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Andrei, \u201cA Dai-Yuan conjugate gradient algorithm with sufficient descent and conjugacy conditions for unconstraind optimization,\u201d Applied Mathematics Letters, vol.21, no.2, pp.165-171, 2008. 10.1016\/j.aml.2007.05.002","DOI":"10.1016\/j.aml.2007.05.002"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] N. Andrei, \u201cAnother hybrid conjugate gradient algorithm for unconstrained optimization,\u201d Numerical Algorithms, vol.47, no.2, pp.143-156, 2008. 10.1007\/s11075-007-9152-9","DOI":"10.1007\/s11075-007-9152-9"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] S. Babaie-Kafaki, \u201cA hybrid conjugate gradient method based on a quadratic relaxation of the Dai-Yuan hybrid conjugate gradient parameter,\u201d Optimization, vol.62, no.7, pp.929-941, 2013. 10.1080\/02331934.2011.611512","DOI":"10.1080\/02331934.2011.611512"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] S. Babaie-Kafaki and R. Ghanbari, \u201cA hybridization of the Hestenes-Stiefel and Dai-Yuan conjugate gradient methods based on a least-squares approach,\u201d Optimization Methods and Software, vol.30, no.4, pp.673-681, 2015. 10.1080\/10556788.2014.966825","DOI":"10.1080\/10556788.2014.966825"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] E.G. Birgin and J.M. Mart\u00ednez, \u201cA spectral conjugate gradient method for unconstrained optimization,\u201d Applied Mathematics and Optimization, vol.43, no.2, pp.117-128, 2001. 10.1007\/s00245-001-0003-0","DOI":"10.1007\/s00245-001-0003-0"},{"key":"6","unstructured":"[6] R. Fletcher, Practical Methods of Optimization, Volume 1: Unconstrained Optimization, Wiley, New York, 1987."},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J.-F. Cai, R. Chan, and B. Morini, \u201cMinimization of an edge-preserving regularization functional by conjugate gradient type methods,\u201d in: Image Processing Based on Partial Differential Equations, in: Mathematics and Visualization, Springer, Berlin Heidelberg, pp.109-122, 2007. 10.1007\/978-3-540-33267-1_7","DOI":"10.1007\/978-3-540-33267-1_7"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] A. Chambolle, \u201cAn Algorithm for Total Variation Minimization and Applications,\u201d Journal of Mathematical Imaging and Vision, vol.20, no.1-2, pp.89-97, 2004. 10.1023\/b:jmiv.0000011321.19549.88","DOI":"10.1023\/B:JMIV.0000011321.19549.88"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] R.H. Chan, C.-W. Ho, and M. Nikolova, \u201cSalt-and-pepper noise removal by median-type noise detector and edge-preserving regularization,\u201d IEEE Trans. Image Process., vol.14, no.10, pp.1479-1485, 2005. 10.1109\/tip.2005.852196","DOI":"10.1109\/TIP.2005.852196"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] R.H. Chan, C. Hu, and M. Nikolova, \u201cAn iterative procedure for removing random-valued impulse noise,\u201d IEEE Signal Process. Lett., vol.11, no.12, pp.921-924, 2004. 10.1109\/lsp.2004.838190","DOI":"10.1109\/LSP.2004.838190"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] T. Chen and H.R. Wu, \u201cAdaptive impulse detection using center-weighted median filters,\u201d IEEE Signal Process. Lett., vol.8, no.1, pp.1-3, 2001. 10.1109\/97.889633","DOI":"10.1109\/97.889633"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] W. Cheng, \u201cA two-term PRP-based descent method,\u201d Numerical Functional Analysis and Optimization, vol.28, no.11-12, pp.1217-1230, 2007. 10.1080\/01630560701749524","DOI":"10.1080\/01630560701749524"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] Y.H. Dai and Y. Yuan, \u201cA nonlinear conjugate gradient with a strong global convergence property,\u201d SIAM Journal on Optimization, vol.10, no.1, pp.177-182, 1999. 