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Literaturverzeichnis 1. Alizadeh , F. (1991) : A sublinear-time r andomi zed parallel algorithm for the m aximum clique problem in perfect gr aph s. Pro ceedings of the second ACM- SIAM Symposium on Discrete Algorithms 2. Aliz ad eh , F . (1995): Interior point me thods in semidefinite progr ammin g wi th applicat ions to combinat orial optimi za tion . SIAM J. Opt ., 5(1) , 13-51 3. Alizadeh, F., Haeberly, J .-P.A., Over ton , M.L. (1994): A new primal -dual int erior-point method for semidefinite programming . In J .G . Lewis, ed., Pro c. Fifth SIAM Conf. on Applied Linear Algebra, SIAM, Ph iladelphia, 113-117 4. Allgower, E.L., Georg, K (1990) : Numerical Con tinu ation Methods, Springer Serie s in Compu ta tion al Mat he mat ics 13, Springer, Berlin 5. Andersen , E.D., Ye, Y.Y. (1996) : A compu tat ional study of the homogeneous algorithm for large-scale convex op timiza tion . Publica tions from Depar tmen t of Management no. 3/ 1996, Odense University, Denmark 6. Ando , T. (1979) : Con cavi ty of cert ain maps and positive definite matrices and applications to Hadamard produ cts. Linear Algebra Appl., 26, 203-241 7. An strei cher, K (1996) : Large Step Volumetri e Potenti al Redu ction Algo- rithms for Linear Progr amming. An nals of Oper . Res. 62, 521-538 8. Barnes, KR. , Hoffm an , A .J . (1984) : P arti tioning , spect ra and linear pro- gramming. Progress in Combin atorial Optimization, R.vV. Pull eyb lank ed., Academic Press, 13-2 5. 9. Ben -Tal, A., Bendsoe, M.P. (1993): A new me thod for op timal tr uss topology de sign . SIA M J . Opt ., 3, 322-358 10. Ben -Tal, A., Nemirovski, A. (1998) : On pol yhedr al approximations of the second-order cone. Research Report Nr. 3/98 , Optimi za tion Labor atory, Fa- cu lty of Ind istrial Engineering and Man agement, Technion - Israel Insti tu te of Technology, Teclmion City, Haifa 32000, Israel, to appear in MOR 11. Björck, A. (1996) : Numerical Me thods for Least Squares Probl ems. SIAM, Phil adelphia 12. Blum, K, Oet tli , VV . (1975) : Mathe matische Optimi erung : Grundlagen und Verfahren. Springer, Berlin 13. Boggs, P.T., Tolle, J .W. (1996) : Sequential Qu adr ati c Programming . Acta Numerica, 4, 1- 51 14. Bonnans, J.F., Gonzaga, C.C. (1994): Convergence of interior-point algo- ri thms for the mono ton e linear compleme ntar ity probl ern. Techn ical Repo rt , INRIA , Rocquencourt, France 15. Borgwa rd t, KH . (2001) : Op timi erung, Op erations Research und Spiel theorie , Bi rkh äuser-Verlag 16. Boyd , S., EI Ghaoui , L., Feron, E., Balakri shn an, V. (1994) : Linear Matrix In equ alities in System and Control Theory. SIAM, Phil adelphia
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Literaturverzeichnis

1. Alizadeh , F . (1991) : A sublinear-t ime randomized par allel algorit hm for t hemaximum clique problem in perfect graphs. Proceedings of t he second ACM­SIAM Symposium on Discrete Algorithms

2. Alizad eh , F . (1995): In terior point methods in semidefinite programming withapplications to combinatorial optimization . SIAM J . Opt., 5(1) , 13-51

3. Alizad eh , F ., Haeberly, J .-P.A., Overton , M.L. (1994) : A new primal-dualinterior-point method for semidefinit e programming. In J .G . Lewis, ed ., Proc.Fift h SIA M Conf. on Applied Line ar Algebra , SIA M, Philadelphia , 113-117

4. Allgower , E .L., Georg, K (1990) : Numerical Con tinuation Methods, SpringerSerie s in Computational Mathematics 13, Springer , Berlin

