Log in

Relevant bibliographies by topics / Convex minimization / Journal articles

To see the other types of publications on this topic, follow the link: Convex minimization.

Journal articles on the topic 'Convex minimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Convex minimization.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Li, Duan, Zhi-You Wu, Heung-Wing Joseph Lee, Xin-Min Yang, and Lian-Sheng Zhang. "Hidden Convex Minimization." Journal of Global Optimization 31, no. 2 (February 2005): 211–33. http://dx.doi.org/10.1007/s10898-004-5697-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Mayeli, Azita. "Non-convex Optimization via Strongly Convex Majorization-minimization." Canadian Mathematical Bulletin 63, no. 4 (December 10, 2019): 726–37. http://dx.doi.org/10.4153/s0008439519000730.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

3

Scarpa, Luca, and Ulisse Stefanelli. "Stochastic PDEs via convex minimization." Communications in Partial Differential Equations 46, no. 1 (October 14, 2020): 66–97. http://dx.doi.org/10.1080/03605302.2020.1831017.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Thach, P. T. "Convex minimization under Lipschitz constraints." Journal of Optimization Theory and Applications 64, no. 3 (March 1990): 595–614. http://dx.doi.org/10.1007/bf00939426.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mifflin, Robert, and Claudia Sagastizábal. "A -algorithm for convex minimization." Mathematical Programming 104, no. 2-3 (July 14, 2005): 583–608. http://dx.doi.org/10.1007/s10107-005-0630-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Shioura, Akiyoshi. "Minimization of an M-convex function." Discrete Applied Mathematics 84, no. 1-3 (May 1998): 215–20. http://dx.doi.org/10.1016/s0166-218x(97)00140-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

O'Hara, John G., Paranjothi Pillay, and Hong-Kun Xu. "Iterative Approaches to Convex Minimization Problems." Numerical Functional Analysis and Optimization 25, no. 5-6 (January 2004): 531–46. http://dx.doi.org/10.1081/nfa-200041707.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Ye, Qiaolin, Chunxia Zhao, Ning Ye, and Xiaobo Chen. "Localized twin SVM via convex minimization." Neurocomputing 74, no. 4 (January 2011): 580–87. http://dx.doi.org/10.1016/j.neucom.2010.09.015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Akagi, Goro, and Ulisse Stefanelli. "Doubly Nonlinear Equations as Convex Minimization." SIAM Journal on Mathematical Analysis 46, no. 3 (January 2014): 1922–45. http://dx.doi.org/10.1137/13091909x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Stefanov, Stefan M. "Convex separable minimization with box constraints." PAMM 7, no. 1 (December 2007): 2060045–46. http://dx.doi.org/10.1002/pamm.200700535.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Nesterov, Yurii. "Unconstrained Convex Minimization in Relative Scale." Mathematics of Operations Research 34, no. 1 (February 2009): 180–93. http://dx.doi.org/10.1287/moor.1080.0348.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Ceng, Lu-Chuan, Yeong-Cheng Liou, and Ching-Feng Wen. "Extragradient method for convex minimization problem." Journal of Inequalities and Applications 2014, no. 1 (2014): 444. http://dx.doi.org/10.1186/1029-242x-2014-444.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Howlett, P. G., and A. J. Zaslavski. "A porosity result in convex minimization." Abstract and Applied Analysis 2005, no. 3 (2005): 319–26. http://dx.doi.org/10.1155/aaa.2005.319.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

14

Rothvoss, Thomas. "Constructive Discrepancy Minimization for Convex Sets." SIAM Journal on Computing 46, no. 1 (January 2017): 224–34. http://dx.doi.org/10.1137/141000282.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Davoli, Elisa, and Ulisse Stefanelli. "Dynamic Perfect Plasticity as Convex Minimization." SIAM Journal on Mathematical Analysis 51, no. 2 (January 2019): 672–730. http://dx.doi.org/10.1137/17m1148864.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Tseng, P. "Decomposition algorithm for convex differentiable minimization." Journal of Optimization Theory and Applications 70, no. 1 (July 1991): 109–35. http://dx.doi.org/10.1007/bf00940507.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Nedić, Angelia. "Random algorithms for convex minimization problems." Mathematical Programming 129, no. 2 (June 4, 2011): 225–53. http://dx.doi.org/10.1007/s10107-011-0468-9.

