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Artículos de revistas sobre el tema "Convex minimization"

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Publicado: 4 de junio de 2021
Última modificación: 14 de febrero de 2022

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1

Li, Duan, Zhi-You Wu, Heung-Wing Joseph Lee, Xin-Min Yang y Lian-Sheng Zhang. "Hidden Convex Minimization". Journal of Global Optimization 31, n.º 2 (febrero de 2005): 211–33. http://dx.doi.org/10.1007/s10898-004-5697-5.

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2

Mayeli, Azita. "Non-convex Optimization via Strongly Convex Majorization-minimization". Canadian Mathematical Bulletin 63, n.º 4 (10 de diciembre de 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 tools to first construct a class of convex majorizers, which approximate the value of non-convex cost function locally, then use the MM algorithm to prove the existence of local minimum. The convergence of the algorithm is guaranteed when the iterative points $x^{(k)}$ are obtained in a ball centred at $x^{(k-1)}$ with small radius. We prove that the algorithm converges to a stationary point (local minimum) of cost function when the surregators are strongly convex.
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3

Scarpa, Luca y Ulisse Stefanelli. "Stochastic PDEs via convex minimization". Communications in Partial Differential Equations 46, n.º 1 (14 de octubre de 2020): 66–97. http://dx.doi.org/10.1080/03605302.2020.1831017.

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4

Thach, P. T. "Convex minimization under Lipschitz constraints". Journal of Optimization Theory and Applications 64, n.º 3 (marzo de 1990): 595–614. http://dx.doi.org/10.1007/bf00939426.

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5

Mifflin, Robert y Claudia Sagastizábal. "A -algorithm for convex minimization". Mathematical Programming 104, n.º 2-3 (14 de julio de 2005): 583–608. http://dx.doi.org/10.1007/s10107-005-0630-3.

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6

Shioura, Akiyoshi. "Minimization of an M-convex function". Discrete Applied Mathematics 84, n.º 1-3 (mayo de 1998): 215–20. http://dx.doi.org/10.1016/s0166-218x(97)00140-6.

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7

O'Hara, John G., Paranjothi Pillay y Hong-Kun Xu. "Iterative Approaches to Convex Minimization Problems". Numerical Functional Analysis and Optimization 25, n.º 5-6 (enero de 2004): 531–46. http://dx.doi.org/10.1081/nfa-200041707.

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8

Ye, Qiaolin, Chunxia Zhao, Ning Ye y Xiaobo Chen. "Localized twin SVM via convex minimization". Neurocomputing 74, n.º 4 (enero de 2011): 580–87. http://dx.doi.org/10.1016/j.neucom.2010.09.015.

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9

Akagi, Goro y Ulisse Stefanelli. "Doubly Nonlinear Equations as Convex Minimization". SIAM Journal on Mathematical Analysis 46, n.º 3 (enero de 2014): 1922–45. http://dx.doi.org/10.1137/13091909x.

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10

Stefanov, Stefan M. "Convex separable minimization with box constraints". PAMM 7, n.º 1 (diciembre de 2007): 2060045–46. http://dx.doi.org/10.1002/pamm.200700535.

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11

Nesterov, Yurii. "Unconstrained Convex Minimization in Relative Scale". Mathematics of Operations Research 34, n.º 1 (febrero de 2009): 180–93. http://dx.doi.org/10.1287/moor.1080.0348.

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12

Ceng, Lu-Chuan, Yeong-Cheng Liou y Ching-Feng Wen. "Extragradient method for convex minimization problem". Journal of Inequalities and Applications 2014, n.º 1 (2014): 444. http://dx.doi.org/10.1186/1029-242x-2014-444.

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13

Howlett, P. G. y A. J. Zaslavski. "A porosity result in convex minimization". Abstract and Applied Analysis 2005, n.º 3 (2005): 319–26. http://dx.doi.org/10.1155/aaa.2005.319.

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We study the minimization problemf(x)→min,x∈C, wherefbelongs to a complete metric spaceℳof convex functions and the setCis a countable intersection of a decreasing sequence of closed convex setsCiin a reflexive Banach space. Letℱbe the set of allf∈ℳfor which the solutions of the minimization problem over the setCiconverge strongly asi→∞to the solution over the setC. In our recent work we show that the setℱcontains an everywhere denseGδsubset ofℳ. In this paper, we show that the complementℳ\ℱis not only of the first Baire category but also aσ-porous set.
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14

Rothvoss, Thomas. "Constructive Discrepancy Minimization for Convex Sets". SIAM Journal on Computing 46, n.º 1 (enero de 2017): 224–34. http://dx.doi.org/10.1137/141000282.

