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Frontiers of Mathematics in China

ISSN 1673-3452

ISSN 1673-3576(Online)

CN 11-5739/O1

Postal Subscription Code 80-964

2018 Impact Factor: 0.565

Front. Math. China    2023, Vol. 18 Issue (4) : 287-299    https://doi.org/10.3868/s140-DDD-023-0018-x
RESEARCH ARTICLE
Three-term derivative-free projection method for solving nonlinear monotone equations
Jinkui LIU(), Xianglin DU
School of Mathematics and Statistics, Chongqing Three Gorges University, Chongqing 404000, China
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Abstract

In this paper, a three-term derivative-free projection method is proposed for solving nonlinear monotone equations. Under some appropriate conditions, the global convergence and R-linear convergence rate of the proposed method are analyzed and proved. With no need of any derivative information, the proposed method is able to solve large-scale nonlinear monotone equations. Numerical comparisons show that the proposed method is effective.

Keywords Nonlinear monotone equations      conjugate gradient method      derivative-free projection method      global convergence      R-linear convergence rate     
Corresponding Author(s): Jinkui LIU   
Online First Date: 07 December 2023    Issue Date: 12 December 2023
 Cite this article:   
Jinkui LIU,Xianglin DU. Three-term derivative-free projection method for solving nonlinear monotone equations[J]. Front. Math. China, 2023, 18(4): 287-299.
 URL:  
https://academic.hep.com.cn/fmc/EN/10.3868/s140-DDD-023-0018-x
https://academic.hep.com.cn/fmc/EN/Y2023/V18/I4/287
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.1500032960.06391130.08311100.03
700032960.09391130.08311100.05
900032960.09401160.09321130.06
1100033990.09401160.13321130.08
1300033990.1433920.16321130.08
1500033990.1333920.14321130.11
Problem 5.25000411150.0637960.05401320.03
7000431210.08421100.09491590.07
9000391050.0838970.08471590.08
11000391050.638970.09521730.09
13000391050.09391010.08612040.11
15000401080.1391010.11471560.11
Problem 5.3500010230.0626540.0620420.03
700011260.0526550.0920430.04
900011260.0827570.1121460.06
1100012300.0627570.1321470.07
1300012300.0827580.1421480.08
1500013340.0927580.1422500.10
Tab.1  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (?1,0)
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.1500030920.05371090.06291020.05
700030920.08381120.09291020.06
900030920.09381120.09291020.05
1100031950.0833960.11291020.09
1300031950.1133960.14291020.09
1500031950.1133960.14301060.09
Problem 5.2500034830.09461180.08511700.05
7000391010.05421070.08461500.07
9000391010.06451170.08622090.10
11000391000.08441140.08491600.08
13000401030.11441130.13451490.10
15000411090.09411090.11421390.10
Problem 5.350009190.0524490.0819390.03
70009190.0525510.1119400.07
90009190.0825520.1119400.04
1100010230.0526540.1320420.05
1300011260.0826540.1420420.06
1500011260.1126550.1720430.09
Tab.2  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (?2, 1)
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.1500031970.06391170.07301090.07
7000321000.08391170.13331210.08
9000331030.09401200.11311130.07
11000331030.10401200.12311130.08
13000331030.10401200.14321160.11
15000331030.13411230.18321160.10
Problem 5.2500039980.0638970.07511760.05
700038960.06471250.07441450.05
900038960.07431100.06491660.07
1100039980.09431100.13501700.09
1300039980.11431100.11571910.12
1500039980.11441130.14521690.11
Problem 5.350009190.0425510.1119400.04
700010230.0526540.1320420.05
900011260.0626540.1120430.05
1100011260.0626550.1621460.07
1300012300.0927570.1721460.08
1500012300.0927570.1621470.09
Tab.3  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (?1, 1)
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.1500032970.09401150.06562350.08
7000381160.0934950.11562350.11
9000381160.0934950.08411570.09
11000381160.1334950.09421610.12
13000381160.1335990.11391470.14
15000381160.1635990.14391470.14
Problem 5.25000431180.06431110.06501700.05
7000431170.08441130.09491650.06
9000411110.09421090.08471580.07
11000431170.09451170.12511720.09
13000431170.11441150.09471560.09
15000431170.14491290.12431410.10
Problem 5.3500011260.0526540.0620430.04
700012300.0527570.1121460.05
900012300.1327580.0921480.06
1100013340.0827580.1122500.08
1300013340.0828620.1422510.09
1500014390.0929650.1722520.10
Tab.4  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (?2, 1)
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.15000341060.0832940.06331210.03
7000341060.0632940.09341250.07
9000341060.1132940.09341250.08
11000351090.0833970.12341250.09
13000351090.1333950.13341250.11
15000351090.1333950.17341250.12
Problem 5.25000421150.06441160.05471590.03
7000421140.08471240.06471590.06
9000421150.09431100.08531820.08
11000431160.09431120.11481630.08
13000431160.17441150.12481640.10
15000441200.12441150.14602090.13
Problem 5.350009190.0624490.0618370.04
70009190.0624490.0819390.03
90009190.0825510.1119400.05