Full text

APA, Harvard, Vancouver, ISO, and other styles

18

Baes, Michel, Timm Oertel, and Robert Weismantel. "Duality for mixed-integer convex minimization." Mathematical Programming 158, no. 1-2 (June 2, 2015): 547–64. http://dx.doi.org/10.1007/s10107-015-0917-y.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Nemirovskii, A. S., and Yu E. Nesterov. "Optimal methods of smooth convex minimization." USSR Computational Mathematics and Mathematical Physics 25, no. 2 (January 1985): 21–30. http://dx.doi.org/10.1016/0041-5553(85)90100-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Jeyakumar, V., and X. Q. Yang. "Convex composite minimization withC 1,1 functions." Journal of Optimization Theory and Applications 86, no. 3 (September 1995): 631–48. http://dx.doi.org/10.1007/bf02192162.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Papageorgiou, Nikolaos S., and Apostolos S. Papageorgiou. "Minimization of nonsmooth integral functionals." International Journal of Mathematics and Mathematical Sciences 15, no. 4 (1992): 673–79. http://dx.doi.org/10.1155/s0161171292000899.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

22

Mirolo, C., S. Carpin, and E. Pagello. "Incremental Convex Minimization for Computing Collision Translations of Convex Polyhedra." IEEE Transactions on Robotics 23, no. 3 (June 2007): 403–15. http://dx.doi.org/10.1109/tro.2007.895084.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

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

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

24

Liu, Yilin, Huiqian Du, Zexian Wang, and Wenbo Mei. "Convex MR brain image reconstruction via non-convex total variation minimization." International Journal of Imaging Systems and Technology 28, no. 4 (July 12, 2018): 246–53. http://dx.doi.org/10.1002/ima.22275.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

YANG, XIAONAN, and HONG-KUN XU. "Projection algorithms for composite minimization." Carpathian Journal of Mathematics 33, no. 3 (2017): 389–97. http://dx.doi.org/10.37193/cjm.2017.03.14.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

