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Artykuły w czasopismach na temat „Convex optimization”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

Luethi, Hans-Jakob. "Convex Optimization." Journal of the American Statistical Association 100, no. 471 (2005): 1097. http://dx.doi.org/10.1198/jasa.2005.s41.

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2

Ceria, Sebastián, and João Soares. "Convex programming for disjunctive convex optimization." Mathematical Programming 86, no. 3 (1999): 595–614. http://dx.doi.org/10.1007/s101070050106.

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3

Lasserre, Jean B. "On convex optimization without convex representation." Optimization Letters 5, no. 4 (2011): 549–56. http://dx.doi.org/10.1007/s11590-011-0323-1.

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4

Ben-Tal, A., and A. Nemirovski. "Robust Convex Optimization." Mathematics of Operations Research 23, no. 4 (1998): 769–805. http://dx.doi.org/10.1287/moor.23.4.769.

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5

Tilahun, Surafel Luleseged. "Convex Grey Optimization." RAIRO - Operations Research 53, no. 1 (2019): 339–49. http://dx.doi.org/10.1051/ro/2018088.

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Many optimization problems are formulated from a real scenario involving incomplete information due to uncertainty in reality. The uncertainties can be expressed with appropriate probability distributions or fuzzy numbers with a membership function, if enough information can be accessed for the construction of either the probability density function or the membership of the fuzzy numbers. However, in some cases there may not be enough information for that and grey numbers need to be used. A grey number is an interval number to represent the value of a quantity. Its exact value or the likelihoo

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6

Ubhaya, Vasant A. "Quasi-convex optimization." Journal of Mathematical Analysis and Applications 116, no. 2 (1986): 439–49. http://dx.doi.org/10.1016/s0022-247x(86)80008-7.

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7

Onn, Shmuel. "Convex Matroid Optimization." SIAM Journal on Discrete Mathematics 17, no. 2 (2003): 249–53. http://dx.doi.org/10.1137/s0895480102408559.

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8

Pardalos, Panos M. "Convex optimization theory." Optimization Methods and Software 25, no. 3 (2010): 487. http://dx.doi.org/10.1080/10556781003625177.

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9

Onn, Shmuel, and Uriel G. Rothblum. "Convex Combinatorial Optimization." Discrete & Computational Geometry 32, no. 4 (2004): 549–66. http://dx.doi.org/10.1007/s00454-004-1138-y.

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10

Mayeli, Azita. "Non-convex Optimization via Strongly Convex Majorization-minimization." Canadian Mathematical Bulletin 63, no. 4 (2019): 726–37. http://dx.doi.org/10.4153/s0008439519000730.

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AbstractIn this paper, we introduce a class of nonsmooth nonconvex optimization problems, and we propose to use a local iterative minimization-majorization (MM) algorithm to find an optimal solution for the optimization problem. The cost functions in our optimization problems are an extension of convex functions with MC separable penalty, which were previously introduced by Ivan Selesnick. These functions are not convex; therefore, convex optimization methods cannot be applied here to prove the existence of optimal minimum point for these functions. For our purpose, we use convex analysis tool

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11

Agrawal, Akshay, Shane Barratt, and Stephen Boyd. "Learning Convex Optimization Models." IEEE/CAA Journal of Automatica Sinica 8, no. 8 (2021): 1355–64. http://dx.doi.org/10.1109/jas.2021.1004075.

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12

Wiesemann, Wolfram, Daniel Kuhn, and Melvyn Sim. "Distributionally Robust Convex Optimization." Operations Research 62, no. 6 (2014): 1358–76. http://dx.doi.org/10.1287/opre.2014.1314.

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13

Kryazhimskii, Arkadii V. "Convex Optimization Via Feedbacks." SIAM Journal on Control and Optimization 37, no. 1 (1998): 278–302. http://dx.doi.org/10.1137/s036301299528030x.

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14

Tatarenko, Tatiana, and Behrouz Touri. "Non-Convex Distributed Optimization." IEEE Transactions on Automatic Control 62, no. 8 (2017): 3744–57. http://dx.doi.org/10.1109/tac.2017.2648041.