110009190.0625510.1719400.06
130009190.0625520.1219400.07
1500010230.0825520.1619400.08
Tab.5  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (0, 2)
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.15000371180.08391180.09431720.07
7000411350.14391180.11451810.09
9000391300.13391200.11461880.12
11000441470.14411270.13451840.14
13000421440.16411270.19451840.16
15000441510.17421320.17451860.18
Problem 5.25000421080.06471190.06531840.05
7000441160.08471230.09592060.07
9000441160.06481260.09461560.06
11000461250.11471240.09592060.10
13000461250.09471240.11642260.13
15000481350.13511370.16602160.14
Problem 5.3500011260.1326550.1320440.04
700012300.0627570.1121460.07
900012300.0627580.1322500.06
1100013340.1128620.1422500.08
1300014390.0928620.1622520.09
1500014390.1129660.1923560.11
Tab.6  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (?5, 5)
DimAlgorithm 2.1PRP-based methodHS-based method
NINFCPUNINFCPUNINFCPU
Problem 5.15000471820.09471670.13803860.13
7000471830.13491770.1620713490.55
9000491970.17491830.1621113750.71
11000502020.19511890.1719312050.76
13000532210.23521930.2317310640.78
15000512140.27522000.27894630.45
Problem 5.25000491380.09571550.08692550.06
7000511460.09511390.09622290.08
9000511490.09571560.14833030.11
11000501460.11611710.17893230.16
13000501470.14571620.14662460.13
15000511510.12581680.16813030.19
Problem 5.3500013340.0528620.1122510.04
700014400.0930700.0923570.07
900016480.0631730.1224620.08
1100017540.0831740.1625660.10
1300018580.1432780.1926700.12
1500018600.1633810.2027760.14
Tab.7  Calculation results of Algorithm 2.1, MPRP-based method and MHS-based method, with initial points in the interval (?10, 10)
1 M Ahookhosh, K Amini, S Bahrami. Two derivative-free projection approaches for systems of largescale nonlinear monotone equations. Numer Algorithms 2013; 64(1): 21–42
2 N Andrei. On three-term conjugate gradient algorithms for unconstrained optimization. Appl Math Comput 2013; 219: 6316–6317
3 W Y Cheng. A PRP type method for systems of monotone equations. Math Comput Modelling 2009; 50: 15–20
4 J E Dennis, J J More. A characterization of superlinear convergence and its application to quasi-Newton methods. Math Comp 1974; 28(126): 549–560
5 J E Dennis, J J More. Quasi-Newton method, motivation and theory. SIAM Rev 1997; 19(1): 46–89
6 S P Dirkse, M C Ferris. MCPLIB: A collection of nonlinear mixed complementarity problems. Optim Methods Softw 2002; 5(4): 319–345
7 W W Hager, H Zhang. A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J Optim 2005; 16(1): 170–192
8 M R Hestens, E L Stiefel. Methods of conjugate gradients for solving linear systems. J Research Nat Bur Standards 1952; 49(6): 409–436
9 A N Iusem, M V Solodov. Newton-type methods with generalized distances for constrained optimization. Optimization 1997; 41(3): 257–278
10 M La Cruz, J M Martinez, M Raydan. Spectral residual method without gradient information for solving large-scale nonlinear systems of equations. Math Comp 2006; 75(255): 1429–1448
11 D H Li, M Fukushima. A global and superlinear convergent Gauss-Newton-based BFGS method for symmetric nonlinear equations. SIAM J Numer Anal 1999; 37(1): 152–172
12 Q N Li, D H Li. A class of derivative-free methods for large-scale nonlinear monotone equations. IMA J Numer Anal 2011; 31(4): 1625–1635
13 J K Liu, S J Li. A projection method for convex constrained monotone nonlinear equations with applications. Comput Math Appl 2015; 70(10): 2442–2453
14 K Meintjes, A P Morgan. A methodology for solving chemical equilibrium systems. Appl Math Comput 1987; 22(4): 333–361
15 B T Polyak. The conjugate gradient method in extremal problems. USSR Comput Math and Math Phys 1969; 9(4): 94–112
16 E Polak, G Ribière. Note sur la convergence de méthodes de directions conjuguées. Rev Francaise Informat Recherche Opérationnelle 1969; 3(16): 35–43
17 M V SolodovB F Svaiter. A globally convergent inexact Newton method for systems of monotone equations. In: Reformulation: Non-smooth, Piecewise Smooth, Semismooth and Smoothing Methods. Dordrecht: Springer, 1999, 355–369
18 X Y Wu, N E Z Sai, H L Zhang. On three-term HS projection algorithm for solving nonlinear monotone equations. Southwest China Normal Univ Natur Sci Ed 2016; 41(5): 41–47
19 Y H Xiao, H Zhu. A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. J Math Anal Appl 2013; 405(1): 310–319
20 Y B Zhao, D H Li. Monotonicity of fixed point and normal mapping associated with variational inequality and its application. SIAM J Optim 2000; 11(4): 962–973
21 G Zhou, K C Toh. Superline convergence of a Newton-type algorithm for monotone equations. J Optim Theory Appl 2005; 125(1): 205–221
22 W J Zhou, D H Li. Limited memory BFGS method for nonlinear monotone equations. J Comput Math 2007; 25(1): 89–96
23 W J Zhou, D H Li. A globally convergent BFGS method for nonlinear monotone equations without any merit functions. Math Comp 2008; 77(264): 2231–2240
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