Figure 1.
Performance profiles based on the CPU time (left) and the number $ N_f $ of function evaluations (right)
-
Previous Article
On phaseless compressed sensing with partially known support
- JIMO Home
- This Issue
-
Next Article
Solving higher order nonlinear ordinary differential equations with least squares support vector machines
May
2020, 16(3): 1503-1518.
doi: 10.3934/jimo.2019013
A class of accelerated conjugate-gradient-like methods based on a modified secant equation
Department of Applied Mathematics, Hainan University, Haikou 570228, China |
This paper proposes a new class of accelerated conjugate-gradient-like algorithms for solving large scale unconstrained optimization problems, which combine the idea of accelerated adaptive Perry conjugate gradient algorithms proposed by Andrei (2017) with the modified secant condition and the nonmonotone line search technique. An attractive property of the proposed methods is that the search direction always provides sufficient descent step which is independent of the line search used and the convexity of objective function. Under common assumptions, it is proven that the proposed methods possess global convergence for nonconvex smooth functions, and R-linear convergence for uniformly convex functions. The numerical experiments show the efficiency of the proposed method in practical computations.
Keywords:
Unconstrained optimization,
self-scaling memoryless BFGS update,
modified secant equation,
accelerated scheme,
convergence analysis.
Mathematics Subject Classification:
Primary: 90C30, 90C06; Secondary: 65K05.
Citation:
Yigui Ou, Haichan Lin. A class of accelerated conjugate-gradient-like methods based on a modified secant equation. Journal of Industrial & Management Optimization,
2020, 16
(3)
: 1503-1518.
doi: 10.3934/jimo.2019013
![]()
References:
| [1] |
N. Andrei,
Scaled conjugate gradient algorithms for unconstrained optimization, Computational Optimization and Applications, 38 (2007), 401-416.
doi: 10.1007/s10589-007-9055-7. |
| [2] |
N. Andrei,
Acceleration of conjugate gradient algorithms for unconstrained optimization, Applied Mathematics and Computation, 213 (2009), 361-369.
doi: 10.1016/j.amc.2009.03.020. |
| [3] |
N. Andrei,
Accerated adaptive Perry conjugate gradient algorithms based on the self-scaling memoryless BFGS update, Journal of Computational and Applied Mathematics, 325 (2017), 149-164.
doi: 10.1016/j.cam.2017.04.045. |
| [4] |
I. Bongartz, A. R. Conn, N. I. M. Gould and P. L. Toint,
CUTE: constrained and unconstrained testing environments, ACM Transactions on Mathematical Software, 21 (1995), 123-160.
doi: 10.1145/200979.201043. |
| [5] |
Z. F. Dai,
Two modified HS type conjugate gradient methods for unconstrained optimization problems, Nonlinear Analysis: Theory Methods & Applications, 74 (2011), 927-936.
doi: 10.1016/j.na.2010.09.046. |
| [6] |
Z. F. Dai, X. H. Chen and F. H. Wen,
A modified Perry's conjugate gradient method-based derivative-free method for solving large-scale nonlinear monotone equations, Applied Mathematics and Computation, 270 (2015), 378-386.
doi: 10.1016/j.amc.2015.08.014. |
| [7] |
Z. F. Dai and F. H. Wen,
Global convergence of a modified Hestenes-Stiefel nonlinear conjugate gradient method with Armijo line search, Numerical Algorithms, 59 (2012), 79-93.
doi: 10.1007/s11075-011-9477-2. |
| [8] |
Y. H. Dai,
On the nonmonotone line search, Journal of Optimization Theory and Applications, 112 (2002), 315-330.
doi: 10.1023/A:1013653923062. |
| [9] |
Y. H. Dai and Y. X. Yuan, Nonlinear Conjugate Gradient Methods (in chinese), Shanghai Scientific and Technical Publishers, Shanghai, 2000. Google Scholar |
| [10] |
E. D. Dolan and J. J. Mor$\acute{e}$, Benchmarking optimization software with performance profiles, Mathematical Programming, Serial A, 91 (2002), 201-213.