5. Andersen , E.D., Ye, Y.Y. (1996) : A computat ional st udy of the homogeneousalgor it hm for large-scale convex op timiza tion. Publica tions from Departmen tof Management no . 3/ 1996, Od ense University, Denmark

6. Ando, T . (1979) : Con cavi ty of certain map s and posit ive definite matricesand applicat ions to Hadamard product s. Linear Algebra Appl. , 26 , 203-241

7. Anstreicher, K (1996) : Large Step Volumetrie Potential Reduction Algo­rithms for Linear Programming. Annals of Oper. Res. 62 , 521-538

8. Barnes, KR. , Hoffm an , A.J . (1984) : Partitioning, spect ra and linear pro­gramming . P rogress in Combinato rial Op timiza t ion , R .vV. Pulleyb lank ed .,Acad emi c P ress, 13-25.

9. Ben-Tal , A., Bendsoe, M.P. (1993) : A new method for op timal truss topologydesign . SIA M J . Opt., 3 , 322-358

10. Ben-Tal , A., Nemirovski , A. (1998) : On polyhedral approx imat ions of thesecond-order cone. Research Report Nr. 3/98 , Optimization Laboratory, Fa­culty of Indist rial En gineering and Managemen t , Technion - Israel Insti tu teof Technology, Teclmion City , Haifa 32000, Israel , to appear in MOR

11. Bj örck , A. (1996) : Numerical Methods for Least Squares Problems. SIAM,Philad elphia

12. Blum, K , Oet tli , VV. (1975) : Mat hemat ische Optimierung: Grundlagen undVerfahren . Sprin ger , Berlin

13. Boggs, P.T ., Tolle , J .W. (1996) : Sequenti al Qu adratic Programming. ActaNumerica, 4 , 1- 51

14. Bonnan s, J .F ., Gonzaga , C.C. (1994) : Convergence of interior-point algo­ri thms for the monotone linear compleme ntarity probl ern . Techn ical Repo rt ,INRIA, Ro cqu encourt , Fran ce

15. Borgwardt , KH. (2001) : Op timierung, Op erations Research und Spiel theorie,Birkhäuser-Verlag

16. Boyd, S., EI Ghaoui, L., Feron , E., Bal ak rishnan , V. (1994) : Linear MatrixInequali t ies in System and Control Theory. SIAM, Philad elphia

464 Lite raturverzeichn is

17. Brent , R . (1973) : Algorithms for minimization without derivatives. PrenticeHa ll

18. Collatz , L., Wetterling, W . (1971) : Optimierungsaufgaben, 2. Aufl . Springer,Berli n (Heidelberger Taschenbücher; 15)

19. Conn, A.R ., Gou ld , N., Sartenaer, A., Toint, P.L., (1996) : Convergence pro ­pert ies of an augmented lagrangian algorithm for optimization wit h a comb i­nat ion of general equality and non linear const raints. SIAM J . Opt., 6, 674-703

20. Conn A.R ., Gou ld N.1.M., Toint Ph.L., (1991) : A globally convergent aug­mented Lagrangian algorithm for optimization with general constraints andsimp le bounds. SIAM J . Numerical Anal. , 28 , 545-572

21. Conn, A.R ., Gou ld , N.1.M., Toint , P.L. (1992) : LANCELOT: a Fortran packa­ge for large-scale non linear optimization (Re lease A) . Computationa l Mathe­matics, Springer, Berl in

22. Cook , \V.J. , Cunningham, W .H., Pulleyblank, \V.R. , Schrijver , A. (1998) :Combinatorial Optimization , John Wi ley, New York

23. Correa , R ., Ramirez C., H. (2002) : Aglobai algorit hm for nonlinear semide­finite programming. Research Report 4672, INRIA, Rocquencourt , France

24. Dantzig, G.B. (1966) : Lineare Programmieru ng und Erweiterungen . Springer,Berlin

25. den Hertog, D., Jarre, F ., Roos, C., Terl aky , T . (1995) : A Sufficient Conditionfor Self-Concordance, with Application to Some Classes of Structured ConvexProgramming Prob lems . Math. Prog., Series B, 69(1), 75-88

26. den Hertog, D., Roos , C. (1989) : A survey of search directions in int erior-pointme thods for linear programming. Report 89-65, Delft Univ ersity of Techno ­logy, The Netherlands