10.1137\/s1052623497318992","DOI":"10.1137\/S1052623497318992"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] R. Fletcher and C.M. Reeves, \u201cFunction minimization by conjugate gradients,\u201d The Computer Journal, vol.7, no.2, pp.149-154, 1964. 10.1093\/comjnl\/7.2.149","DOI":"10.1093\/comjnl\/7.2.149"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] B. Gu, Y. Chen, H. Liao, Z. Zhou, and D. Zhang, \u201cA distributed and context-aware task assignment mechanism for collaborative mobile edge computing,\u201d Sensors, vol.18, no.8, 2018. 10.3390\/s18082423","DOI":"10.3390\/s18082423"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] W.W. Hager and H. Zhang, \u201cA new conjugate gradient method with guaranteed descent and an efficient line search,\u201d SIAM Journal on Optimization, vol.16, no.1, pp.170-192, 2005. 10.1137\/030601880","DOI":"10.1137\/030601880"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] M.R. Hestenes and E. Stiefel, \u201cMethods of conjugate gradients for solving linear systems,\u201d Journal of Research of the National Bureau of Standards, vol.49, no.6, pp.409-436, 1952. 10.6028\/jres.049.044","DOI":"10.6028\/jres.049.044"},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] H. Hwang and R.A. Haddad, \u201cAdaptive median filters: New algorithms and results,\u201d IEEE Trans. Image Process., vol.4, no.4, pp.499-502, 1995. 10.1109\/83.370679","DOI":"10.1109\/83.370679"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] H. Ji and Y. Li, \u201cBlock conjugate gradient algorithms for least squares problems,\u201d Journal of Computational and Applied Mathematics, vol.317, pp.203-217, 2017. 10.1016\/j.cam.2016.11.031","DOI":"10.1016\/j.cam.2016.11.031"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] S.-J. Ko and Y.H. Lee, \u201cCenter weighted median filters and their applications to image enhancement,\u201d IEEE Trans. Circuits Syst., vol.38, no.9, pp.984-993, 1991. 10.1109\/31.83870","DOI":"10.1109\/31.83870"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] D.-H. Li and M. Fukushima, \u201cA modified BFGS method and its global convergence in nonconvex minimization,\u201d Journal of Computational and Applied Mathematics, vol.129, no.1-2, pp.15-35, 2001. 10.1016\/s0377-0427(00)00540-9","DOI":"10.1016\/S0377-0427(00)00540-9"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] Y. Liu and C. Storey, \u201cEfficient generalized conjugate gradient algorithms, part 1: theory,\u201d Journal of Optimization Theory and Applications, vol.69, no.1, pp.129-137, 1991. 10.1007\/bf00940464","DOI":"10.1007\/BF00940464"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] J. Liu and S. Li, \u201cMultivariate spectral DY-type projection method for convex constrained nonlinear monotone equations,\u201d Journal of Industrial & Management Optimization, vol.13, no.1, pp.283-295, 2017. 10.3934\/jimo.2016017","DOI":"10.3934\/jimo.2016017"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] I.E. Livieris, V. Tampakas, and P. Pintelas, \u201cA descent hybrid conjugate gradient method based on the memoryless BFGS update,\u201d Numerical Algorithms, https:\/\/doi.org\/10.1007\/s11075-018-0479-1, 2018.","DOI":"10.1007\/s11075-018-0479-1"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] S. Niu, S. Zhang, J. Huang, Z. Bian, W. Chen, G. Yu, Z. Liang, and J. Ma, \u201cLow-dose cerebral perfusion computed tomography image restoration via low-rank and total variation regularizations,\u201d Neurocomputing, vol.197, pp.143-160, 2016. 10.1016\/j.neucom.2016.01.090","DOI":"10.1016\/j.neucom.2016.01.090"},{"key":"26","unstructured":"[26] J. Nocedal and S.J. Wright, Numerical Optimization, 2nd edition, Springer, New York, 2006."