doi: 10.1007/s101070100263. |
| [11] |
L. Grippo, F. Lampariello and S. Lucidi,
A nonmonotone line search technique for Newton's method, SIAM Journal on Numerical Analysis, 23 (1986), 707-716.
doi: 10.1137/0723046. |
| [12] |
N. Z. Gu and J. T. Mo,
Incorporating nonmonotone strategies into the trust region method for unconstrained optimization, Computers and Mathematics with Applications, 55 (2008), 2158-2172.
doi: 10.1016/j.camwa.2007.08.038. |
| [13] |
W. W. Hager and H. C. Zhang,
A new conjugate gradient method with guaranteed descent and an efficient line search, SIAM Journal on Optimization, 16 (2005), 170-192.
doi: 10.1137/030601880. |
| [14] |
W. W. Hager and H. C. Zhang,
A survey of nonlinear conjugate gradient methods, Pacific Journal of Optimization, 2 (2006), 35-58.
|
| [15] |
D. H. Li and M. Fukushima,
A modified BFGS method and its global convergence in nonconvex minimization, Journal of Computational and Applied Mathematics, 129 (2001), 15-35.
doi: 10.1016/S0377-0427(00)00540-9. |
| [16] |
I. E. Livieris and P. intelas,
Globally convergent modified Perry's conjugate gradient method, Applied Mathematics and Computation, 218 (2012), 9197-9207.
doi: 10.1016/j.amc.2012.02.076. |
| [17] |
J. Nocedal and S. J. Wright, Numerical Optimization, Springer, New York, 1999.
doi: 10.1007/b98874. |
| [18] |
Y. G. Ou,
A note on the global convergence theorem of accelerated adaptive Perry conjugate gradient methods, Journal of Computational and Applied Mathematics, 332 (2018), 101-106.
doi: 10.1016/j.cam.2017.10.024. |
| [19] |
D. F. Shanno and K. H. Phua,
Algorithm 500, minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software, 2 (1976), 87-94.
doi: 10.1145/355666.355673. |
| [20] |
W. Y. Sun and Y. X. Yuan, Optimization Theory and Methods: Nonlinear Programming, Springer, New York, 2006. |
| [21] |
S. W. Yao, D. L. He and L. H. Shi,
An improved Perry conjugate gradient method with adaptive parameter choice, Numerical Algorithms, 78 (2018), 1255-1269.
doi: 10.1007/s11075-017-0422-x. |
| [22] |
G. L. Yuan,
Modified nonlinear conjugate gradient methods with sufficient descent property for large-scale optimization problems, Optimization Letters, 3 (2009), 11-21.
doi: 10.1007/s11590-008-0086-5. |
| [23] |
G. L. Yuan, Z. H. Meng and Y. Li,
A modified Hestenes and Stiefel conjugate gradient algorithm for large scale nonsmooth minimizations and nonlinear equations, Journal of Optimization Theory and Applications, 168 (2016), 129-152.
doi: 10.1007/s10957-015-0781-1. |
| [24] |
J. Z. Zhang, N. Y. Deng and L. H. Chen,
New quasi-Newton equation and related methods for unconstrained optimization, Journal of Optimization Theory and Applications, 102 (1999), 147-167.
doi: 10.1023/A:1021898630001. |
| [25] |
H. C. Zhang and W. W. Hager,
A nonmonotone line search technique and its application to unconstrained optimization, SIAM Jpournal on Optimization, 14 (2004), 1043-1056.
doi: 10.1137/S1052623403428208. |
show all references
References:
| [1] |
N. Andrei,
Scaled conjugate gradient algorithms for unconstrained optimization, Computational Optimization and Applications, 38 (2007), 401-416.