27. Deuflhard, P., Hohmann, A., (1993) : Numerische Mathematik I, 2., üb erar­beitete Auflage. Waltor de Gruyter , Berlin, New York

28. Dieudonne, J . (1960) : Foundations ofModern Analysis , VolL Academic Press,New York , London

29. Donath , W .E., Hoffman, A.J . (1973) : Lower bo unds for t he partitioning ofgraphs . IBM Jo urnal of Research and Deve lopment 17 (5) , 420-425

30. Fares, B., Ap karian , P., Noll, D. (2001) : An Augmented Lagrangian Met hodfor a Class of LMI-Constrained Problems in Robust Control Theory. Inter­nat ional .Journ al of Control, 74 (4) , 348-360

31. Fares, B., Noll, D., Apkarian, P. (2002) : Robust Control via Sequential Semi­definite P rogramming . SIAM .Journ al on Control and Optimizat ion . 40 (6) ,1791-1820

32. Fiacco, A.V., McCormick, G.P . (1968) : Nonlinear Programming: SequentialUnconstrained Minimization Techniques . W iley, New York

33. F letcher , R . (1980) : Unconstrained optimization. Addison Wesley34. F letcher , R . (1981) : Constrained optimization. Addison Wesley35. F let cher , R . (1987) : P ractical methods of optim ization , 2n d ed itio n. Jo hn

\Viley, Chichester36. F letcher , R ., Leyffer , S. (1997) : Non linear programming without a penalty

function . Numerical Analysis Report NA /l71 University of Dundee, Dundee,UK , rev ised 2000

37. F letcher , R ., Leyffer , S., Toint , P. (2000) : On the global convergence of a filt er­SQP algorit hm. Numerica l Analysis Report NA /197 University of Dundee ,Dundee, UK

Literaturverzeichnis 465

38. Forsgren, A. (2000) : Op timali ty condit ions for non convex semidefinit e pro­gramming. Math . Prog., Serie s A, 88, 105-128.

39. Freund , R.M ., Epelman , M., (2000): Condit ion Number Complexi ty of anElementar y Aigorithm for Comput ing a Reli able Solu tion of a Conic Line arSystem . Ma th. P rog., Series A, 88 (3) , 451- 485.

40. Freund , R.\V. (2003): Optimal pump control of broadband Raman amplifyersvia linear programming. Manuscript , Lucent Bell Laboratories, Murray Hili ,NJ , USA

41. Freund , R.\V. , Jarr e, F . (1997): A QMR-Based In terior-Point Aigori thm forSolving Linear Programs. Math . Prog., Series B, 76 , 183-210

42. Freund , R.\V. , J arr e, F . (2001) : Solvin g the Sum-of-rat ios problem by anInterior-Point Met hod . J . of Global Opt ., 19 , 83-102

43. Freund, R .W ., J arre, F . (2000) : An Extension of t he Positi ve Re al Lemmato Descripto r Systems. Report 00/ 3-09, Scientific Computing In terest Group,Bell Lab s, Lucent Technologies

44. Fujie, T ., Kojima , M. (1997) : Semidefinite programming relaxation for non­convex qu ad rati c programs. Journal of Global Op t. , 10, 367-380

45. Gar ey, M.R. , Johnson , D .S. (1979) : Computers and Intract ability : A Guideto t he T heory of N P -Complet eness . Freeman , San Fran cisco

46. Gass, S.l. (1975) : Lin ear Programming, Methods and Applications. McGraw­Hili , New York

47. Geiger , C., Kan zow, C. (1999): Numerische Verfahren zur Loesung unrestrin­gierter Minimierungsaufgab en. Sprin ger , Berlin

48. Geiger , C., Kanzow, C. (1999) : T heorie und Numerik restring ierter Op timie­rungsaufgaben . Springer, Berlin

49. GilI, P. , Murray, W. ,Wright, M. (1981): P ract ical Optimiza tion. Acad em icP ress

50. Glineur, F . (2001): Computational experiments with a linear approximationof second-order cone optimiza tion. Techn ical Report 0001, Faculte Polytech­nique de Mons , Frankreich .

