Nash - Compact Numerical Methods for Computers (523163), страница 57
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36 638-50HEALY M J R 1968 Triangular decomposition of a symmetric matrix (algorithm AS6) Appl Srat. 17 195- 7HENRICI P 1964 Elements of Numerical Analysis (New York: Wiley)HESTENES M R 1958 Inversion of matrices by biorthogonahzation and related results J. Soc. Ind. Appl.Math. 5 51-90---1975 Pseudoinverses and conjugate gradients Commun. ACM 18 40-3HESTENES M R and STIFFEL E 1952 Methods of conjugate gradients for solving linear systems J. Res. Nat.Bur.
Stand. 49 409-36HILLSTROM K E 1976 A simulation test approach to the evaluation and comparison of unconstrainednonlinear optimization algorithms Argonne National Laboratory Report ANL-76-20HOCK W and SCHITTKOWSKI K 1981 Test examples for nonlinear programming codes Lecture Notes inEconomics and Mathematical Systems 187 (Berlin: Springer)HOLT J N and FLETCHER R 1979 An algorithm for constrained nonlinear least squares J. Inst. MathsApplics 23 449-63HOOKE R and JEEVES T A 1961 ‘Direct Search’ solution of numerical and statistical problems J.
ACM 8212-29JACOBI C G J 1846 Uber ein leichtes Verfahren. die in der Theorie der Sakularstorungen vorkommendenGleichungen numerisch aufzulosen Crelle's J. 30 51-94JACOBY S L S. KOWALIK J S and PIZZO J T 1972 Iterative Methods for Nonlinear Optimization Problems(Englewood Cliff‘s, NJ: Prentice Hall)JENKINS M A and TRAUB J F 1975 Principles for testing polynomial zero-finding programs ACM Trans.Math. Softw.
1 26-34JONES A 1970 Spiral a new algorithm for non-linear parameter estimation using least squares Comput. J.13 301-8KAHANER D, MOLER C and NASH S G 1989 Numerical Analysis and Software (Englewood Cliffs. NJ:Prentice Hall)KAHANER D and PARLETT B N 1976 How far should you go with the Lanczos process’! Sparse MatrixComputations eds J R Bunch and D J Rose (New York: Academic) pp 131-44KAISER H F 1972 The JK method: a procedure for finding the eigenvectors and eigenvalues of a realsymmetric matrix Comput. J. 15 271-3KARMARKAR N 1984 A new polynomial time algorithm for linear programming Combinatorica 4 373-95KARPINSKI R 1985 PARANOIA: a floating-point benchmark Byte 10(2) 223-35 (February)KAUFMAN L 1975 A variable projection method for solving separable nonlinear least squares problemsBIT 15 49-57KENDALL M G 1973 Time-series (London: Griffin)KENDALL M G and STEWART A 1958-66 The Advanced Theory of Statistics vols 1-3 (London: Griffin)KENNEDY W J Jr and GENTLE J E 1980 Statistical Computing (New York: Marcel Dekker)KERNIGHAN B W and PLAUGER P J 1974 The Elements of Programming Style (New York: McGraw-Hill)KIRKPATRICK S, GELATT C D Jr and VECCHI M P 1983 Optimization by simulated annealing Science 220(4598) 671-80KOWALIK J and OSBORNE M R 1968 Methods for Unconstrained Optimization Problems (New York:American Elsevier)KUESTER J L and MIZE H H 1973 Optimization Techniques with FORTRAN (New York London Toronto:McGraw-Hill)KUI.ISCH U 1987 Pascal SC: A Pascal extension for scientific computation (Stuttgart: B G Teubner andChichester: Wiley)LANCZOS C 1956 Applied Analysis (Englewood Cliffs.
NJ: Prentice Hall)LAWSON C L and HANSON R J 1974 Solving Least Squares Problems (Englewood Cliffs, NJ: Prentice Hall)LEVENBERG K 1944 A method for the solution of certain non-linear problems in least squares Q. Appl.Math. 2 164-8Bibliography267LOOTSMA F A (ed.) 1972 Numerical Methods for Non-Linear Optimization (London/New York: Academic)MAINDONALD J H 1984 Statistical Computation (New York: Wiley)MALCOLM M A 1972 Algorithms to reveal properties of floating-point arithmetic Commun. ACM 15949-51MARQUARDT D W 1963 An algorithm for least-squares estimation of nonlinear parameters J.