doi: 10.1007/s10589-007-9055-7. |
| [2] |
N. Andrei,
Acceleration of conjugate gradient algorithms for unconstrained optimization, Applied Mathematics and Computation, 213 (2009), 361-369.
doi: 10.1016/j.amc.2009.03.020. |
| [3] |
N. Andrei,
Accerated adaptive Perry conjugate gradient algorithms based on the self-scaling memoryless BFGS update, Journal of Computational and Applied Mathematics, 325 (2017), 149-164.
doi: 10.1016/j.cam.2017.04.045. |
| [4] |
I. Bongartz, A. R. Conn, N. I. M. Gould and P. L. Toint,
CUTE: constrained and unconstrained testing environments, ACM Transactions on Mathematical Software, 21 (1995), 123-160.
doi: 10.1145/200979.201043. |
| [5] |
Z. F. Dai,
Two modified HS type conjugate gradient methods for unconstrained optimization problems, Nonlinear Analysis: Theory Methods & Applications, 74 (2011), 927-936.
doi: 10.1016/j.na.2010.09.046. |
| [6] |
Z. F. Dai, X. H. Chen and F. H. Wen,
A modified Perry's conjugate gradient method-based derivative-free method for solving large-scale nonlinear monotone equations, Applied Mathematics and Computation, 270 (2015), 378-386.
doi: 10.1016/j.amc.2015.08.014. |
| [7] |
Z. F. Dai and F. H. Wen,
Global convergence of a modified Hestenes-Stiefel nonlinear conjugate gradient method with Armijo line search, Numerical Algorithms, 59 (2012), 79-93.
doi: 10.1007/s11075-011-9477-2. |
| [8] |
Y. H. Dai,
On the nonmonotone line search, Journal of Optimization Theory and Applications, 112 (2002), 315-330.
doi: 10.1023/A:1013653923062. |
| [9] |
Y. H. Dai and Y. X. Yuan, Nonlinear Conjugate Gradient Methods (in chinese), Shanghai Scientific and Technical Publishers, Shanghai, 2000. Google Scholar |
| [10] |
E. D. Dolan and J. J. Mor$\acute{e}$, Benchmarking optimization software with performance profiles, Mathematical Programming, Serial A, 91 (2002), 201-213.
doi: 10.1007/s101070100263. |
| [11] |
L. Grippo, F. Lampariello and S. Lucidi,
A nonmonotone line search technique for Newton's method, SIAM Journal on Numerical Analysis, 23 (1986), 707-716.
doi: 10.1137/0723046. |
| [12] |
N. Z. Gu and J. T. Mo,
Incorporating nonmonotone strategies into the trust region method for unconstrained optimization, Computers and Mathematics with Applications, 55 (2008), 2158-2172.
doi: 10.1016/j.camwa.2007.08.038. |
| [13] |
W. W. Hager and H. C. Zhang,
A new conjugate gradient method with guaranteed descent and an efficient line search, SIAM Journal on Optimization, 16 (2005), 170-192.
doi: 10.1137/030601880. |
| [14] |
W. W. Hager and H. C. Zhang,
A survey of nonlinear conjugate gradient methods, Pacific Journal of Optimization, 2 (2006), 35-58.
|
| [15] |
D. H. Li and M. Fukushima,
A modified BFGS method and its global convergence in nonconvex minimization, Journal of Computational and Applied Mathematics, 129 (2001), 15-35.
doi: 10.1016/S0377-0427(00)00540-9. |
| [16] |
I. E. Livieris and P. intelas,
Globally convergent modified Perry's conjugate gradient method, Applied Mathematics and Computation, 218 (2012), 9197-9207.
doi: 10.1016/j.amc.2012.02.076. |
| [17] |
J. Nocedal and S. J. Wright, Numerical Optimization, Springer, New York, 1999.
doi: 10.1007/b98874. |
| [18] |
Y. G. Ou,
A note on the global convergence theorem of accelerated adaptive Perry conjugate gradient methods, Journal of Computational and Applied Mathematics, 332 (2018), 101-106.
