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• W4248068199 abstract "Free Access References R. Fletcher, R. FletcherSearch for more papers by this author Book Author(s):R. Fletcher, R. FletcherSearch for more papers by this author First published: 23 May 2000 https://doi.org/10.1002/9781118723203.refs AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat References Abadie, J. and Carpentier, J. (1969). Generalization of the Wolfe reduced gradient method to the case of nonlinear constraints, in Optimization (Ed. R Fletcher), Academic Press, London. Akaike, H. (1959). On a successive transformation of probability distribution and its application to the analysis of the optimum gradient method, Ann. Inst. Stat. Math. Tokyo, 11, 1– 16. Al-Baali, M. (1985). Descent property and global convergence of the Fletcher–Reeves method with inexact line search, IMA J. Num. Anal., 5, 121– 124. Al-Baali, M. and Fletcher, R. (1985). Variational methods for nonlinear least squares, J. Oper. Res. Soc., 36, 405– 421. Al-Baali, M. and Fletcher, R. (1986). An efficient line search for nonlinear least squares, J. Opt. Theo. Applns., 48, 359– 378. Andreassen, D. O. and Watson, G. (1976). Nonlinear Chebyshev approximation subject to constraints, J. Approx. Theo., 18, 241– 250. Apostol, T. M. (1957). Mathematical Analysis, Addison-Wesley, Reading, Mass. Appelgren, L. (1971). Integer programming methods for a vessel scheduling problem, Transportation Science, 5, 64– 78. Balinski, M. L. and Gomory, R. E. (1963). A mutual primal-dual simplex method, in Recent Advances in Mathematical Programming (Eds R. L Graves and P Wolfe), McGraw-Hill, New York. M. L. Balinski and P. Wolfe (Eds) (1975). Nondifferentiable Optimization, Mathematical Programming Study 3, North-Holland, Amsterdam. Bandler, J. W. and Charalambous, C. (1972). Practical least p-th optimization of networks, IEEE Trans. Microwave Theo. Tech., (1972 Symposium Issue), 20, 834– 840. Barnes, J. G. P. (1965). An algorithm for solving nonlinear equations based on the secant method, Computer J., 8, 66– 72. Barrodale, I. (1970). On computing best L1 approximations, in Approximation Theory (Ed. A Talbot), Academic Press, London. Barrodale, I. and Roberts, F. D. K. (1973). An improved algorithm for discrete l1 linear approximation, SIAM J. Num. Anal., 10, 839– 848. Bartels, R. H. (1971). A stabilization of the simplex method, Numer. Math., 16, 414– 434. Beale, E. M. L. (1959). On quadratic programming, Naval Res. Log. Quart., 6, 227– 244. Beale, E. M. L. (1967). Numerical methods, in Nonlinear Programming (Ed. J Abadie), North-Holland, Amsterdam. Beale, E. M. L. (1968). Mathematical Programming in Practice, Pitman, London. Beale, E. M. L. (1970). Advanced algorithmic features for general mathematical programming systems, in Integer and Nonlinear Programming (Ed. J Abadie), North-Holland, Amsterdam. Beale, E. M. L. (1972). A derivation of conjugate gradients, in Numerical Methods for Nonlinear Optimization (Ed. F. A Lootsma), Academic Press, London. Beale, E. M. L. (1978). Integer programming, in The State of the Art in Numerical Analysis (Ed. D. A. H Jacobs), Academic Press, London. Bennett, J. M. (1965). Triangular factors of modified matrices, Numer. Math., 7, 217– 221. Benveniste, R. (1979). A quadratic programming algorithm using conjugate search directions, Math. Prog., 16, 63– 80. Biggs, M. C. (1973). A note on minimization algorithms which make use of non-quadratic properties of the objective function, J. Inst Math. Applns., 12, 337– 338. Biggs, M. C. (1975). Constrained minimization using recursive quadratic programming: some alternative subproblem formulations, in Towards Global Optimization (Eds L. C. W Dixon and G. P Szego), North-Holland, Amsterdam. Biggs, M. C. (1978). On the convergence of some constrained minimization algorithms based on recursive quadratic programming, J. Inst. Maths. Applns., 21, 67– 81. Björck, A. (1985). Stability analysis of the method of semi-normal-equations for linear least squares problems, Linköping Univ. Report LiTH-MAT-R-1985-08. Boggs, P. T. and Tolle, J. W. (1984). A family of descent functions for constrained optimization, SIAM J. Num. Anal., 21, 1146– 1161. Boggs, P. T., Tolle, J. W. and Wang, P. (1982). On the local convergence of quasi-Newton methods for constrained optimization, SIAM J. Control Optim., 20, 161– 171. Bradley, J. and Clyne, H. M. (1976). Applications of geometric programming to building design problems, in Optimization in Action (Ed. L. C. W Dixon), Academic Press, London. Branin, F. H. and Hoo, S. K. (1972). A method for finding multiple extrema of a function of n variables, in Numerical Methods for Nonlinear Optimization (Ed. F. A Lootsma), Academic Press, London. Brent, R. P. (1973a). Algorithms for Minimization without Derivatives, Prentice-Hall Inc., Englewood Cliffs, N. J. Brent, R. P. (1973b). Some efficient algorithms for solving systems of nonlinear equations, SIAM J. Num. Anal., 10, 327– 344. Breu, R. and Burdet, C.-A. (1974). Branch and bound experiments in zero–one programming, in Approaches to Integer Programming (Ed. M. L Balinski), Mathematical Programming Study 2, North-Holland, Amsterdam. Brodlie, K. W. (1975). A new direction set method for unconstrained minimization without evaluating derivatives, J. Inst. Maths. Applns., 15, 385– 396. Brodlie, K. W. (1977). Unconstrained minimization, in The State of the Art in Numerical Analysis (Ed. Jacobs D. A. H), Academic Press, London. Brodlie, K. W., Gourlay, A. R. and Greenstadt, J. L. (1973). Rank-one and rank-two corrections to positive definite matrices expressed in product form, J. Inst. Maths. Applns., 11, 73– 82. Brown, K. M. and Dennis, J. E. (1971). A new algorithm for nonlinear least squares curve fitting, in Mathematical Software (Ed. J. R Rice), Academic Press, New York. Broyden, C. G. (1965). A class of methods for solving nonlinear simultaneous equations, Maths. Comp., 19, 577– 593. Broyden, C. G. (1967). Quasi-Newton methods and their application to function minimization, Maths. Comp., 21, 