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15

Davoli, Elisa y Ulisse Stefanelli. "Dynamic Perfect Plasticity as Convex Minimization". SIAM Journal on Mathematical Analysis 51, n.º 2 (enero de 2019): 672–730. http://dx.doi.org/10.1137/17m1148864.

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16

Tseng, P. "Decomposition algorithm for convex differentiable minimization". Journal of Optimization Theory and Applications 70, n.º 1 (julio de 1991): 109–35. http://dx.doi.org/10.1007/bf00940507.

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17

Nedić, Angelia. "Random algorithms for convex minimization problems". Mathematical Programming 129, n.º 2 (4 de junio de 2011): 225–53. http://dx.doi.org/10.1007/s10107-011-0468-9.

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18

Baes, Michel, Timm Oertel y Robert Weismantel. "Duality for mixed-integer convex minimization". Mathematical Programming 158, n.º 1-2 (2 de junio de 2015): 547–64. http://dx.doi.org/10.1007/s10107-015-0917-y.

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19

Nemirovskii, A. S. y Yu E. Nesterov. "Optimal methods of smooth convex minimization". USSR Computational Mathematics and Mathematical Physics 25, n.º 2 (enero de 1985): 21–30. http://dx.doi.org/10.1016/0041-5553(85)90100-4.

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20

Jeyakumar, V. y X. Q. Yang. "Convex composite minimization withC 1,1 functions". Journal of Optimization Theory and Applications 86, n.º 3 (septiembre de 1995): 631–48. http://dx.doi.org/10.1007/bf02192162.

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21

Papageorgiou, Nikolaos S. y Apostolos S. Papageorgiou. "Minimization of nonsmooth integral functionals". International Journal of Mathematics and Mathematical Sciences 15, n.º 4 (1992): 673–79. http://dx.doi.org/10.1155/s0161171292000899.

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In this paper we examine optimization problems involving multidimensional nonsmooth integral functionals defined on Sobolev spaces. We obtain necessary and sufficient conditions for optimality in convex, finite dimensional problems using techniques from convex analysis and in nonconvex, finite dimensional problems, using the subdifferential of Clarke. We also consider problems with infinite dimensional state space and we finally present two examples.
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22

Mirolo, C., S. Carpin y E. Pagello. "Incremental Convex Minimization for Computing Collision Translations of Convex Polyhedra". IEEE Transactions on Robotics 23, n.º 3 (junio de 2007): 403–15. http://dx.doi.org/10.1109/tro.2007.895084.

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23

Heskes, T. "Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies". Journal of Artificial Intelligence Research 26 (30 de junio de 2006): 153–90. http://dx.doi.org/10.1613/jair.1933.

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Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms have been shown to correspond to extrema of the Bethe and Kikuchi free energy, both of which are approximations of the exact Helmholtz free energy. However, belief propagation does not always converge, which motivates approaches that explicitly minimize the Kikuchi/Bethe free energy, such as CCCP and UPS. Here we describe a class of algorithms that solves this typically non-convex constrained minimization problem through a sequence of convex constrained minimizations of upper bounds on the Kikuchi free energy. Intuitively one would expect tighter bounds to lead to faster algorithms, which is indeed convincingly demonstrated in our simulations. Several ideas are applied to obtain tight convex bounds that yield dramatic speed-ups over CCCP.
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24

Liu, Yilin, Huiqian Du, Zexian Wang y Wenbo Mei. "Convex MR brain image reconstruction via non-convex total variation minimization". International Journal of Imaging Systems and Technology 28, n.º 4 (12 de julio de 2018): 246–53. http://dx.doi.org/10.1002/ima.22275.

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25

YANG, XIAONAN y HONG-KUN XU. "Projection algorithms for composite minimization". Carpathian Journal of Mathematics 33, n.º 3 (2017): 389–97. http://dx.doi.org/10.37193/cjm.2017.03.14.

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Parallel and cyclic projection algorithms are proposed for minimizing the sum of a finite family of convex functions over the intersection of a finite family of closed convex subsets of a Hilbert space. These algorithms consist of two steps. Once the kth iterate is constructed, an inner circle of gradient descent process is executed through each local function, and then a parallel or cyclic projection process is applied to produce the (k + 1) iterate. These algorithms are proved to converge to an optimal solution of the composite minimization problem under investigation upon assuming boundedness of the gradients at the iterates of the local functions and the stepsizes being chosen appropriately.
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26

Richtárik, Peter. "Approximate Level Method for Nonsmooth Convex Minimization". Journal of Optimization Theory and Applications 152, n.º 2 (9 de septiembre de 2011): 334–50. http://dx.doi.org/10.1007/s10957-011-9908-1.