doi: 10.1016/j.cam.2017.10.024. |
| [19] |
D. F. Shanno and K. H. Phua,
Algorithm 500, minimization of unconstrained multivariate functions, ACM Transactions on Mathematical Software, 2 (1976), 87-94.
doi: 10.1145/355666.355673. |
| [20] |
W. Y. Sun and Y. X. Yuan, Optimization Theory and Methods: Nonlinear Programming, Springer, New York, 2006. |
| [21] |
S. W. Yao, D. L. He and L. H. Shi,
An improved Perry conjugate gradient method with adaptive parameter choice, Numerical Algorithms, 78 (2018), 1255-1269.
doi: 10.1007/s11075-017-0422-x. |
| [22] |
G. L. Yuan,
Modified nonlinear conjugate gradient methods with sufficient descent property for large-scale optimization problems, Optimization Letters, 3 (2009), 11-21.
doi: 10.1007/s11590-008-0086-5. |
| [23] |
G. L. Yuan, Z. H. Meng and Y. Li,
A modified Hestenes and Stiefel conjugate gradient algorithm for large scale nonsmooth minimizations and nonlinear equations, Journal of Optimization Theory and Applications, 168 (2016), 129-152.
doi: 10.1007/s10957-015-0781-1. |
| [24] |
J. Z. Zhang, N. Y. Deng and L. H. Chen,
New quasi-Newton equation and related methods for unconstrained optimization, Journal of Optimization Theory and Applications, 102 (1999), 147-167.
doi: 10.1023/A:1021898630001. |
| [25] |
H. C. Zhang and W. W. Hager,
A nonmonotone line search technique and its application to unconstrained optimization, SIAM Jpournal on Optimization, 14 (2004), 1043-1056.
doi: 10.1137/S1052623403428208. |
Figure 2.
Performance profiles based on the CPU time (left) and the number $ N_f $ of function evaluations (right)
Figure 3.
Performance profiles based on the CPU time (left) and the number $ N_f $ of function evaluations (right)
Table 1.
List of the test problems
| No. | Description of function | Dim. | No. | Description of function | Dim. |
| 1 | Ex. Freud. Roth | 10000 | 35 | Ex. Tri. Diag 2 | 10000 |
| 2 | Ex. Trigonometric | 10000 | 36 | FLETCBV3(CUTE) | 1000 |
| 3 | Ex. Rosenbrock | 10000 | 37 | FLETCHCR(CUTE) | 5000 |
| 4 | Ex. White and Holst | 10000 | 38 | BDQRTIC(CUTE) | 10000 |
| 5 | Ex. Beale | 10000 | 39 | TRIDIA(CUTE) | 10000 |
| 6 | Ex. Penalty | 10000 | 40 | ARGLINB(CUTE) | 10000 |
| 7 | Perturbed Quad. | 10000 | 41 | ARWHEAD(CUTE) | 10000 |
| 8 | Raydan 1 | 10000 | 42 | NONDIA(CUTE) | 10000 |
| 9 | Raydan 2 | 10000 | 43 | NONDQUAR(CUTE) | 10000 |
| 10 | Diagonal 1 | 10000 | 44 | DQDRTIC(CUTE) | 10000 |
| 11 | Diagonal 2 | 10000 | 45 | EG2(CUTE) | 10000 |
| 12 | Diagonal 3 | 10000 | 46 | CURLY20(CUTE) | 1000 |
| 13 | Hager Function | 10000 | 47 | DIXMAANA(CUTE) | 9000 |
| 14 | Generalized Tri. Diag. 1 | 10000 | 48 | DIXMAANB(CUTE) | 9000 |