368– 381. Broyden, C. G. (1970). The convergence of a class of double rank minimization algorithms, parts I and II, J. Inst. Maths. Applns., 6, 76– 90 and 222–231. Broyden, C. G. (1975). Basic Matrices, Macmillan, London. Broyden, C. G., Dennis, J. E. and Moré, J. J. (1973). On the local and superlinear convergence of quasi-Newton methods, J. Inst. Maths. Applns., 12, 223– 245. Buckley, A. (1975). Constrained minimization using Powell's conjugacy approach, AERE Harwell report CSS 22. Bunch, J. R. and Parlett, B. N. (1971). Direct methods for solving symmetric indefinite systems of linear equations, Num. Anal., 8, 639– 655. Buys, J. D. (1972). Dual algorithms for constrained optimization problems, Ph.D. thesis, Univ. of Leiden. Byrd, R. H. (1984 ). On the convergence of constrained optimization methods with accurate Hessian information on a subspace, Univ. of Colorado at Boulder, Dept. of Comp. Sci. Report CU-CS-270–84. Byrd, R. H. and Shultz, G. A. (1982). A practical class of globally convergent active set strategies for linearly constrained optimization, Univ. of Colorado at Boulder, Dept. of Comp. Sci. Report CU-CS-238-82. Carroll, C. W. (1961). The created response surface technique for optimizing nonlinear restrained systems, Operations Res., 9, 169– 84. Chamberlain, R. M. (1979). Some examples of cycling in variable metric methods for constrained minimization, Math. Prog., 16, 378– 383. Chamberlain, R. M., Lemarechal, C., Pedersen, H. C. and Powell, M. J. D. (1982). The watchdog technique for forcing convergence in algorithms for constrained optimization, in Algorithms for Constrained Minimization of Smooth Nonlinear Functions (Eds A. G Buckley and J.-L Goffin), Mathematical Programming Study 16, North-Holland, Amsterdam. Charalambous, C. (1977). Nonlinear least p-th optimization and nonlinear programming, Math. Prog., 12, 195– 225. Charalambous, C. (1979). Acceleration of the least p-th algorithm for minimax optimization with engineering applications, Math. Prog., 17, 270– 297. Charalambous, C. and Conn, A. R. (1978). An efficient method to solve the minimax problem directly, SIAM J. Num. Anal., 15, 162– 187. Chames, A. (1952). Optimality and degeneracy in linear programming, Econometrica, 20, 160– 170. Clarke, F. H. (1975). Generalized gradients and applications, Trans. Amer. Math. Soc., 205, 247– 262. Coleman, T. F. and Conn, A. R. (1982a). Nonlinear programming via an exact penalty function: asymptotic analysis, Math. Prog., 24, 123– 136. Coleman, T. F. and Conn, A. R. (1982b). Nonlinear programming via an exact penalty function: global analysis, Math. Prog., 24, 137– 161. Colville, A. R. (1968). A comparative study on nonlinear programming codes, IBM NY Scientific Center Report 320–2949. Conçus, P. (1967). Numerical solution of the minimal surface equation, Maths. Comp., 21, 340– 350. Conn, A. R. (1973). Constrained optimization using a nondifferentiable penalty function, Num. Anal., 13, 145– 154. Conn, A. R. (1979). An efficient second order method to solve the (constrained) minimax problem, Univ. of Waterloo Dept. of Combinatorics and Optimization Res. Report CORR-79–5. Conn, A. R. and Sinclair, J. W. (1975). Quadratic programming via a non-differentiable penalty function, Univ. of Waterloo Dept. of Combinatorics and Optimization Report CORR 75/15. Coope, I. D. (1976). Conjugate direction algorithms for unconstrained optimization, Ph.D. thesis, Univ. of Leeds Dept. of Mathematics. Coope, I. D. and Fletcher, R. (1980). Some numerical experience with a globally convergent algorithm for nonlinearly constrained optimization, J. Opt. Theo. Applns., 32, 1– 16. Cottle, R. W. and Dantzig, G. B. (1968). Complementary pivot theory of mathematical programming, J. Linear Algebra Applns., 1, 103– 125. Courant, R. (1943). Variational methods for the solution of problems of equilibrium and vibration, Bull. Amer. Math. Soc., 49, 1– 23. Curtis, A. R., Powell, M. J. D. and Reid, J. K. (1974). On the estimation of sparse Jacobian matrices, J. Inst. Maths. Applns., 13, 117– 120. Curry, H. (1944). The method of steepest descent for nonlinear minimization problems, Quart. Appl. Math., 2, 258– 261. Dantzig, G. B. (1963). Linear Programming and Extensions, Princeton University Press, Princeton, N. J. Dantzig, G. B., Orden, A. and Wolfe, P. (1955). The generalized simplex method for minimizing a linear form under linear inequality restraints, Pacific J. Maths., 5, 183– 195. Dantzig, G. B. and Wolfe, P. (1960). A decomposition principle for linear programs, Operations Res., 8, 101– 111. Davidon, W. C. (1959). Variable metric method for minimization, AEC Res. and Dev. Report ANL-5990 (revised). Davidon, W. C. (1968). Variance algorithms for minimization, Computer J. 10, 406– 410. Davidon, W. C. (1975). Optimally conditioned optimization algorithms without line searches, Math. Prog., 9, 1– 30. Davidon, W. C. (1980). Conic approximations and collinear scaling for optimizers, SIAM J. Num. Anal., 17, 268– 281. Dembo, R. S. (1976). A set of geometric programming test problems and their solutions, Math. Prog., 10, 192– 213. Dembo, R. S. (1979). Second order algorithms for the posynomial geometric programming dual, Part I: Analysis, Math. Prog., 17, 156– 175. Dembo, R. S. and Avriel, M. (1978). Optimal design of a membrane separation process using geometric programming, Math. Prog., 15, 12– 25. Dembo, R. S. and Klincewicz, J. G. (1981). A scaled reduced gradient algorithm for network flow problems with convex separable costs, in Network Models and Applications (Ed. D Klingman and J. M Mulvey), Mathematical Programming Study 15, North-Holland Amsterdam. Demyanov, V. F. and Malozemov, V. N. (1971). The theory of nonlinear minimax problems, Uspekhi Matematcheski Nauk, 26, 53– 104. Dennis, J. E. (1977). Nonlinear least squares and equations, in The State of the Art in Numerical Analysis (Ed. D. A. H Jacobs), Academic Press, London. Dennis, J. E., Gay, D. M. and Welsch, R. E. (1981). An adaptive nonlinear least-squares algorithm, ACM Trans. Math. Software, 7, 348– 368 Di Pillo, G. and