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27

Güler, Osman. "New Proximal Point Algorithms for Convex Minimization". SIAM Journal on Optimization 2, n.º 4 (noviembre de 1992): 649–64. http://dx.doi.org/10.1137/0802032.

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28

Bertsekas, Dimitri P. y Paul Tseng. "Partial Proximal Minimization Algorithms for Convex Pprogramming". SIAM Journal on Optimization 4, n.º 3 (agosto de 1994): 551–72. http://dx.doi.org/10.1137/0804031.

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29

Nesterov, Yu. "Excessive Gap Technique in Nonsmooth Convex Minimization". SIAM Journal on Optimization 16, n.º 1 (enero de 2005): 235–49. http://dx.doi.org/10.1137/s1052623403422285.

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30

Tsintsadze, Z. A. "Optimal processes in smooth-convex minimization problems". Journal of Mathematical Sciences 148, n.º 3 (enero de 2008): 399–480. http://dx.doi.org/10.1007/s10958-008-0011-6.

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31

Ma, Shiqian. "Alternating Proximal Gradient Method for Convex Minimization". Journal of Scientific Computing 68, n.º 2 (18 de diciembre de 2015): 546–72. http://dx.doi.org/10.1007/s10915-015-0150-0.

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32

Lu, Yuan, Li-Ping Pang, Xi-Jun Liang y Zun-Quan Xia. "An approximate decomposition algorithm for convex minimization". Journal of Computational and Applied Mathematics 234, n.º 3 (junio de 2010): 658–66. http://dx.doi.org/10.1016/j.cam.2010.01.003.

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33

Tseng, P. "Descent methods for convex essentially smooth minimization". Journal of Optimization Theory and Applications 71, n.º 3 (diciembre de 1991): 425–63. http://dx.doi.org/10.1007/bf00941397.

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34

Bereznev, V. A., V. G. Karmanov y A. A. Tret'yakov. "The unconditional minimization of non-convex functions". USSR Computational Mathematics and Mathematical Physics 27, n.º 6 (enero de 1987): 101–4. http://dx.doi.org/10.1016/0041-5553(87)90198-4.

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35

Correa, Rafael y Claude Lemaréchal. "Convergence of some algorithms for convex minimization". Mathematical Programming 62, n.º 1-3 (febrero de 1993): 261–75. http://dx.doi.org/10.1007/bf01585170.

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36

Massey, Pedro y Mariano Ruiz. "Minimization of convex functionals over frame operators". Advances in Computational Mathematics 32, n.º 2 (20 de agosto de 2008): 131–53. http://dx.doi.org/10.1007/s10444-008-9092-5.

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37

Nesterov, Yurii y Vladimir Spokoiny. "Random Gradient-Free Minimization of Convex Functions". Foundations of Computational Mathematics 17, n.º 2 (30 de noviembre de 2015): 527–66. http://dx.doi.org/10.1007/s10208-015-9296-2.

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38

Demyanov, Alexey V., Antonio Fuduli y Giovanna Miglionico. "A bundle modification strategy for convex minimization". European Journal of Operational Research 180, n.º 1 (julio de 2007): 38–47. http://dx.doi.org/10.1016/j.ejor.2006.04.005.

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39

Boţ, Radu Ioan y Christopher Hendrich. "Convex risk minimization via proximal splitting methods". Optimization Letters 9, n.º 5 (9 de octubre de 2014): 867–85. http://dx.doi.org/10.1007/s11590-014-0809-8.

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40

Mirolo, Claudio. "Convex Minimization on a Grid and Applications". Journal of Algorithms 26, n.º 2 (febrero de 1998): 209–37. http://dx.doi.org/10.1006/jagm.1997.0908.

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41

Ceng, Lu-Chuan y Ching-Feng Wen. "Hybrid Gradient-Projection Algorithm for Solving Constrained Convex Minimization Problems with Generalized Mixed Equilibrium Problems". Journal of Function Spaces and Applications 2012 (2012): 1–26. http://dx.doi.org/10.1155/2012/678353.

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It is well known that the gradient-projection algorithm (GPA) for solving constrained convex minimization problems has been proven to have only weak convergence unless the underlying Hilbert space is finite dimensional. In this paper, we introduce a new hybrid gradient-projection algorithm for solving constrained convex minimization problems with generalized mixed equilibrium problems in a real Hilbert space. It is proven that three sequences generated by this algorithm converge strongly to the unique solution of some variational inequality, which is also a common element of the set of solutions of a constrained convex minimization problem, the set of solutions of a generalized mixed equilibrium problem, and the set of fixed points of a strict pseudocontraction in a real Hilbert space.
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42

Yazdi, M. "A new iterative method for generalized equilibrium and constrained convex minimization problems". Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica 74, n.º 2 (28 de diciembre de 2020): 81. http://dx.doi.org/10.17951/a.2020.74.2.81-99.