SIAM 11431-41---1970 Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimationTechnometrics 12 59l-612MCKEOWN J J 1973 A comparison of methods for solving nonlinear parameter estimation problemsIdentification & System Parameter Estimation, Proc. 3rd IFAC Symp.
ed. P Eykhoff (The Hague: Delft)pp 12-15-- 1974 Specialised versus general purpose algorithms for minimising functions that are sums of squaredterms Hatfield Polytechnic, Numerical Optimization Centre Technical Report No 50, Issue 2MEYER R R and ROTH P M 1972 Modified damped least squares: an algorithm for non-linear estimation J.Inst. Math.
Applic. 9 218-33MOLER C M and VAN LOAN C F 1978 Nineteen dubious ways to compute the exponential of a matrixSIAM Rev. 20 801-36MORÉ J J, GARBOW B S and HILLSTROM K E 1981 Testing unconstrained optimization software ACMTrans. Math. Softw. 7 17-41MOSTOW G D and SAMPSON J H 1969 Linear Algebra (New York: McGraw-Hill)MURRAY W (ed.) 1972 Numerical Methods for Unconstrained Optimization (London: Academic)NASH J C 1974 The Hermitian matrix eigenproblem HX=eSx using compact array storage Comput.Phys.
Commun. 8 85-94---1975 A one-sided transformation method for the singular value decomposition and algebraiceigenproblem Comput. J. 18 74-6---1976 An Annotated Bibliography on Methods for Nonlinear Least Squares Problems Including TestProblems (microfiche) (Ottawa: Nash Information Services)---1977 Minimizing a nonlinear sum of squares function on a small computer J. Inst. Maths Applics 19231-7---1979a Compact Numerical Methods for Computers: Linear Algebra and Function Minimisation(Bristol: Hilger and New York: Halsted)---1979b Accuracy of least squares computer programs: another reminder: comment Am.
J. Ag. Econ.61 703-9---1980 Problémes mathématiques soulevés par les modéles économiques Can. J. Ag. Econ. 28 51-7---1981 Nonlinear estimation using a microcomputer Computer Science and Statistics: Proceedings ofthe 13th Symposium on the Interface ed. W F Eddy (New York: Springer) pp 363-6---1984a Effective Scientific Problem Solving with Small Computers (Reston, VA: Reston Publishing) (allrights now held by J C Nash)---1984b LEQB05: User Guide - A Very Small Linear Algorithm Package (Ottawa, Ont.: NashInformation Services Inc.)---1985 Design and implementation of a very small linear algebra program package Commun.
ACM 2889-94---1986a Review: IMSL MATH/PC-LIBRARY Am. Stat. 40 301-3---1986b Review: IMSL STAT/PC-LIBRARY Am. Stat. 40 303-6---1986c Microcomputers, standards, and engineering calculations Proc. 5th Canadian Conf. Engineering Education, Univ. of Western Ontario, May 12-13, 1986 pp 302-16NASH J C and LEFKOVITCH L P 1976 Principal components and regression by singular value decompositionon a small computer Appl. Stat. 25 210-16---1977 Programs for Sequentially Updated Principal Components and Regression by Singular ValueDecomposition (Ottawa: Nash Information Services)NASH J C and NASH S G 1977 Conjugate gradient methods for solving algebraic eigenproblems Proc.Symp. Minicomputers and Large Scale Computation, Montreal ed.
P Lykos (New York: AmericanChemical Society) pp 24-32---1988 Compact algorithms for function minimisation Asia-Pacific J. Op. Res. 5 173-92NASH J C and SHLIEN S 1987 Simple algorithms for the partial singular value decomposition Comput. J. 30268-75268Compact numerical methods for computersNASH J C and TEETER N J 1975 Building models: an example from the Canadian dairy industry Can. Farm.Econ. 10 17-24NASH J C and WALKER-SMITH M 1986 Using compact and portable function minimization codes inforecasting applications INFOR 24 158-68-- 1987 Nonlinear Parameter Estimation, an Integrated System in Basic (New York: Marcel Dekker)NASH J C and WANG R L C 1986 Algorithm 645 Subroutines for testing programs that compute thegeneralized inverse of a matrix ACM Trans.
Math. Softw. 12 274-7N ASH S G 1982 Truncated-Newton methods Report No STAN-CS-82-906 (Stanford, CA: Dept ofComputer Science, Stanford Univ.)---1983 Truncated-Newton methods for large-scale function minimization Applications of NonlinearProgramming to Optimization and Control ed. H E Rauch (Oxford: Pergamon) pp 91-100---1984 Newton-type minimization via the Lanczos method SIAM J. Numer. Anal. 21 770-88---1985a Preconditioning of truncated-Newton methods SIAM J.
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