| 15 | Ex. Tri. Diag. 1 | 10000 | 49 | DIXMAANC(CUTE) | 9000 |
| 16 | Ex. Three Exp. | 10000 | 50 | DIXMAAND(CUTE) | 9000 |
| 17 | Generalized Tri. Diag. 2 | 10000 | 51 | DIXMAANE(CUTE) | 9000 |
| 18 | Diagonal 4 | 10000 | 52 | DIXMAANF(CUTE) | 9000 |
| 19 | Diagonal 5 | 10000 | 53 | DIXMAANG(CUTE) | 9000 |
| 20 | Ex. Himmelblau | 10000 | 54 | DIXMAANH(CUTE) | 9000 |
| 21 | Generalized PSC1 | 10000 | 55 | DIXMAANI(CUTE) | 9000 |
| 22 | Ex. PSC1 | 10000 | 56 | DIXMAANJ(CUTE) | 9000 |
| 23 | Ex. Powell | 10000 | 57 | DIXMAANK(CUTE) | 9000 |
| 24 | Ex. Block-Diag. BD1 | 10000 | 58 | DIXMAANL(CUTE) | 9000 |
| 25 | Ex. Maratos | 10000 | 59 | LIARWHD(CUTE) | 10000 |
| 26 | Ex. Cliff | 10000 | 60 | POWER(CUTE) | 5000 |
| 27 | Quad. Dia. Perturbed | 10000 | 61 | ENGVAL1(CUTE) | 10000 |
| 28 | Ex. wood | 10000 | 62 | EDENSCH(CUTE) | 10000 |
| 29 | Ex. Hiebert | 10000 | 63 | VARDIM(CUTE) | 10000 |
| 30 | Quadratic QF1 | 10000 | 64 | QUARTC(CUTE) | 10000 |
| 31 | Ex. Quad. Pena. QP1 | 10000 | 65 | SINQUAD(CUTE) | 5000 |
| 32 | Ex. Quad. Pena. QP2 | 5000 | 66 | DENSCHNB(CUTE) | 10000 |
| 33 | Quadratic QF2 | 10000 | 67 | DENSCHNF(CUTE) | 10000 |
| 34 | Ex. EP1 | 10000 | 68 | COSINE(CUTE) | 10000 |
| No. | Description of function | Dim. | No. | Description of function | Dim. |
| 1 | Ex. Freud. Roth | 10000 | 35 | Ex. Tri. Diag 2 | 10000 |
| 2 | Ex. Trigonometric | 10000 | 36 | FLETCBV3(CUTE) | 1000 |
| 3 | Ex. Rosenbrock | 10000 | 37 | FLETCHCR(CUTE) | 5000 |
| 4 | Ex. White and Holst | 10000 | 38 | BDQRTIC(CUTE) | 10000 |
| 5 | Ex. Beale | 10000 | 39 | TRIDIA(CUTE) | 10000 |
| 6 | Ex. Penalty | 10000 | 40 | ARGLINB(CUTE) | 10000 |
| 7 | Perturbed Quad. | 10000 | 41 | ARWHEAD(CUTE) | 10000 |
| 8 | Raydan 1 | 10000 | 42 | NONDIA(CUTE) | 10000 |
| 9 | Raydan 2 | 10000 | 43 | NONDQUAR(CUTE) | 10000 |
| 10 | Diagonal 1 | 10000 | 44 | DQDRTIC(CUTE) | 10000 |
| 11 | Diagonal 2 | 10000 | 45 | EG2(CUTE) | 10000 |
| 12 | Diagonal 3 | 10000 | 46 | CURLY20(CUTE) | 1000 |
| 13 | Hager Function | 10000 | 47 | DIXMAANA(CUTE) | 9000 |
| 14 | Generalized Tri. Diag. 1 | 10000 | 48 | DIXMAANB(CUTE) | 9000 |
| 15 | Ex. Tri. Diag. 1 | 10000 | 49 | DIXMAANC(CUTE) | 9000 |
| 16 | Ex. Three Exp. | 10000 | 50 | DIXMAAND(CUTE) | 9000 |
| 17 | Generalized Tri. Diag. 2 | 10000 | 51 | DIXMAANE(CUTE) | 9000 |
| 18 | Diagonal 4 | 10000 | 52 | DIXMAANF(CUTE) | 9000 |
| 19 | Diagonal 5 | 10000 | 53 | DIXMAANG(CUTE) | 9000 |
| 20 | Ex. Himmelblau | 10000 | 54 | DIXMAANH(CUTE) | 9000 |
| 21 | Generalized PSC1 | 10000 | 55 | DIXMAANI(CUTE) | 9000 |
| 22 | Ex. PSC1 | 10000 | 56 | DIXMAANJ(CUTE) | 9000 |