Grippo, L. (1979). A new class of augmented Lagrangians in nonlinear programming, SIAM J. Control Optim., 17, 618– 628. Dixon, L. C. W. (1972). Quasi-Newton algorithms generate identical points, Math. Prog., 2, 383– 387. Dixon, L. C. W. (1973). Conjugate directions without line searches, J. Inst. Maths. Applns., 11, 317– 328. L. C. W. Dixon (ed.) (1976). Optimization in Action, Academic Press, London. L. C. W. Dixon and G. P. Szego (eds) (1975). Towards Global Optimization, North-Holland, Amsterdam. Duffin, R. J., Peterson, E. L. and Zener, C. (1967). Geometric Programming—Theory and Application, John Wiley, New York. El-Attar, R. A., Vidyasagar, M. and Dutta, S. R. K. (1979). An algorithm for l1-norm minimization with application to nonlinear l1 approximation, SIAM J. Num. Anal., 16, 70– 86. Eriksson, J. (1980). A note on solution of large sparse maximum entropy problems with linear equality constraints, Math. Prog., 18, 146– 154. Fiacco, A. V. and McCormick, G. P. (1968). Nonlinear Programming, John Wiley, New York. Fisher, M. L., Northup, W. D. and Shapiro, J. F. (1975). Using duality to solve discrete optimization problems: theory and computational experience, in Nondifferentiable Optimization (Eds M. L Balinski and P Wolfe), Mathematical Programming Study 3, North-Holland, Amsterdam. Fletcher, R. (1965). Function minimization without evaluating derivatives—a reivew, Computer J., 8, 33– 41. Fletcher, R. (1969). A technique for orthogonalization, J. Inst. Maths. Applns., 5, 162– 116. Fletcher, R. (1970a). A new approach to variable metric algorithms, Computer J., 13, 317–322. Fletcher, R. (1970b). The calculation of feasible points for linearly constrained optimization problems, AERE Harwell Report AERE-R6354. Fletcher, R. (1971a). A modified Marquardt subroutine for nonlinear least squares, AERE Harwell Report AERE-R6799. Fletcher, R. (1971b). A general quadratic programming algorithm, J. Inst. Maths. Applns., 7, 76– 91. Fletcher, R. (1972a). Conjugate direction methods, in Numerical Methods for Unconstrained Optimization (Ed. W Murray), Academic Press, London. Fletcher, R. (1972b). An algorithm for solving linearly constrained optimization problems, Math. Prog., 2, 133– 165. Fletcher, R. (1972c). Minimizing general functions subject to linear constraints, in Numerical Methods for Nonlinear Optimization (Ed. F. A Lootsma), Academic Press, London. Fletcher, R. (1973). An exact penalty function for nonlinear programming with inequalities, Math. Prog., 5, 129– 150. Fletcher, R. (1975). An ideal penalty function for constrained optimization, J. Inst. Maths. Applns., 15, 319– 342, and in Nonlinear Programming 2 (Eds O. L. Mangasarian, R. R. Meyer and S. M. Robinson), Academic Press, London (1975). Fletcher, R. (1978). On Newton's method for minimization, in Proc. IX Int. Symp. on Math. Programming (Ed. A Prekopa), Akademiai Kiado, Budapest. Fletcher, R. (1981). Numerical experiments with an L1 exact penalty function method, in Nonlinear Programming 4 (Eds O. L Mangasarian, R. R Meyer and S. M Robinson), Academic Press, New York. Fletcher, R. (1982a). A model algorithm for composite nondifferentiable optimization problems, in Nondifferential and Variational Techniques in Optimization (Eds D. C Sorensen and R. J.-B Wets), Mathematical Programming Study 17, North-Holland, Amsterdam. Fletcher, R. (1982b). Second order corrections for nondifferentiable optimization, in Numerical Analysis, Dundee 1981 (Ed. G. A Watson), Lecture Notes in Mathematics 912, Springer-Verlag, Berlin. Fletcher, R. (1985a). Degeneracy in the presence of round-off errors, Univ. of Dundee Dept. of Math. Sci. Report NA/79. Fletcher, R. (1985b). An l1 penalty method for nonlinear constraints, in Numerical Optimization 1984 (Eds P. T Boggs, R. H Byrd and R. B Schnabel), SIAM Publications, Philadelphia. Fletcher, R. and Freeman, T. L. (1977). A modified Newton method for minimization, J. Opt. Theo. Applns., 23, 357– 372. Fletcher, R. and Jackson, M. P. (1974). Minimization of a quadratic function of many variables subject only to lower and upper bounds, J. Inst. Maths. Applns., 14, 159– 174. Fletcher, R. and Matthews, S. P. J. (1984). Stable modification of explicit LU factors for simplex updates, Math. Prog., 30, 267– 284. Fletcher, R. and Matthews, S. P. J. (1985). A stable algorithm for updating triangular factors under a rank one change, Maths. Comp., 45, 471– 485. Fletcher, R. and McCann, A. P. (1969). Acceleration techniques for nonlinear programming, in Optimization (Ed. R Fletcher), Academic Press, London. Fletcher, R. and Powell, M. J. D. (1963). A rapidly convergent descent method for minimization, Computer. J., 6, 163– 168. Fletcher, R. and Powell, M. J. D. (1974). On the modification of LDLT factorizations, Maths. Comp., 29, 1067– 1087. Fletcher, R. and Reeves, C. M. (1964). Function minimization by conjugate gradients, Computer J., 7, 149– 154. Fletcher, R. and Sainz de la Maza, E. (1987). Nonlinear programming and nonsmooth optimization by successive linear programming, Univ. of Dundee, Dept. of Math. Sci. Report NA/100. Fletcher, R. and Sinclair, J. W. (1981). Degenerate values for Broyden methods, J. Opt. Theo. Applns., 33, 311– 324. Fletcher, R. and Watson, G. A. (1980). First and second order conditions for a class of nondifferentiable optimization problems, Math. Prog., 18, 291– 307; abridged from a Univ. of Dundee Dept. of Mathematics Report NA/28 (1978). Fletcher, R. and Xu, C. (1985). Hybrid methods for nonlinear least squares, Univ. of Dundee Dept. of Math. Sci. Report NA/92, (to appear in IMA J. Num. Anal.). Forrest, J. J. H. and Tomlin, J. A. (1972). Updated triangular factors of the basis to maintain sparsity in the product form simplex method, Math. Prog., 2, 263– 278. Frisch, K. R. (1955). The logarithmic potential method of convex programming, Oslo Univ. Inst. of Economics Memorandum, May 1955. Gacs, P. and Lovasz, L. (1979). Khachian's algorithm for linear programming, Stanford Univ. Dept. of Comp. Sci. Report CS 750. Gay, D. M. (1979). Some convergence properties of