Cubic regularization in symmetric rank-1 quasi-Newton methods
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[1] A. Schiela. A Flexible Framework for Cubic Regularization Algorithms for Nonconvex Optimization in Function Space , 2019, Numerical Functional Analysis and Optimization.
[2] Jorge J. Moré,et al. Digital Object Identifier (DOI) 10.1007/s101070100263 , 2001 .
[3] David F. Shanno,et al. Conjugate Gradient Methods with Inexact Searches , 1978, Math. Oper. Res..
[4] Marco Sciandrone,et al. On the use of iterative methods in cubic regularization for unconstrained optimization , 2015, Comput. Optim. Appl..
[5] Nicholas I. M. Gould,et al. Adaptive cubic regularisation methods for unconstrained optimization. Part I: motivation, convergence and numerical results , 2011, Math. Program..
[6] David F. Shanno,et al. Interior-point methods for nonconvex nonlinear programming: cubic regularization , 2014, Comput. Optim. Appl..
[7] Richard H. Byrd,et al. Analysis of a Symmetric Rank-One Trust Region Method , 1996, SIAM J. Optim..
[8] Anthony V. Fiacco,et al. Nonlinear programming;: Sequential unconstrained minimization techniques , 1968 .
[9] William C. Davidon,et al. Variable Metric Method for Minimization , 1959, SIAM J. Optim..
[10] C. G. Broyden. The Convergence of a Class of Double-rank Minimization Algorithms 2. The New Algorithm , 1970 .
[11] S. Ulbrich,et al. Geometry Optimization of Branched Sheet Metal Products , 2012 .
[12] R. Fletcher,et al. A New Approach to Variable Metric Algorithms , 1970, Comput. J..
[13] Anima Anandkumar,et al. Efficient approaches for escaping higher order saddle points in non-convex optimization , 2016, COLT.
[14] D. Goldfarb. A family of variable-metric methods derived by variational means , 1970 .
[15] Stefania Bellavia,et al. Strong local convergence properties of adaptive regularized methods for nonlinear least squares , 2015 .
[16] M. J. D. Powell,et al. Recent advances in unconstrained optimization , 1971, Math. Program..
[17] C. G. Broyden. Quasi-Newton methods and their application to function minimisation , 1967 .
[18] Stephen J. Wright,et al. Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .
[19] William C. Davidon,et al. Variance Algorithm for Minimization , 1968, Comput. J..
[20] Yurii Nesterov,et al. Cubic regularization of Newton method and its global performance , 2006, Math. Program..
[21] S. Oren. SELF-SCALING VARIABLE METRIC (SSVM) ALGORITHMS Part II: Implementation and Experiments*t , 1974 .
[22] Robert J. Vanderbei,et al. An Interior-Point Algorithm for Nonconvex Nonlinear Programming , 1999, Comput. Optim. Appl..
[23] P. Wolfe. Convergence Conditions for Ascent Methods. II , 1969 .
[24] Richard H. Byrd,et al. A Theoretical and Experimental Study of the Symmetric Rank-One Update , 1993, SIAM J. Optim..
[25] Roger Fletcher,et al. A Rapidly Convergent Descent Method for Minimization , 1963, Comput. J..
[26] P. Wolfe. Convergence Conditions for Ascent Methods. II: Some Corrections , 1971 .
[27] José Mario Martínez,et al. Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models , 2017, Math. Program..
[28] 高自友,et al. 关于“SUFFICIENT CONDITIONS FOR THE CONVERGENCE OF A VAR … , 1990 .
[29] Nicholas I. M. Gould,et al. Updating the regularization parameter in the adaptive cubic regularization algorithm , 2012, Comput. Optim. Appl..
[30] José Mario Martínez,et al. Cubic-regularization counterpart of a variable-norm trust-region method for unconstrained minimization , 2017, J. Glob. Optim..
[31] T. Cullen. Global , 1981 .
[32] S. I. Feldman,et al. A Fortran to C converter , 1990, FORF.
[33] Lue Li,et al. A trust region algorithm with adaptive cubic regularization methods for nonsmooth convex minimization , 2012, Comput. Optim. Appl..
[34] J. Dussault. Simple unified convergence proofs for Trust Region and a new ARC variant , 2015 .
[35] D. Luenberger,et al. Self-Scaling Variable Metric (SSVM) Algorithms , 1974 .
[36] Shmuel S. Oren,et al. Optimal conditioning of self-scaling variable Metric algorithms , 1976, Math. Program..
[37] H. Y. Huang. Unified approach to quadratically convergent algorithms for function minimization , 1970 .
[38] Marco Sciandrone,et al. A cubic regularization algorithm for unconstrained optimization using line search and nonmonotone techniques , 2016, Optim. Methods Softw..
[39] Nicholas I. M. Gould,et al. Convergence of quasi-Newton matrices generated by the symmetric rank one update , 1991, Math. Program..
[40] Brian W. Kernighan,et al. AMPL: A Modeling Language for Mathematical Programming , 1993 .
[41] D. Luenberger,et al. SELF-SCALING VARIABLE METRIC ( SSVM ) ALGORITHMS Part I : Criteria and Sufficient Conditions for Scaling a Class of Algorithms * t , 2007 .
[42] D. F. Shanno,et al. Matrix conditioning and nonlinear optimization , 1978, Math. Program..
[43] Charles B. Dunham,et al. Remark on “Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]” , 1977, TOMS.
[44] Ruey-Lin Sheu,et al. On the p-regularized trust region subproblem , 2014 .
[45] R. Fletcher. Practical Methods of Optimization , 1988 .
[46] D. Shanno. Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .
[47] Nicholas I. M. Gould,et al. Adaptive cubic regularisation methods for unconstrained optimization. Part II: worst-case function- and derivative-evaluation complexity , 2011, Math. Program..
[48] Peter Deuflhard,et al. Affine conjugate adaptive Newton methods for nonlinear elastomechanics , 2007, Optim. Methods Softw..
[49] Todd Munson,et al. Benchmarking optimization software with COPS. , 2001 .
[50] Andreas Griewank,et al. Cubic overestimation and secant updating for unconstrained optimization of C2, 1 functions , 2014, Optim. Methods Softw..