26

Richtárik, Peter. "Approximate Level Method for Nonsmooth Convex Minimization." Journal of Optimization Theory and Applications 152, no. 2 (September 9, 2011): 334–50. http://dx.doi.org/10.1007/s10957-011-9908-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Güler, Osman. "New Proximal Point Algorithms for Convex Minimization." SIAM Journal on Optimization 2, no. 4 (November 1992): 649–64. http://dx.doi.org/10.1137/0802032.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Bertsekas, Dimitri P., and Paul Tseng. "Partial Proximal Minimization Algorithms for Convex Pprogramming." SIAM Journal on Optimization 4, no. 3 (August 1994): 551–72. http://dx.doi.org/10.1137/0804031.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Nesterov, Yu. "Excessive Gap Technique in Nonsmooth Convex Minimization." SIAM Journal on Optimization 16, no. 1 (January 2005): 235–49. http://dx.doi.org/10.1137/s1052623403422285.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Tsintsadze, Z. A. "Optimal processes in smooth-convex minimization problems." Journal of Mathematical Sciences 148, no. 3 (January 2008): 399–480. http://dx.doi.org/10.1007/s10958-008-0011-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Ma, Shiqian. "Alternating Proximal Gradient Method for Convex Minimization." Journal of Scientific Computing 68, no. 2 (December 18, 2015): 546–72. http://dx.doi.org/10.1007/s10915-015-0150-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Lu, Yuan, Li-Ping Pang, Xi-Jun Liang, and Zun-Quan Xia. "An approximate decomposition algorithm for convex minimization." Journal of Computational and Applied Mathematics 234, no. 3 (June 2010): 658–66. http://dx.doi.org/10.1016/j.cam.2010.01.003.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Tseng, P. "Descent methods for convex essentially smooth minimization." Journal of Optimization Theory and Applications 71, no. 3 (December 1991): 425–63. http://dx.doi.org/10.1007/bf00941397.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Bereznev, V. A., V. G. Karmanov, and A. A. Tret'yakov. "The unconditional minimization of non-convex functions." USSR Computational Mathematics and Mathematical Physics 27, no. 6 (January 1987): 101–4. http://dx.doi.org/10.1016/0041-5553(87)90198-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Correa, Rafael, and Claude Lemaréchal. "Convergence of some algorithms for convex minimization." Mathematical Programming 62, no. 1-3 (February 1993): 261–75. http://dx.doi.org/10.1007/bf01585170.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Massey, Pedro, and Mariano Ruiz. "Minimization of convex functionals over frame operators." Advances in Computational Mathematics 32, no. 2 (August 20, 2008): 131–53. http://dx.doi.org/10.1007/s10444-008-9092-5.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Nesterov, Yurii, and Vladimir Spokoiny. "Random Gradient-Free Minimization of Convex Functions." Foundations of Computational Mathematics 17, no. 2 (November 30, 2015): 527–66. http://dx.doi.org/10.1007/s10208-015-9296-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Demyanov, Alexey V., Antonio Fuduli, and Giovanna Miglionico. "A bundle modification strategy for convex minimization." European Journal of Operational Research 180, no. 1 (July 2007): 38–47. http://dx.doi.org/10.1016/j.ejor.2006.04.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Boţ, Radu Ioan, and Christopher Hendrich. "Convex risk minimization via proximal splitting methods." Optimization Letters 9, no. 5 (October 9, 2014): 867–85. http://dx.doi.org/10.1007/s11590-014-0809-8.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Mirolo, Claudio. "Convex Minimization on a Grid and Applications." Journal of Algorithms 26, no. 2 (February 1998): 209–37. http://dx.doi.org/10.1006/jagm.1997.0908.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Ceng, Lu-Chuan, and 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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

42

Yazdi, M. "A new iterative method for generalized equilibrium and constrained convex minimization problems." Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica 74, no. 2 (December 28, 2020): 81. http://dx.doi.org/10.17951/a.2020.74.2.81-99.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

43

Yazdi, Maryam. "New iterative methods for equilibrium and constrained convex minimization problems." Asian-European Journal of Mathematics 12, no. 03 (May 27, 2019): 1950042. http://dx.doi.org/10.1142/s1793557119500426.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

44

Scott, C. H., T. R. Jefferson, and E. Sirri. "On duality for convex minimization with nested maxima." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 26, no. 4 (April 1985): 517–22. http://dx.doi.org/10.1017/s0334270000004690.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

45

Pedregal, Pablo, and Baisheng Yan. "On two-dimensional ferromagnetism." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 139, no. 3 (May 26, 2009): 575–94. http://dx.doi.org/10.1017/s0308210507000662.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

46

Hinderer, A., and M. Stieglitz. "Minimization of quasi-convex symmetric and of discretely quasi-convex symmetric functions." Optimization 36, no. 4 (January 1996): 321–32. http://dx.doi.org/10.1080/02331939608844187.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Botkin, Nikolai D., and Josef Stoer. "Minimization of convex functions on the convex hull of a point set." Mathematical Methods of Operations Research 62, no. 2 (October 6, 2005): 167–85. http://dx.doi.org/10.1007/s00186-005-0018-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Tian, Ming, and 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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

49

Tian, Ming, and 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.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

50

Dylewski, Robert. "Projection method with level control in convex minimization." Discussiones Mathematicae. Differential Inclusions, Control and Optimization 30, no. 1 (2010): 101. http://dx.doi.org/10.7151/dmdico.1114.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!