Broyden's method, SIAM J. Num. Anal., 16, 623– 630. Gay, D. M. (1985). A variant of Karmarkar's linear programming algorithm for problems in standard form, AT&T Bell Labs, Num. Anal. Manuscript 85–10. Gentleman, W. M. (1973). Least squares computations by Givens' transformations without square roots, J. Inst. Maths. Applns., 12, 329– 336. Gerber, R. R. and Luk, F. T. (1980). A generalized Broyden's method for solving simultaneous linear equations, Cornell Univ. Dept. of Comp. Sci. Report TR-80438. Gill, P. E. and Murray, W. (1972). Quasi-Newton methods for unconstrained optimization, J. Inst. Maths. Applns., 9, 91– 108. Gill, P. E. and Murray, W. (1973). A numerically stable form of the simplex algorithm, J. Linear Algebra Applns., 7, 99– 138. Gill, P. E. and Murray, W. (1974a). Newton type methods for linearly constrained optimization, in Numerical Methods for Constrained Optimization (Eds P. E Gill and W Murray), Academic Press, London. Gill, P. E. and Murray, W. (1974b). Methods for large-scale linearly constrained problems, in Numerical Methods for Constrained Optimization (Eds P. E Gill and W Murray), Academic Press, London. Gill, P. E. and Murray, W. (1974c). Quasi-Newton methods for linearly constrained optimization, in Numerical Methods for Constrained Optimization (Eds P. E Gill and W Murray), Academic Press, London. Gill, P. E. and Murray, W. (1976a). Nonlinear least squares and nonlinearly constrained optimization, in Numerical Analysis, Dundee 1975 (Ed. G. A Watson), Lecture Notes in Mathematics 506, Springer-Verlag, Berlin. Gill, P. E. and Murray, W. (1976b). Minimization of a nonlinear function subject to bounds on the variables, NPL Report NAC 72. Gill, P. E. and Murray, W. (1978a). Modification of matrix factorizations after a rank-one change, in The State of the Art in Numerical Analysis (Ed. D. A. H Jacobs), Academic Press, London. Gill, P. E. and Murray, W. (1978b). Numerically stable methods for quadratic programming, Math. Prog., 14, 349– 372. Gill, P. E., Murray, W. and Picken, S. M. (1972). The implementation of two modified Newton algorithms for unconstrained optimization, NPL Report NAC24. Gill, P. E., Murray, W. and Pitfield, R. A. (1972). The implementation of two revised quasi-Newton algorithms for unconstrained optimization, NPL Report NAC11. Gill, P. E., Gould, N. I. M., Murray, W., Saunders, M. A. and Wright, M. H. (1984a). Weighted Gram–Schmidt method for convex quadratic programming, Math. Prog., 30, 176– 195. Gill, P. E., Murray, W, Saunders, M. A. and Wright, M. H. (1984b). Sparse matrix methods in optimization, SIAM J. Sci. Stat. Comp., 5, 562– 589. Gill, P. E., Murray, W, Saunders, M. A., Tomlin, J. A. and Wright, M. H. (1985). On projected Newton barrier methods for linear programming and an equivalence to Karmarkar's projective method, Stanford Univ. Systems Optimization Lab. Report SOL 85–11. Gill, P. E., Murray, W., Saunders, M. A. and Wright, M. H. (1986). Some theoretical properties of an augmented Lagrangian merit function, Stanford Univ. Systems Optimization Lab. Report SOL 86–6. Goldfarb, D. (1969). Extension of Davidon's variable metric method to maximization under linear inequality and equality constraints, SIAM J. Appl. Math., 17, 739– 764. Goldfarb, D. (1970). A family of variable metric methods derived by variational means, Maths. Comp., 24, 23– 26. Goldfarb, D. (1972). Extensions of Newton's method and simplex methods for solving quadratic programs, in Numerical Methods for Nonlinear Optimization (Ed. F. A Lootsma), Academic Press, London. Goldfarb, D. (1980). Curvilinear path steplength algorithms for minimization which use directions of negative curvature, Math. Prog., 18, 31– 40. Goldfarb, D. and Idnani, A. (1981). Dual and primal-dual methods for solving strictly convex quadratic programs, in IIMAS Workshop in Numerical Analysis (1981) Mexico (Ed. J. P Hennart), Lecture Notes in Mathematics, Springer-Verlag, Berlin. Goldfarb, D. and Idnani, A. (1983). A numerically stable dual method for solving strictly convex quadratic programs, Math. Prog., 21, 1– 33. Goldfarb, D. and Reid, J. K. (1977). A practicable steepest-edge algorithm, Math. Prog., 12, 361– 371. Goldfeld, S. M., Quandi, R. E. and Trotter, H. F. (1966). Maximisation by quadratic hill-climbing, Econometrica, 34, 541– 551. Goldstein, A. A. (1965). On steepest descent, SIAM J. Control, 3, 147– 151. Goldstein, A. A. and Price, J. F. (1967). An effective algorithm for minimization, Numer. Math., 10, 184– 189. Grandinetti, L. (1979). Factorization versus nonfactorization in quasi-Newtonian algorithms for differentiable optimization, in Methods for Operations Research, Hain Verlay. Graves, G. W. and Brown, G. G. (1979). Computational implications of degeneracy in large scale mathematical programming, X Symposium on Mathematical Programming, Montreal, August 1979. Greenstadt, J. L. (1970). Variations of variable metric methods, Maths. Comp., 24, 1– 22. Grigoriadis, M. (1982). Minimum-cost network flows, Part I: an implementation of the network simplex method, Rutgers Univ. Lab. for Comp. Sci. Report LCSR-TR-37. Griewank, A. and Toint, Ph. L. (1984). Numerical experiments with partially separable optimization problems, in Numerical Analysis, Dundee 1983 (Ed. D. F Griffiths), Lecture Notes in Mathematics 1066, Springer-Verlag, Berlin. Hadley, G. (1961). Linear Algebra, Addison-Wesley, Reading, Mass. Hadley, G. (1962). Linear Programming, Addison-Wesley, Reading, Mass. Hald, J. and Madsen, K. (1981). Combined LP and quasi-Newton methods for minimax optimization, Math. Prog., 20, 49– 62. Hald, J. and Madsen, K. (1985). Combined LP and quasi-Newton methods for nonlinear l1 optimization, SIAM J. Num. Anal., 22, 68– 80. Han, S. P. (1976). Superlinearly convergent variable metric algorithms for general nonlinear programming problems, Math. Prog., 11, 263– 282. Han, S. P. (1977). A globally convergent method for nonlinear programming, J. Opt. Theo. Applns., 22, 297– 309. Han, S. P. and Mangasarian, O. L. (1979). Exact penalty functions in nonlinear programming, Math. Prog., 