Abstract
As it is known that, not all conjugate gradient (CG) methods satisfy descent property, a necessary condition for attaining global convergence result. In this article, we propose three different sufficient-descent conjugate gradient projection algorithms for constrained monotone equations. Using Dai–Yuan (DY) conjugate gradient parameter, we generate three satisfied sufficient-descent directions. Under suitable conditions, global convergence of the algorithms is established. Numerical examples using benchmark test functions indicate that the algorithms are effective for solving constrained monotone nonlinear equations. Moreover, we also extend the method to solve \(\ell _1\)-norm regularized problems to decode a sparse signal in compressive sensing. Performance comparisons show that the proposed methods are practical, efficient and competitive with the compared methods.
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References
Abdullahi H, Halilu AS, Waziri MY (2018) A modified conjugate gradient method via a double direction approach for solving large-scale symmetric nonlinear systems. J Numer Math Stoch 10(1):32–44
Abubakar AB, Kumam P (2018) A descent Dai–Liao conjugate gradient method for nonlinear equations. Numer Algorithm
Abubakar AB, Kumam P, Auwal AM (2018) A descent Dai–Liao projection method for convex constrained nonlinear monotone equations with applications. Thai J Math 17:128–152
Abubakar AB, Kumam P, Auwal AM (2019a) A modified conjugate gradient method for monotone nonlinear equations with convex constraints. Results Appl Math 4:100069
Abubakar AB, Kumam P, Auwal AM (2019b) Global convergence via descent modified three-term conjugate gradient algorithm with applications to signal recovery. Results Appl Math 4:100069
Amini K, Rostami F (2015) A modified two steps Levenberg–Marquardt method for nonlinear equations. J Comput Appl Math 288:341–350
Dai YH (2011) Nonlinear conjugate gradient methods. In: Cochran JJ (ed) Wiley Encyclopedia of Operations Research and Management Science. Wiley, New York
Dai Z, Zhu H (2020) A modified Hestenes–Stiefel-type derivative-free method for large-scale nonlinear monotone equations. Mathematics 8:168. https://doi.org/10.3390/math8020168
Dauda MK, Mamat M, Mohamad MF, Magaji AS, Waziri MY (2019) Derivative free conjugate gradient method via Broyden’s update for solving symmetric systems of nonlinear equations. J Phys Conf Ser
Dolan ED, Moré JJ (2002) Benchmarking optimization software with performance profiles. Math Program Ser A91:201–213
Elaine T, Wotao Y, Yin Z (2007) A fixed-point continuation method for \(\ell _1\)-regularized minimization with applications to compressed sensing. CAAM TR07-07, Rice University, pp 43–44
Ferris MJ, Dirkse SP (1995) A collection of nonlinear mixed complementarity problems. Optim Methods Softw 5:319–45
Figueiredo M, Nowak R, Wright SJ (2007) Gradient projection for sparse reconstruction, application to compressed sensing and other inverse problems. IEEE J-STSP IEEE Press, Piscataway, pp 586–597
Hager WW, Zhang H (2005) A new conjugate gradient method with guaranteed descent and an efficient line search. SIAM J Optim 16:170–192
Halilu AS, Waziri MY (2018) An improved derivative-free method via double direction approach for solving systems of nonlinear equations. J Ramanujan Math Soc 33:75–89
Halilu AS, Dauda MK, Waziri MY, Mamat M (2019) A derivative-free decent method via acceleration parameter for solving systems of nonlinear equations. Open J Sci Technol 2(3):1–4
Halilu AS, Majumder A, Waziri MY, Abdullahi H (2020a) Double direction and step length method for solving system of nonlinear equations. Eur J Mol Clin Med 7(7):3899–3913
Halilu AS, Waziri MY, Yusuf I (2020b) Efficient matrix-free direction method with line search for solving large-scale system of nonlinear equations. Yugoslav J Oper Res 30(4):399–412
Halilu AS, Majumder A, Waziri MY, Ahmed K (2021a) Signal recovery with convex constrained nonlinear monotone equations through conjugate gradient hybrid approach. Math Comput Simul. https://doi.org/10.1016/j.matcom.2021.03.020
Halilu AS, Majumder A, Waziri MY, Ahmed K (2021b) On solving double direction methods for convex constrained monotone nonlinear equations with image restoration. Comput Appl Math. https://doi.org/10.1007/s40314-021-01624-1
Li D, Fukushima M (1998) A globally and superlinearly convergent Gauss–Newton based BFGS method for symmetric equations. SIAM J Numer Anal 37:152–172
Li Q, Li DH (2011) A class of derivative-free methods for large-scale nonlinear monotone equations. IMA J Numer Anal 31:1625–1635
Li Z, Weijun Z, Li D (2006) A descent modified Polak-Ribi\(\acute{e}re\) Polyak conjugate gradient method and its global convergence. IMA J Numer Anal 26(4):629–40
Liao D, Fukushima M (1999) Global and superlinear convergent Gauss–Newton based BFGS method for symmetric nonlinear equation. SIAM J Numer Anal 37:152–172
Liu JK (2016) Derivative-free spectral PRP projection method for solving nonlinear monotone equations with convex constraints. Math Numer Sin 38:113–24
Liu JK, Li SJ (2015) A projection method for convex constrained monotone nonlinear equations with applications. Comput Math Appl x:x