},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] E. Polak and G. Ribi\u00e8re, \u201cNote sur la convergence de m\u00e9thodes de directions conjugu\u00e9es,\u201d Revue Fran\u00e7aise d'Informatique et de Recherche Op\u00e9rationnelle, S\u00e9rie rouge, vol.3, no.16, pp.35-43, 1969. 10.1051\/m2an\/196903r100351","DOI":"10.1051\/m2an\/196903R100351"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] H. Sato, \u201cA Dai-Yuan-type Riemannian conjugate gradient method with the weak Wolfe conditions,\u201d Computational Optimization and Application, vol.64, no.1, pp.101-118, 2016. 10.1007\/s10589-015-9801-1","DOI":"10.1007\/s10589-015-9801-1"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] D.G. Skariah and M. Arigovindan, \u201cNested conjugate gradient algorithm with nested preconditioning for non-linear image restoration,\u201d IEEE Trans. Image Process., vol.26, no.9, pp.4471-4482, 2017. 10.1109\/tip.2017.2717182","DOI":"10.1109\/TIP.2017.2717182"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, \u201cImage quality assessment: from error measurement to structural similarity,\u201d IEEE Trans. Image Process., vol.13, no.4, pp.600-612, 2004. 10.1109\/tip.2003.819861","DOI":"10.1109\/TIP.2003.819861"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] Z. Wei, S. Yao, and L. Liu, \u201cThe convergence properties of some new conjugate gradient methods,\u201d Applied Mathematics and Computation, vol.183, no.2, pp.1341-1350, 2006. 10.1016\/j.amc.2006.05.150","DOI":"10.1016\/j.amc.2006.05.150"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] S. Yao and L. Ning, \u201cAn adaptive three-term conjugate gradient method based on self-scaling memoryless BFGS matrix,\u201d Journal of Computational and Applied Mathematics, vol.332, pp.72-85, 2018. 10.1016\/j.cam.2017.10.013","DOI":"10.1016\/j.cam.2017.10.013"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] G. Yu, L. Guan, and W. Chen, \u201cSpectral conjugate gradient methods with sufficient descent property for large-scale unconstrained optimization,\u201d Optimization Methods and Software, vol.23, no.2, pp.275-293, 2008. 10.1080\/10556780701661344","DOI":"10.1080\/10556780701661344"},{"key":"34","doi-asserted-by":"publisher","unstructured":"[34] G. Yu, J. Huang, and Y. Zhou, \u201cA descent spectral conjugate gradient method for impulse noise removal,\u201d Applied Mathematics Letters, vol.23, no.5, pp.555-560, 2010. 10.1016\/j.aml.2010.01.010","DOI":"10.1016\/j.aml.2010.01.010"},{"key":"35","doi-asserted-by":"publisher","unstructured":"[35] G. Yu, W. Xue, and Y. Zhou, \u201cA nonmonotone adaptive projected gradient method for primal-dual total variation image restoration,\u201d Signal Processing, vol.103, pp.242-249, 2014. 10.1016\/j.sigpro.2014.02.025","DOI":"10.1016\/j.sigpro.2014.02.025"},{"key":"36","doi-asserted-by":"publisher","unstructured":"[36] L. Zhang, \u201cTwo modified Dai-Yuan nonlinear conjugate gradient methods,\u201d Numerical Algorithms, vol.50, no.1, pp.1-16, 2009. 10.1007\/s11075-008-9213-8","DOI":"10.1007\/s11075-008-9213-8"},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] L. Zhang, W. Zhou, and D. Li, \u201cGlobal convergence of a modified Fletcher-Reeves conjugate gradient method with Armijo-type line search,\u201d Numerische Mathematik, vol.104, no.4, pp.561-572, 2006. 10.1007\/s00211-006-0028-z","DOI":"10.1007\/s00211-006-0028-z"},{"key":"38","doi-asserted-by":"publisher","unstructured":"[38] Z. Zhou, J. Feng, B. Gu, B. Ai, S. Mumtaz, J. Rodriguez, and M. Guizani, \u201cWhen mobile crowd sensing mMeets UAV: Energy-efficient task assignment and route planning,\u201d IEEE Trans. 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