17, 251– 269. Hardy, G. H. (1960). A Course of Pure Mathematics ( 10th edn), Cambridge Univ. Press; Cambridge, England. Hebden, M. D. (1973). An algorithm for minimization using exact second derivatives, AERE Harwell Report TP515. Hestenes, M. R. (1969). Multiplier and gradient methods, J. Opt. Theo. Applns., 4, 303– 320, and in Computing Methods in Optimization Problems, 2 (Eds L. A. Zadeh, L. W. Neustadt and A. V. Balakrishnan), Academic Press, New York (1969). Hestenes, M. R. and Stiefel, E. (1952). Methods of conjugate gradients for solving linear systems, J. Res. N.B.S., 49, 409– 436. Holt, J. N. and Fletcher, R. (1979). An algorithm for constrained nonlinear least squares, J. Inst. Maths. Applns., 23, 449– 464. Hoshino, S. (1972). A formulation of variable metric methods, J. Inst. Maths. Applns., 10, 394– 403. Huang, H. Y. (1970). Unified approach to quadratically convergent algorithms for function minimization, J. Opt. Theo. Applns., 5, 405– 423. Jensen, P. A. and Barnes, J. W. (1980). Network Flow Programming, John Wiley, New York. Kamesam, P. V. and Meyer, R. R. (1984). Multipoint methods for separable nonlinear networks, in Mathematical Programming at Oberwolfach (Eds B Korte and K Ritter), Mathematical Programming Study 22, North-Holland, Amsterdam. Karmarkar, N. (1984). A new polynomial-time algorithm for linear programming, Combinatorics, 4, 373– 395. Kennington, J. L. and Helgason, R. V. (1980). Algorithms for Network Programming, John Wiley, New York. Kershaw, D. S. (1978). The incomplete Cholesky-conjugate gradient method for the iterative solution of systems of linear equations, J. Comp. Phys., 26, 43– 65. Khachiyan, L. G. (1979). A polynomial algorithm in linear programming, Doklady Akad. Nauk. USSR, 244, 1093– 1096, translated as Soviet Mathematics Dokladv, 20, 191–194. Kiefer, J. (1957). Optimal sequential search and approximation methods under minimum regularity conditions, SIAM J. Appl. Math., 5, 105– 136. Klee, V. and Minty, G. J. (1971). How good is the simplex algorithm?, in Inequalities III (Ed. O Shisha), Academic Press. Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming, in Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability (Ed. J Neyman), University of California Press. Lancaster, P. (1969). Theory of Matrices, Academic Press, New York. Lasdon, L. S. (1985). Nonlinear programming algorithms—applications, software and comparisons, in Numerical Optimization 1984 (Eds P. T Boggs, R. H Byrd and R. B Schnabel), SIAM Publications, Philadelphia. Lemarechal, C. (1978). Bundle methods in nonsmooth optimization, in Nonsmooth Optimization (Eds C Lemarechal and R. Mifflin), IIASA Proceedings 3, Pergamon, Oxford. C. Lemarechal and R. Mifflin (eds) (1978). Nonsmooth Optimization, IIASA Proceedings 3, Pergamon, Oxford. Lemke, C. E. (1965). Bimatrix equilibrium points and mathematical programming, Management Sci., 11, 681– 689. Levenberg, K. (1944). A method for the solution of certain nonlinear problems in least squares, Quart. Appl. Math., 2, 164– 168. Lustig, I. J. (1985). A practical approach to Karmarkar's algorithm, Stanford Univ. Systems Optimization Lab. Report SOL 85–5. Madsen, K. (1975). An algorithm for minimax solution of overdetermined systems of nonlinear equations, J. Inst. Maths. Applns., 16, 321– 328. Maratos, N. (1978). Exact penalty function algorithms for finite dimensional and control optimization problems, Ph.D. thesis, Univ. of London. Marquardt, D. W. (1963). An algorithm for least squares estimation of nonlinear parameters, SIAM J., 11, 431– 441. Marsten, R. E. (1975). The use of the boxstep method in discrete optimization, in Nondifferentiable Optimization (Eds M. L Balinski and P Wolfe), Mathematical Programming Study 3, North-Holland, Amsterdam. Mayne, D. Q. (1980). On the use of exact penalty functions to determine step length in optimization algorithms, in Numerical Analysis, Dundee 1979 (Ed. G. A Watson), Lecture Notes in Mathematics 773, Springer-Verlag, Berlin. McCormick, G. P. (1977). A modification of Armijo's step-size rule for negative curvature, Math. Prog., 13, 111– 115. McCormick. G. P. and Pearson, J. D. (1969). Variable metric methods and unconstrained optimization, in Optimization (Ed. R Fletcher), Academic Press, London. McLean, R. A. and Watson, G. A. (1980). Numerical methods for nonlinear discrete L1 approximation problems, in Numerical Methods of Approximation Theory (Eds L Collatz, G Meinardus and H Warner), ISNM 52, Birkhauser-Verlag, Basle. Moré, J. J. (1978). The Levenberg–Marquardt algorithm: implementation and theory, in Numerical Analysis, Dundee 1977 (Ed. G. A. Watson), Lecture Notes in Mathematics 630, Springer-Verlag, Berlin. Moré, J. J. and Sorensen, D. C. (1982). Newton's method, Argonne Nat. Lab. Report ANL-82-8. Morrison, D. D. (1968). Optimization by least squares, SIAM J. Num. Anal., 5, 83– 88. Murray, W. (1969). An algorithm for constrained minimization, in Optimization (Ed. R Fletcher), Academic Press, London. Murray, W. (1971). An algorithm for finding a local minimum of an indefinite quadratic program, NPL Report NAC 1. Murray, W. (1972). Second derivative methods, in Numerical Methods for Unconstrained Optimization (Ed. W Murray), Academic Press, London. Murtagh, B. A. and Sargent, R. W. H. (1969). A constrained minimization method with quadratic convergence, in Optimization (Ed. R Fletcher), Academic Press, London. Nelder, J. A. and Mead, R. (1965). A simplex method for function minimization, Computer J., 7, 308– 313. Nocedal, J. and Overton, M. L. (1985). Projected Hessian updating algorithms for nonlinearly constrained optimization, SIAM J. Num. Anal., 22, 821– 850. Oren, S. S. (1974). On the selection of parameters in self-scaling variable metric algorithms, Math. Prog., 7, 351– 367. Osborne, M. R. (1972). Topics in optimization, Stanford Univ. Dept. of Comp. Sci. Report STAN-CS-72-279. Osborne, M. R. (1985). Finite Algorithms in Optimization and Data Analysis, John Wiley, Chichester. Osborne, M. R. and Watson, G. A. (1969). An algorithm for minimax