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15

Gershman, Alex, Nicholas Sidiropoulos, Shahram Shahbazpanahi, Mats Bengtsson, and Bjorn Ottersten. "Convex Optimization-Based Beamforming." IEEE Signal Processing Magazine 27, no. 3 (2010): 62–75. http://dx.doi.org/10.1109/msp.2010.936015.

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16

Bard, Jonathan F. "Convex two-level optimization." Mathematical Programming 40-40, no. 1-3 (1988): 15–27. http://dx.doi.org/10.1007/bf01580720.

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17

Lesage-Landry, Antoine, Iman Shames, and Joshua A. Taylor. "Predictive online convex optimization." Automatica 113 (March 2020): 108771. http://dx.doi.org/10.1016/j.automatica.2019.108771.

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18

Heaton, Howard, Xiaohan Chen, Zhangyang Wang, and Wotao Yin. "Safeguarded Learned Convex Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7848–55. http://dx.doi.org/10.1609/aaai.v37i6.25950.

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Applications abound in which optimization problems must be repeatedly solved, each time with new (but similar) data. Analytic optimization algorithms can be hand-designed to provably solve these problems in an iterative fashion. On one hand, data-driven algorithms can "learn to optimize" (L2O) with much fewer iterations and similar cost per iteration as general-purpose optimization algorithms. On the other hand, unfortunately, many L2O algorithms lack converge guarantees. To fuse the advantages of these approaches, we present a Safe-L2O framework. Safe-L2O updates incorporate a safeguard to gu

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19

Lasserre, Jean B. "Erratum to: On convex optimization without convex representation." Optimization Letters 8, no. 5 (2014): 1795–96. http://dx.doi.org/10.1007/s11590-014-0735-9.

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20

Belotti, Pietro, Christian Kirches, Sven Leyffer, Jeff Linderoth, James Luedtke, and Ashutosh Mahajan. "Mixed-integer nonlinear optimization." Acta Numerica 22 (April 2, 2013): 1–131. http://dx.doi.org/10.1017/s0962492913000032.

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Many optimal decision problems in scientific, engineering, and public sector applications involve both discrete decisions and nonlinear system dynamics that affect the quality of the final design or plan. These decision problems lead to mixed-integer nonlinear programming (MINLP) problems that combine the combinatorial difficulty of optimizing over discrete variable sets with the challenges of handling nonlinear functions. We review models and applications of MINLP, and survey the state of the art in methods for solving this challenging class of problems.Most solution methods for MINLP apply s

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21

Popovici, Nicolae. "Convexité au sens direct ou inverse et applications dans l'optimisation vectorielle." Journal of Numerical Analysis and Approximation Theory 29, no. 1 (2000): 75–82. http://dx.doi.org/10.33993/jnaat291-656.

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(in English) The aim of this paper is to study vector optimization poblems involving objective functions which are convex in some direct or inverse sense (i.e. a special class of cone-quasiconvex functions). In particular, it is shown that the image of the objective function is a cone-convex set, property which is important from the scalarization point of view in vector optimization. (in French) Le but de cet article est d'etudier les problemes d'optimisation vectorielles ayant des fonctions objectifs convexes au sens direct ou inverse. Il s'agit notamment de donner des conditions suffis

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22

Lan, Yu, and Ji Li. "Mars Ascent Trajectory Optimization Based on Convex Optimization." Journal of Physics: Conference Series 2364, no. 1 (2022): 012013. http://dx.doi.org/10.1088/1742-6596/2364/1/012013.

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Abstract In this paper, a Mars ascent trajectory optimization algorithm based on convex optimization is designed for the Mars ascent trajectory optimization problem. The main accomplishment of this paper is the trajectory optimization of the second-stage ignition based on the convex optimization. For the first-stage ignition, open-loop guidance is used. In this stage, the ascender flies using the maximum thrust to track the attitude profile, thus completing the ascent flight control. In the presence of errors in the first-stage ignition, the experiment generates multiple sets of first-stage sh

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23

Ni, Zhitong, Andrew Jian Zhang, Ren-Ping Liu, and Kai Yang. "Doubly Constrained Waveform Optimization for Integrated Sensing and Communications." Sensors 23, no. 13 (2023): 5988. http://dx.doi.org/10.3390/s23135988.