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The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this paper, we combine the GPA and averaged mapping approach to propose an explicit composite iterative scheme for finding a common solution of a generalized equilibrium problem and a constrained convex minimization problem. Then, we prove a strong convergence theorem which improves and extends some recent results.
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43

Yazdi, Maryam. "New iterative methods for equilibrium and constrained convex minimization problems". Asian-European Journal of Mathematics 12, n.º 03 (27 de mayo de 2019): 1950042. http://dx.doi.org/10.1142/s1793557119500426.

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The gradient-projection algorithm (GPA) plays an important role in solving constrained convex minimization problems. In this paper, we combine the GPA and averaged mapping approach to propose implicit and explicit composite iterative schemes for finding a common solution of an equilibrium problem and a constrained convex minimization problem. Then, we prove some strong convergence theorems which improve and extend some recent results.
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44

Scott, C. H., T. R. Jefferson y E. Sirri. "On duality for convex minimization with nested maxima". Journal of the Australian Mathematical Society. Series B. Applied Mathematics 26, n.º 4 (abril de 1985): 517–22. http://dx.doi.org/10.1017/s0334270000004690.

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AbstractIn this paper, we consider convex programs with linear constraints where the objective function involves nested maxima of linear functions as well as a convex function. A dual program is constructed which has interpretational significance and may be easier to solve than the primal formulation. A numerical example is given to illustrate the method.
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45

Pedregal, Pablo y Baisheng Yan. "On two-dimensional ferromagnetism". Proceedings of the Royal Society of Edinburgh: Section A Mathematics 139, n.º 3 (26 de mayo de 2009): 575–94. http://dx.doi.org/10.1017/s0308210507000662.

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We present a new method for solving the minimization problem in ferromagnetism. Our method is based on replacing the non-local non-convex total energy of magnetization by a new local non-convex energy of divergence-free fields. Such a general method works in all dimensions. However, for the two-dimensional case, since the divergence-free fields are equivalent to the rotated gradients, this new energy can be written as an integral functional of gradients and hence the minimization problem can be solved by some recent non-convex minimization procedures in the calculus of variations. We focus on the two-dimensional case in this paper and leave the three-dimensional situation to future work. Special emphasis is placed on the analysis of the existence/non-existence depending on the applied field and the physical domain.
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46

Hinderer, A. y M. Stieglitz. "Minimization of quasi-convex symmetric and of discretely quasi-convex symmetric functions". Optimization 36, n.º 4 (enero de 1996): 321–32. http://dx.doi.org/10.1080/02331939608844187.

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47

Botkin, Nikolai D. y Josef Stoer. "Minimization of convex functions on the convex hull of a point set". Mathematical Methods of Operations Research 62, n.º 2 (6 de octubre de 2005): 167–85. http://dx.doi.org/10.1007/s00186-005-0018-4.

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48

Tian, Ming y Min-Min Li. "A Hybrid Gradient-Projection Algorithm for Averaged Mappings in Hilbert Spaces". Journal of Applied Mathematics 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/782960.

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It is well known that the gradient-projection algorithm (GPA) is very useful in solving constrained convex minimization problems. In this paper, we combine a general iterative method with the gradient-projection algorithm to propose a hybrid gradient-projection algorithm and prove that the sequence generated by the hybrid gradient-projection algorithm converges in norm to a minimizer of constrained convex minimization problems which solves a variational inequality.
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49

Tian, Ming y Jun-Ying Gong. "Strong Convergence of Modified Algorithms Based on the Regularization for the Constrained Convex Minimization Problem". Abstract and Applied Analysis 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/870102.

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As is known, the regularization method plays an important role in solving constrained convex minimization problems. Based on the idea of regularization, implicit and explicit iterative algorithms are proposed in this paper and the sequences generated by the algorithms can converge strongly to a solution of the constrained convex minimization problem, which also solves a certain variational inequality. As an application, we also apply the algorithm to solve the split feasibility problem.
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50

Dylewski, Robert. "Projection method with level control in convex minimization". Discussiones Mathematicae. Differential Inclusions, Control and Optimization 30, n.º 1 (2010): 101. http://dx.doi.org/10.7151/dmdico.1114.

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