approximation in the nonlinear case, Computer J., 12, 63– 68. Pietrzykowski, T. (1969). An exact potential method for constrained maxima, SIAM J. Num. Anal., 6, 217– 238. Polak, E. (1971). Computational Methods in Optimization: A Unified Approach, Academic Press, New York. Powell, M. J. D. (1964). An efficient method for finding the minimum of a function of several variables without calculating derivatives, Computer J., 7, 155– 162. Powell, M. J. D. (1965). A method for minimizing a sum of squares of nonlinear functions without calculating derivatives, Computer J., 11, 302– 304. Powell, M. J. D. (1969). A method for nonlinear constraints in minimization problems, in Optimization (Ed. R Fletcher), Academic Press, London. Powell, M. J. D. (1970a). A new algorithm for unconstrained optimization, in Nonlinear Programming (Eds J. B Rosen, O. L Mangasarian and K Ritter), Academic Press, New York. Powell, M. J. D. (1970b). A hybrid method for nonlinear equations, in Numerical Methods for Nonlinear Algebraic Equations (Ed. P Rabinowitz), Gordon and Breach, London. Powell, M. J. D. (1971). On the convergence of the variable metric algorithm, J. Inst. Maths. Applns., 7, 21– 36. Powell, M. J. D. (1972a). Unconstrained minimization and extensions for constraints, AERE Harwell Report TP495. Powell, M. J. D. (1972b). Some properties of the variable metric algorithm, in Numerical Methods for Nonlinear Optimization (Ed. F. A Lootsma), Academic Press, London. Powell, M. J. D. (1972c). Quadratic termination properties of minimization algorithms, I and II, J. Inst. Maths. Applns., 10, 333– 342 and 343–357. Powell, M. J. D. (1972d). Unconstrained minimization algorithms without computation of derivatives, AERE Harwell Report TP483. Powell, M. J. D. (1972e). Problems related to unconstrained optimization, in Numerical Methods for Unconstrained Optimization (Ed. W Murray), Academic Press, London. Powell, M. J. D. (1973). On search directions for minimization algorithms, Math. Prog., 4, 193– 201. Powell, M. J. D. (1975a). A view of minimization algorithms that do not require derivatives, ACM Trans. Math. Software, 1, 97– 107. Powell, M. J. D. (1975b). Convergence properties of a class of minimization algorithms, in Nonlinear Programming 2 (Eds O. L Mangasarian, R. R Meyer and S. M Robinson, Academic Press, New York. Powell, M. J. D. (1976). Some global convergence properties of a variable metric algorithm for minimization without exact line searches, in SIAM-AMS Proceedings, Vol. IX (Eds R. W Cottle and C. E Lemke), SIAM Publications, Philadelphia. Powell, M. J. D. (1977a). Quadratic termination properties of Davidon's new variable metric algorithm, Math. Prog., 12, 141– 147. Powell, M. J. D. (1977b). Restart procedures for the conjugate gradient method, Math. Prog., 12, 241– 254. Powell, M. J. D. (1977c). Constrained optimization by a variable metric method, Cambridge Univ. DAMTP Report 77/NA6. Powell, M. J. D. (1978a). A fast algorithm for nonlinearly constrained optimization calculations, in Numerical Analysis, Dundee 1977 (Ed. G. A Watson), Lecture Notes in Mathematics 630, Springer-Verlag, Berlin. Powell, M. J. D. (1978b). The convergence of variable metric methods for nonlinearly constrained optimization calculations, in Nonlinear Programming 3 (Eds O. L Mangasarian, R. R Meyer and S. M Robinson), Academic Press, New York. Powell, M. J. D. (1985a). On the quadratic programming algorithm of Goldfarb and Idnani, in Mathematical Programming Essays in Honor of George B. Dantzig, Part II (Ed. R. W Cottle), Mathematical Programming Study 25, North-Holland, Amsterdam. Powell, M. J. D. (1985b). On error growth in the Bartels–Golub and Fletcher–Matthews algorithms for updating matrix factorizations, Cambridge Univ. DAMTP Report 85/NA10. Powell, M. J. D. (1985c). The performance of two subroutines for constrained optimization on some difficult test problems, in Numerical Optimization 1984 (Eds P. T Boggs, R. H Byrd and R. B Schnabel), SIAM Publications, Philadelphia. Powell, M. J. D. (1985d). How bad are the BFGS and DFP methods when the objective function is quadratic?, Univ. of Cambridge DAMTP Report 85/NA4. Powell, M. J. D. (1985e). Updating conjugate directions by the BFGS formula, Univ. of Cambridge DAMTP Report 85/NA11. Powell, M. J. D. and Yuan, Y. (1986). A recursive quadratic programming algorithm that uses differentiable penalty functions, Math. Prog., 35, 265– 278. Pshenichnyi, B. N. (1978). Nonsmooth optimization and nonlinear programming, in Nonsmooth Optimization (Eds C Lemarechal and R Mifflin), IIASA Proceedings 3, Pergamon, Oxford. Rail, L. B. (1969). Computational Solution of Nonlinear Operator Equations, John Wiley, New York. Reid, J. K. (1971a). On the method of conjugate gradients for the solution of large sparse systems of equations, in Large Sparse Sets of Linear Equations (Ed. J. K Reid), Academic Press, London. J. K. Reid (ed.) (1971b). Large Sparse Sets of Linear Equations, Academic Press, London. Reid, J. K. (1975). A sparsity-exploiting version of the Bartels–Golub decomposition for linear programming bases, AERE Harwell Report CSS20. Rhead, D. (1974). On a new class of algorithms for function minimization without evaluating derivatives, Univ. of Nottingham, Dept. of Mathematics Report. Robinson, S. M. (1982). Generalised equations and their solutions, Part II: applications to nonlinear programming, in Optimality and Stability in Mathematical Programming (Ed. M. G ), Mathematical Programming Study 19, North-Holland, Amsterdam. Rockafeller, R. T. (1974). Augmented Lagrange multiplier functions and duality in non-convex programming, SIAM J. Control, 12, 268– 285. Rosen, J. B. (1960). The gradient projection method for nonlinear programming, Part I: Linear constraints, J. SIAM, 8, 181– 217. Rosen, J. B. (1961). The gradient projection method for nonlinear programming, Part II: Nonlinear constraints, J. SIAM, 9, 514– 532. Sargent, R. W. H. (1974). Reduced gradient and projection methods for nonlinear programming, in Numerical Methods for Constrained Optimization (Eds P. E Gill and W Murray), Academic Press, London. Saunders, M. A. (1972). Product form of the Cholesky factorization for large-scale linear Programming, Stanford Univ. Report STAN-CS-72–301. Schubert, L. K. (1970). Modification of a quasi-Newton method for nonlinear equations with a sparse Jacobian, Maths. Comp., 24, 27– 30. Schittkowski, K. (1983). On the convergence of a sequential quadratic programming method with an augmented Lagrangian line search function, Math. Operationsforschung u. Stat., Ser. Optimization, 14, 197– 216. Schittkowski, K. and Stoer, J. (1979). A factorization method for the solution of constrained linear least-squares problems allowing data changes, Numer. Math., 31, 431– 463. Shanno, D. F. (1970). Conditioning of quasi-Newton methods for function minimization, Maths. Comp., 24, 647– 656. Shanno, D. F. and Phua, K.