experimental data on the behaviour of the single-particle two-dimensional correlation functions R versus Q (Q is the number of nucleons emitted from nuc- lei) and Ap (Ap is the mass of projectile nuclei) are presented in this paper. The interactions of protons, d, 4He and 12C nuclei with carbon nuclei (at a momentum of 4.2 A GeV/c) are considered.The values of R are obtained separately for pi minus mesons and protons.In so doing,the values of R are normalized so that -1=

Published
1997
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36. Nonsmooth Optimization-Based Model and Algorithm for Semisupervised Clustering

Author

Bagirov, Adil M., Taheri, Sona, Bai, Fusheng, and Zheng, Fangying

Abstract

Using a nonconvex nonsmooth optimization approach, we introduce a model for semisupervised clustering (SSC) with pairwise constraints. In this model, the objective function is represented as a sum of three terms: the first term reflects the clustering error for unlabeled data points, the second term expresses the error for data points with must-link (ML) constraints, and the third term represents the error for data points with cannot-link (CL) constraints. This function is nonconvex and nonsmooth. To find its optimal solutions, we introduce an adaptive SSC (A-SSC) algorithm. This algorithm is based on the combination of the nonsmooth optimization method and an incremental approach, which involves the auxiliary SSC problem. The algorithm constructs clusters incrementally starting from one cluster and gradually adding one cluster center at each iteration. The solutions to the auxiliary SSC problem are utilized as starting points for solving the nonconvex SSC problem. The discrete gradient method (DGM) of nonsmooth optimization is applied to solve the underlying nonsmooth optimization problems. This method does not require subgradient evaluations and uses only function values. The performance of the A-SSC algorithm is evaluated and compared with four benchmarking SSC algorithms on one synthetic and 12 real-world datasets. Results demonstrate that the proposed algorithm outperforms the other four algorithms in identifying compact and well-separated clusters while satisfying most constraints.

Published
2023
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