| 23 | Ex. Powell | 10000 | 57 | DIXMAANK(CUTE) | 9000 |
| 24 | Ex. Block-Diag. BD1 | 10000 | 58 | DIXMAANL(CUTE) | 9000 |
| 25 | Ex. Maratos | 10000 | 59 | LIARWHD(CUTE) | 10000 |
| 26 | Ex. Cliff | 10000 | 60 | POWER(CUTE) | 5000 |
| 27 | Quad. Dia. Perturbed | 10000 | 61 | ENGVAL1(CUTE) | 10000 |
| 28 | Ex. wood | 10000 | 62 | EDENSCH(CUTE) | 10000 |
| 29 | Ex. Hiebert | 10000 | 63 | VARDIM(CUTE) | 10000 |
| 30 | Quadratic QF1 | 10000 | 64 | QUARTC(CUTE) | 10000 |
| 31 | Ex. Quad. Pena. QP1 | 10000 | 65 | SINQUAD(CUTE) | 5000 |
| 32 | Ex. Quad. Pena. QP2 | 5000 | 66 | DENSCHNB(CUTE) | 10000 |
| 33 | Quadratic QF2 | 10000 | 67 | DENSCHNF(CUTE) | 10000 |
| 34 | Ex. EP1 | 10000 | 68 | COSINE(CUTE) | 10000 |
| [1] |
Hong Seng Sim, Wah June Leong, Chuei Yee Chen, Siti Nur Iqmal Ibrahim. Multi-step spectral gradient methods with modified weak secant relation for large scale unconstrained optimization. Numerical Algebra, Control & Optimization, 2018, 8 (3) : 377-387. doi: 10.3934/naco.2018024 |
| [2] |
Fernando P. da Costa, João T. Pinto, Rafael Sasportes. On the convergence to critical scaling profiles in submonolayer deposition models. Kinetic & Related Models, 2018, 11 (6) : 1359-1376. doi: 10.3934/krm.2018053 |
| [3] |
Caifang Wang, Tie Zhou. The order of convergence for Landweber Scheme with $\alpha,\beta$-rule. Inverse Problems & Imaging, 2012, 6 (1) : 133-146. doi: 10.3934/ipi.2012.6.133 |
| [4] |
Min Li. A three term Polak-Ribière-Polyak conjugate gradient method close to the memoryless BFGS quasi-Newton method. Journal of Industrial & Management Optimization, 2020, 16 (1) : 245-260. doi: 10.3934/jimo.2018149 |
| [5] |
Jan Prüss, Laurent Pujo-Menjouet, G.F. Webb, Rico Zacher. Analysis of a model for the dynamics of prions. Discrete & Continuous Dynamical Systems - B, 2006, 6 (1) : 225-235. doi: 10.3934/dcdsb.2006.6.225 |
| [6] |
Thomas Y. Hou, Ruo Li. Nonexistence of locally self-similar blow-up for the 3D incompressible Navier-Stokes equations. Discrete & Continuous Dynamical Systems - A, 2007, 18 (4) : 637-642. doi: 10.3934/dcds.2007.18.637 |
| [7] |
Ardeshir Ahmadi, Hamed Davari-Ardakani. A multistage stochastic programming framework for cardinality constrained portfolio optimization. Numerical Algebra, Control & Optimization, 2017, 7 (3) : 359-377. doi: 10.3934/naco.2017023 |
| [8] |
Luke Finlay, Vladimir Gaitsgory, Ivan Lebedev. Linear programming solutions of periodic optimization problems: approximation of the optimal control. Journal of Industrial & Management Optimization, 2007, 3 (2) : 399-413. doi: 10.3934/jimo.2007.3.399 |
| [9] |