-H. (1978). Matrix conditioning and nonlinear optimization, Math. Prog., 14, 149– 160. Sherman, A. H. (1978). On Newton iterative methods for the solution of systems of nonlinear equations, SIAM J. Num. Anal., 15, 755– 771. Sinclair, J. E. and Fletcher, R. (1974). A new method of saddle point location for the calculation of defect migration energies, J. Phys. C: Solid State Phys., 7, 864– 870. Sinclair, J. W. (1979). On quasi-Newton methods, Ph.D. thesis, Univ. of Dundee Dept. of Mathematics. Smith, C. S. (1962). The automatic computation of maximum likelihood estimates, N.C.B. Sci. Dept. Report SC846/MR/40. Spendley, W., Hext, G. R. and Himsworth, F. R. (1962). Sequential application of simplex designs in optimization and evolutionary operation, Technometrics, 4, 441– 461. Stewart, G. W. (1967). A modification of Davidon's minimization method to accept difference approximations to derivatives, J. Ass. Comput. Mach., 14, 72– 83. Stoer, J. (1975). On the convergence rate of imperfect minimization algorithms in Broyden's β-class, Math. Prog., 9, 313– 335. Swann, W. H. (1972). Direct search methods, in Numerical Methods for Unconstrained Optimization (Ed. W Murray), Academic Press, London. Swann, W. H. (1974). Constrained optimization by direct search, in Numerical Methods for Constrained Optimization (Eds P. E Gill and W Murray), Academic Press, London. Tapia, R. (1984). On the characterization of Q-superlinear convergence of quasi-Newton methods for constrained optimization, Rice Univ. Dept. of Math. Sci. Report 84–2. Toint, Ph.L. (1977). On sparse and symmetric updating subject to a linear equation, Maths. Comp., 31, 954– 961. Varga, R. S. (1962). Matrix Iterative Analysis, Prentice-Hall, Englewood Cliffs, N.J. Walsh, G. R. (1975). Methods of Optimization, John Wiley, London. Watson, G. A. (1978). A class of programming problems whose objective function contains a norm, J. Approx. Theo., 23, 401– 411. Watson, G. A. (1979). The minimax solution of an overdetermined system of nonlinear equations, J. Inst. Maths. Applns., 23, 167– 180. Wilkinson, J. H. (1965). The Algebraic Eigenvalue Problem, Oxford Univ. Press, Oxford. Wilkinson, J. H. (1977). Some recent advances in numerical linear algebra, in The State of the Art in Numerical Analysis (Ed. D. A. H. Jacobs), Academic Press, London. Wilson, R. B. (1963). A simplicial algorithm for concave programming, Ph.D. dissertation, Harvard Univ. Graduate School of Business Administration. Winfield, D. (1973). Function minimization by interpolation in a data table, J. Inst. Maths. Applns., 12, 339– 348. Wolfe, P. (1959). The secant method for simultaneous nonlinear equations, Comm. Ass. Comput. Mach., 2, 12– 13. Wolfe, P. (1961). A duality theorem for nonlinear programming, Quart. Appl. Math., 19, 239– 244. Wolfe, P. (1963a). Methods of nonlinear programming, in Recent Advances in Mathematical Programming (Eds R. L Graves and P Wolfe), McGraw-Hill, New York. Wolfe, P. (1963b). A technique for resolving degeneracy in linear programming, J. SIAM, 11, 205– 211. Wolfe, P. (1965). The composite simplex algorithm, SIAM Rev., 1, 42– 54. Wolfe, P. (1967). Methods of nonlinear programming, in Nonlinear Programming (Ed. J Abadie), North-Holland, Amsterdam. Wolfe, P. (1968a). Another variable metric method, working paper. Wolfe, P. (1968b). Convergence conditions for ascent methods, SIAM Rev., 11, 226– 235. Wolfe, P. (1972). On the convergence of gradient methods under constraint, IBM J. Res. and Dev, 16, 407– 411. Wolfe, P. (1975). A method of conjugate subgradients, in Nondifferentiable Optimization (Eds M. L Balinski and P Wolfe), Mathematical Programming Study 3, North-Holland, Amsterdam. Wolfe, P. (1980). The ellipsoid algorithm, Optima (Math. Prog. Soc. newsletter), Number 1. Womersley, R. S. (1978). An approach to nondifferentiable optimization, M.Sc. thesis, Univ. of Dundee Dept. of Mathematics. Womersley, R. S. (1981). Numerical methods for structured problems in nonsmooth optimization, Ph.D. thesis, Univ. of Dundee Dept. of Mathematics. Womersley, R. S. (1982). Optimality conditions for piecewise smooth functions, in Nondifferential and Variational Techniques in Optimization (Eds D. C Sorensen and R. J.-B Wets), Mathematical Programming Study 17, North-Holland, Amsterdam. Womersley, R. S. (1984a). Censored discrete linear l1 approximation, ANU Dept. of Math. Sci. Res. Report No. 8. Womersley, R. S. (1984b). Minimizing nonsmooth composite functions, ANU Dept. of Math. Sci. Res. Report No. 13. Womersley, R. S. and Fletcher, R. (1986). An algorithm for composite nonsmooth optimization problems, J. Opt. Theo. Applns., 48, 493– 523. Yuan, Y. (1984). An example of only linear convergence of trust region algorithms for nonsmooth optimization, IMA J. Num. Anal, 4, 327– 335. Yuan, Y. (1985a). Conditions for convergence of trust region algorithms for nonsmooth optimization, Math. Prog., 31, 220– 228. Yuan, Y. (1985b). On the superlinear convergence of a trust region algorithm for nonsmooth optimization, Math. Proq., 31, 269– 285. Zoutendijk, G. (1960). Methods of Feasible Directions, Elsevier, Amsterdam. Practical Methods of Optimization, Second Edition ReferencesRelatedInformation" @default.