Liu J, Li S (2017) Multivariate spectral Dy-type projection method for convex constrained nonlinear monotone equations. J Ind Manag Optim 13(1):283–295
Liu S, Huang Y, Jiao H (2014) sufficient descent conjugate gradient methods for solving convex constrained nonlinear monotone equations. Hindawi Publ Corp Abstr Appl Anal 2014:305643
Mario AT, Figueiredo R, Nowak D (2003) An EM algorithm for wavelet-based image restoration. IEEE Trans Image Process 12(8):906–916
Masoud A, Keyvan A, Somayeh B (2013) Two derivative-free projection approaches for systems of large-scale nonlinear monotone equations. Numer Algorithms. https://doi.org/10.1007/s11075-012-9653-z
Mohammad H, Abubakar AB (2020) A descent derivative-free algorithm for nonlinear monotone equations with convex constraints. RAIRO Oper Res 54:489–505
Musa YB, Waziri MY, Halilu AS (2017) On computing the regularization parameter for the Levenberg–Marquardt method via the spectral radius approach to solving systems of nonlinear equations. J Numer Math Stoch 9(1):80–94
Narushima Y, Yabe H, Ford J (2011) A three-term conjugate gradient method with sufficient descent property for unconstrained optimization. SIAM J Optim 21(1):212–30
Orovic I, Papic V, Ioana C, Li X, Stankovic S (2016) Compressive sensing in signal processing: algorithms and transform domain formulations. Hindawi Publ Corp Math Probl Eng 2016:7616393. https://doi.org/10.1155/2016/7616393
Pang JS (1986) Inexact Newton methods for the nonlinear complementarity problem. Math Program 1:54–71
Sabiu J, Shah A, Waziri MY (2020) Two optimal Hager–Zhang conjugate gradient methods for solving monotone nonlinear equations. Appl Numer Math
Schnabel RB, Frank PD (1984) Tensor methods for nonlinear equations. Soc Ind Appl Math 21(5)
Solodov MV, Svaiter BF (1998) A globally convergent inexact Newton method for systems of monotone equations. In: Fukushima M, Qi L (eds) Reformulation: nonsmooth, piecewise smooth, semismooth and smoothing methods. Kluwer Academic Publishers, Dordrecht, pp 355–369
Torabi M, Hosseini M (2018) A new descent algorithm using the three-step discretization method for solving unconstrained optimization problems. Mathematics
Waziri MY, Leong WJ, Hassan MA (2011) Jacobian-free diagonal newtons method for solving nonlinear systems with singular Jacobian. Malays J Math Sci 5:241–255
Waziri MY, Ahmad K, Halilu AS (2020a) Enhanced Dai-Liao conjugate gradient methods for systems of monotone nonlinear equations. SeMA J. https://doi.org/10.1007/s40324-020-00228-9
Waziri MY, Ahmed K, Sabiu J (2020b) Descent Perry conjugate gradient methods for systems of monotone nonlinear equations. Numer Algorithms
Waziri MY, Kufena YM, Halilu AS (2020c) Derivative-free three-term spectral conjugate gradient method for symmetric nonlinear equations. Thai J Math 18(3):1417–1431
Waziri M, Muhammad HU, Halilu AS, Ahmed K (2020d) Modified matrix-free methods for solving system of nonlinear equations. Optimization 70:2321–2340
Xiao Y, Zhu H (2013a) A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. J Math Anal Appl 405:310–319
Xiao Y, Zhu H (2013b) A conjugate gradient method to solve convex constrained monotone equations with applications in compressive sensing. J Math Anal Appl 405(1):310–319
Xiao Y, Wang Q, Hu Q (2011a) Non-smooth equations based method for \(l1-norm\) problems with applications to compressed sensing. Nonlinear Anal Theory Methods Appl 74(11):3570–3577
Xiao Y, Wang Q, Hu Q (2011b) Non-smooth equations based method for \(\ell _1\) problems with applications to compressed sensing. Nonlinear Anal Theory Methods Appl 74(11):3570–3577
Yana Q, Penga X, Li D (2010) A globally convergent derivative-free method for solving large-scale nonlinear monotone equations. J Comput Appl Math 234:649–657
Yu GH, Niu SZ, Ma JH (2013) Multivariate spectral gradient projection method for non-linear monotone equations with convex constraints. J Ind Manag Optim 9:117–129
Yuan YX (2009) Subspace methods for large scale nonlinear equations and nonlinear least squares. State Key Laboratory of Scientific and Engineering Computing
Yuan N (2017) A derivative-free projection method for solving convex constrained monotone equations. Sci Asia 43:195–200
Zhang JL, Wang W (2003) A new trust region method for nonlinear equations. Math Methods Oper Res 58:283–298
Zhao YB, Li D (2001) Monotonicity of fixed point and normal mappings associated with variational inequality and its application. SIAM J Optim 11:962–973
Zoltan P, Sanja R (2015) FR type methods for systems of large-scale nonlinear monotone equations. Appl Math Comput 269:816–823. https://doi.org/10.1016/j.camwa.2015.09.014
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Communicated by Gabriel Haeser.
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Abdullahi, H., Awasthi, A.K., Waziri, M.Y. et al. Descent three-term DY-type conjugate gradient methods for constrained monotone equations with application. Comp. Appl. Math. 41, 32 (2022). https://doi.org/10.1007/s40314-021-01724-y
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DOI: https://doi.org/10.1007/s40314-021-01724-y
Keywords
- Constrained-monotone nonlinear equations
- Descent conjugate gradient
- Projection method
- Compressive sensing