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This paper investigates threshold-constrained joint waveform optimization for an integrated sensing and communication (ISAC) system. Unlike existing studies, we employ mutual information (MI) and sum rate (SR) as sensing and communication metrics, respectively, and optimize the waveform under constraints to both metrics simultaneously. This provides significant flexibility in meeting system performance. We formulate three different optimization problems that constrain the radar performance only, the communication performance only, and the ISAC performance, respectively. New techniques are deve

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24

Sun, Xiang-Kai, and Hong-Yong Fu. "A Note on Optimality Conditions for DC Programs Involving Composite Functions." Abstract and Applied Analysis 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/203467.

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By using the formula of theε-subdifferential for the sum of a convex function with a composition of convex functions, some necessary and sufficient optimality conditions for a DC programming problem involving a composite function are obtained. As applications, a composed convex optimization problem, a DC optimization problem, and a convex optimization problem with a linear operator are examined at the end of this paper.

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25

Wang, Xuanxuan, Wujun Ji, and Yun Gao. "Optimization Strategy of the Electric Vehicle Power Battery Based on the Convex Optimization Algorithm." Processes 11, no. 5 (2023): 1416. http://dx.doi.org/10.3390/pr11051416.

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With the development of the electric vehicle industry, electric vehicles have provided more choices for people. However, the performance of electric vehicles needs improvement, which makes most consumers take a wait-and-see attitude. Therefore, finding a method that can effectively improve the performance of electric vehicles is of great significance. To improve the current performance of electric vehicles, a convex optimization algorithm is proposed to optimize the motor model and power battery parameters of electric vehicles, improving the overall performance of electric vehicles. The perfor

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26

Chen, Po-Yu, and Ivan W. Selesnick. "Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization." IEEE Transactions on Signal Processing 62, no. 13 (2014): 3464–78. http://dx.doi.org/10.1109/tsp.2014.2329274.

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27

Dutta, Joydeep. "Barrier method in nonsmooth convex optimization without convex representation." Optimization Letters 9, no. 6 (2014): 1177–85. http://dx.doi.org/10.1007/s11590-014-0811-1.

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28

Rixon Fuchs, Louise, Atsuto Maki, and Andreas Gällström. "Optimization Method for Wide Beam Sonar Transmit Beamforming." Sensors 22, no. 19 (2022): 7526. http://dx.doi.org/10.3390/s22197526.

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Imaging and mapping sonars such as forward-looking sonars (FLS) and side-scan sonars (SSS) are sensors frequently used onboard autonomous underwater vehicles. To acquire information from around the vehicle, it is desirable for these sonar systems to insonify a large area; thus, the sonar transmit beampattern should have a wide field of view. In this work, we study the problem of the optimization of wide transmission beampatterns. We consider the conventional phased-array beampattern design problem where all array elements transmit an identical waveform. The complex weight vector is adjusted to

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29

Salman, Abbas Musleh, Ahmed Alridha, and Ahmed Hadi Hussain. "Some Topics on Convex Optimization." Journal of Physics: Conference Series 1818, no. 1 (2021): 012171. http://dx.doi.org/10.1088/1742-6596/1818/1/012171.

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30

Ghavamzadeh, Mohammed, Shie Mannor, Joelle Pineau, and Aviv Tamar. "Convex Optimization: Algorithms and Complexity." Foundations and Trends® in Machine Learning 8, no. 5-6 (2015): 359–483. http://dx.doi.org/10.1561/2200000049.

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31

Bubeck, Sébastien. "Convex Optimization: Algorithms and Complexity." Foundations and Trends® in Machine Learning 8, no. 3-4 (2015): 231–357. http://dx.doi.org/10.1561/2200000050.

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32

Hazan, Elad. "Introduction to Online Convex Optimization." Foundations and Trends® in Optimization 2, no. 3-4 (2016): 157–325. http://dx.doi.org/10.1561/2400000013.

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33

Boyd, Stephen P. "Real-time Embedded Convex Optimization." IFAC Proceedings Volumes 42, no. 11 (2009): 9. http://dx.doi.org/10.3182/20090712-4-tr-2008.00004.