• W4248068199 created "2022-05-12" @default.
• W4248068199 date "2000-05-23" @default.
• W4248068199 modified "2023-10-17" @default.
• W4248068199 title "References" @default.
• W4248068199 cites W1538222621 @default.
• W4248068199 cites W1573176827 @default.
• W4248068199 cites W1579664581 @default.
• W4248068199 cites W1592546441 @default.
• W4248068199 cites W1963750522 @default.
• W4248068199 cites W1966539012 @default.
• W4248068199 cites W1968557508 @default.
• W4248068199 cites W1969468690 @default.
• W4248068199 cites W1969991107 @default.
• W4248068199 cites W1971258090 @default.
• W4248068199 cites W1973060629 @default.
• W4248068199 cites W1973589970 @default.
• W4248068199 cites W1974531281 @default.
• W4248068199 cites W1975326300 @default.
• W4248068199 cites W1977822596 @default.
• W4248068199 cites W1978245827 @default.
• W4248068199 cites W1978275959 @default.
• W4248068199 cites W1979089199 @default.
• W4248068199 cites W1979154120 @default.
• W4248068199 cites W1979968848 @default.
• W4248068199 cites W1982263471 @default.
• W4248068199 cites W1984488466 @default.
• W4248068199 cites W1986947514 @default.
• W4248068199 cites W1987556593 @default.
• W4248068199 cites W1988849934 @default.
• W4248068199 cites W1991976020 @default.
• W4248068199 cites W1992499818 @default.
• W4248068199 cites W1993553082 @default.
• W4248068199 cites W1994038822 @default.
• W4248068199 cites W1994482884 @default.
• W4248068199 cites W1994707765 @default.
• W4248068199 cites W1995393679 @default.
• W4248068199 cites W2000058639 @default.
• W4248068199 cites W2000863540 @default.
• W4248068199 cites W2000957563 @default.
• W4248068199 cites W2001116826 @default.
• W4248068199 cites W2002052347 @default.
• W4248068199 cites W2003140178 @default.
• W4248068199 cites W2004709509 @default.
• W4248068199 cites W2004936497 @default.
• W4248068199 cites W2005136695 @default.
• W4248068199 cites W2005411772 @default.
• W4248068199 cites W2011540169 @default.
• W4248068199 cites W2011959190 @default.
• W4248068199 cites W2011986332 @default.
• W4248068199 cites W2012231377 @default.
• W4248068199 cites W2013434059 @default.
• W4248068199 cites W2013615502 @default.
• W4248068199 cites W2014569127 @default.
• W4248068199 cites W2016859302 @default.
• W4248068199 cites W2017035316 @default.
• W4248068199 cites W2017659682 @default.
• W4248068199 cites W2022772618 @default.
• W4248068199 cites W2024490641 @default.
• W4248068199 cites W2024585637 @default.
• W4248068199 cites W2026393324 @default.
• W4248068199 cites W2027138954 @default.
• W4248068199 cites W2027929240 @default.
• W4248068199 cites W2031390949 @default.
• W4248068199 cites W2032083269 @default.
• W4248068199 cites W2032087206 @default.
• W4248068199 cites W2032266076 @default.
• W4248068199 cites W2034027216 @default.
• W4248068199 cites W2035079355 @default.
• W4248068199 cites W2035910846 @default.
• W4248068199 cites W2036050714 @default.
• W4248068199 cites W2037091587 @default.
• W4248068199 cites W2037909160 @default.
• W4248068199 cites W2038713466 @default.
• W4248068199 cites W2039665200 @default.
• W4248068199 cites W2040419583 @default.
• W4248068199 cites W2041285248 @default.
• W4248068199 cites W2043170821 @default.
• W4248068199 cites W2043382734 @default.
• W4248068199 cites W2045292585 @default.
• W4248068199 cites W2045961034 @default.
• W4248068199 cites W2046915036 @default.
• W4248068199 cites W2050786966 @default.
• W4248068199 cites W2050850334 @default.
• W4248068199 cites W2051237474 @default.
• W4248068199 cites W2051598083 @default.
• W4248068199 cites W2052204734 @default.
• W4248068199 cites W2053661713 @default.
• W4248068199 cites W2053732480 @default.
• W4248068199 cites W2053940560 @default.
• W4248068199 cites W2053983779 @default.
• W4248068199 cites W2056673717 @default.
• W4248068199 cites W2057448751 @default.
• W4248068199 cites W2057624533 @default.
• W4248068199 cites W2057875970 @default.
• W4248068199 cites W2059899863 @default.
• W4248068199 cites W2060206997 @default.
• W4248068199 cites W2063767894 @default.

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