J. Frédéric Bonnans, Justina Gianatti, Francisco J. Silva. On the convergence of the Sakawa-Shindo algorithm in stochastic control. Mathematical Control & Related Fields, 2016, 6 (3) : 391-406. doi: 10.3934/mcrf.2016008 |
| [10] |
Alberto Bressan, Carlotta Donadello. On the convergence of viscous approximations after shock interactions. Discrete & Continuous Dynamical Systems - A, 2009, 23 (1&2) : 29-48. doi: 10.3934/dcds.2009.23.29 |
| [11] |
Qiang Guo, Dong Liang. An adaptive wavelet method and its analysis for parabolic equations. Numerical Algebra, Control & Optimization, 2013, 3 (2) : 327-345. doi: 10.3934/naco.2013.3.327 |
| [12] |
Wen-Bin Yang, Yan-Ling Li, Jianhua Wu, Hai-Xia Li. Dynamics of a food chain model with ratio-dependent and modified Leslie-Gower functional responses. Discrete & Continuous Dynamical Systems - B, 2015, 20 (7) : 2269-2290. doi: 10.3934/dcdsb.2015.20.2269 |
| [13] |
Vladimir Georgiev, Sandra Lucente. Focusing nlkg equation with singular potential. Communications on Pure & Applied Analysis, 2018, 17 (4) : 1387-1406. doi: 10.3934/cpaa.2018068 |
| [14] |
Daoyin He, Ingo Witt, Huicheng Yin. On the strauss index of semilinear tricomi equation. Communications on Pure & Applied Analysis, 2020, 19 (10) : 4817-4838. doi: 10.3934/cpaa.2020213 |
| [15] |
Wolf-Jüergen Beyn, Janosch Rieger. The implicit Euler scheme for one-sided Lipschitz differential inclusions. Discrete & Continuous Dynamical Systems - B, 2010, 14 (2) : 409-428. doi: 10.3934/dcdsb.2010.14.409 |
| [16] |
Alina Chertock, Alexander Kurganov, Mária Lukáčová-Medvi${\rm{\check{d}}}$ová, Șeyma Nur Özcan. An asymptotic preserving scheme for kinetic chemotaxis models in two space dimensions. Kinetic & Related Models, 2019, 12 (1) : 195-216. doi: 10.3934/krm.2019009 |
| [17] |
Martial Agueh, Reinhard Illner, Ashlin Richardson. Analysis and simulations of a refined flocking and swarming model of Cucker-Smale type. Kinetic & Related Models, 2011, 4 (1) : 1-16. doi: 10.3934/krm.2011.4.1 |
| [18] |
Rui Hu, Yuan Yuan. Stability, bifurcation analysis in a neural network model with delay and diffusion. Conference Publications, 2009, 2009 (Special) : 367-376. doi: 10.3934/proc.2009.2009.367 |
| [19] |
Seung-Yeal Ha, Shi Jin. Local sensitivity analysis for the Cucker-Smale model with random inputs. Kinetic & Related Models, 2018, 11 (4) : 859-889. doi: 10.3934/krm.2018034 |
| [20] |
Diana Keller. Optimal control of a linear stochastic Schrödinger equation. Conference Publications, 2013, 2013 (special) : 437-446. doi: 10.3934/proc.2013.2013.437 |
2019 Impact Factor: 1.366
Tools
Metrics
Other articles
by authors
[Back to Top]