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34

Drusvyatskiy, Dmitriy, and Adrian S. Lewis. "Generic nondegeneracy in convex optimization." Proceedings of the American Mathematical Society 139, no. 7 (2010): 2519–27. http://dx.doi.org/10.1090/s0002-9939-2010-10692-5.

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35

Selim, S. Z. "Optimization of linear-convex programs." Optimization 29, no. 4 (1994): 319–31. http://dx.doi.org/10.1080/02331939408843961.

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36

Simpkins, Alex. "Convex Optimization [On the Shelf]." IEEE Robotics & Automation Magazine 20, no. 4 (2013): 164–65. http://dx.doi.org/10.1109/mra.2013.2283189.

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37

Hu, T. C., Victor Klee, and David Larman. "Optimization of Globally Convex Functions." SIAM Journal on Control and Optimization 27, no. 5 (1989): 1026–47. http://dx.doi.org/10.1137/0327055.

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38

Temlyakov, V. N. "Greedy expansions in convex optimization." Proceedings of the Steklov Institute of Mathematics 284, no. 1 (2014): 244–62. http://dx.doi.org/10.1134/s0081543814010180.

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39

Adivar, Murat, and Shu-Cherng Fang. "Convex optimization on mixed domains." Journal of Industrial & Management Optimization 8, no. 1 (2012): 189–227. http://dx.doi.org/10.3934/jimo.2012.8.189.

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40

Park, Poogyeon, and Thomas Kailath. "H filtering via convex optimization." International Journal of Control 66, no. 1 (1997): 15–22. http://dx.doi.org/10.1080/002071797224793.

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41

Chen, Niangjun, Anish Agarwal, Adam Wierman, Siddharth Barman, and Lachlan L. H. Andrew. "Online Convex Optimization Using Predictions." ACM SIGMETRICS Performance Evaluation Review 43, no. 1 (2015): 191–204. http://dx.doi.org/10.1145/2796314.2745854.

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42

Gawlitza, Thomas Martin, Helmut Seidl, Assalé Adjé, Stéphane Gaubert, and Éric Goubault. "Abstract interpretation meets convex optimization." Journal of Symbolic Computation 47, no. 12 (2012): 1416–46. http://dx.doi.org/10.1016/j.jsc.2011.12.048.

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43

Joshi, S., and S. Boyd. "Sensor Selection via Convex Optimization." IEEE Transactions on Signal Processing 57, no. 2 (2009): 451–62. http://dx.doi.org/10.1109/tsp.2008.2007095.

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44

Rantzer, Anders. "Dynamic programming via convex optimization." IFAC Proceedings Volumes 32, no. 2 (1999): 2059–64. http://dx.doi.org/10.1016/s1474-6670(17)56349-1.

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45

Luan, Nguyen Ngoc, and Jen-Chih Yao. "Generalized polyhedral convex optimization problems." Journal of Global Optimization 75, no. 3 (2019): 789–811. http://dx.doi.org/10.1007/s10898-019-00763-4.

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46

Kryazhimskii, A. V., and R. A. Usachev. "Convex two-level optimization problem." Computational Mathematics and Modeling 19, no. 1 (2008): 73–101. http://dx.doi.org/10.1007/s10598-008-0007-6.

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47

Pintér, J. "Global optimization on convex sets." Operations-Research-Spektrum 8, no. 4 (1986): 197–202. http://dx.doi.org/10.1007/bf01721128.

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48

Tsitsiklis, John N., and Zhi-Quan Luo. "Communication complexity of convex optimization." Journal of Complexity 3, no. 3 (1987): 231–43. http://dx.doi.org/10.1016/0885-064x(87)90013-6.

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Kulikov, A. N., and V. R. Fazylov. "Convex optimization with prescribed accuracy." USSR Computational Mathematics and Mathematical Physics 30, no. 3 (1990): 16–22. http://dx.doi.org/10.1016/0041-5553(90)90185-u.

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50

Nguyen, Hao, and Guergana Petrova. "Greedy Strategies for Convex Optimization." Calcolo 54, no. 1 (2016): 207–24. http://dx.doi.org/10.1007/s10092-016-0183-2.

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