51. Goeman s, M.X. Willi amson , D.P . (1995): Improved Approximation AIgo­rithms for Maximum Cut and Satisfiability P roblem s Using Semidefini te Pro­gramming. J . ACM, 42, 1115-1145

52. Goldfarb , D., Idnani , A. (1983) : A numerical st able du al method for solvingstrict ly convex quadra t ic programs. Ma th. P rogr ., 27 , 1- 33

53. Goldman , A.J ., Tucker , A.W . (1956) : Theory of Line ar Programming, H.\V.Kulm und A.\V. Tu cker cds, Linear Inequ ali t ies and relat ed Systems, Armalsof Mathematical Studies, 38 , 53- 97 North-Holland , Amsterdam

54. Golub, G.H., Van Loan , C.F . (1989) Matrix comput at ions . Baltimore55. Gondzio, J ., Terlak y, T . (1994): A computationa l view of interior-point me­

thods for linear programming. Repo rt 94-73, Delft University of Technology,T he Netherlands

56. Gonzaga , C., Karas, E ., Van ti, M. (2002): A globally convergent filter methodfor nonlinear programming. Technical Report , Dept of Mathematics, Univ. ofSanta Ca tarina, Flo rianopolis, Br asilien

57. Großmann, C., Terno, .1. (1993): Numerik der Optimierung. Teubner, Stutt­gar t

58. Grät schei , M., Lovasz, L., Schrijver, A. (1988) : Geometrie Aigorit hms andCombinatorial Optimization. Springer Verlag , Berlin

466 Literaturverzeichnis

59. Halicka , M., de Klerk, E ., Roos, C. (2002) : On t he Convergence of t he Cent ralPath in Semidefinite Op timization. SIA M Journal on Op timization 12 (4) ,1090-1099

60. Hast ad , J . (2001) : Some optimal inapproximability resul t s. Proc. of t he 29thACM Syrnp. on Theory Comput. Journal of ACM, 48, 798-859.

61. Helmberg, C., Rendl , F ., Wolkowicz, H., Vanderbei, R .J . (1996): An interior­point method for semidefinit e programming. SIAM J . Opt . 6 (2) , 342-361

62. Hiriar t-Urruty, J .-B. , Lem arechal , C . (1991): Convex Analys is and Minimi­zat ion Algori thms 1. Springer-Verlag, Berlin-Heidelb erg-New York.

63. Horn , R.A ., Johnson , C.R . (1985) : Matrix An alysis. Universit y Press, Cam­bridge

64. Hor st , R. , Pardalos, P.M., (eds.) (1995) : Handbook of Global Op timization .Kluwer, Dordrecht

65. Hu ard , P. , Lieu , B.T . (1966): La methode des centres dans un espace topolo­gique. Numerische Mat hemat ik, 8 , 56-67

66. B. Hupper t , B. (1990): An gewandte Lineare Algebr a. De Gru yter Verlag67. J arr e, F . (1992): In terior-point methods for convex programming. Appliod

Math . and Op t . 26 , 287-31168. Jarre, F . (1994) : Interior-point methods via self-concordance or relative Lip­

schit z condit ion , Habiliti ationsschrift . Univers ität Würzburg69. J arr e, F . (1996) : In terior-point methods for convex programs. in T . Terlaky

ed .: Interior-Point Met hods of Mat hemat ica l Programming. Kluwer , Dord­recht

70. J arr e, F ., Kocvara , M., Zowe, J . (1998) : Op timal Tr uss Design by In terior­Point Methods. SIAM J . Opt . 8(4) , 1084-1107

71. J arr e, F ., Wechs, M. (1997) : Extending Mehrotra 's Corr ecto r for LinearP rograms. Report # 219, Inst itu t für An gewandte Mathem atik, Universit ätWürzburg, http:/ /www.opt.uni-duesseldorf.de ;-jarre/en/report-fs.html

72. Kanto rovi ch , L.W ., Akilow, G.P., (1964) : Funktionalanalysis in normier tenRäumen. Akademie-Verlag, Berlin

73. Kan torovich , L.W ., (1948) : Funktionalan alysis und angewandte Mathematik.Usp echi Mathem . Nauk, 3 , 6 (28) (ru ssisch) .

74. Karmarkar , N. (1984) : A new polynomial-time algorit hm for linear program­ming. Combinatorica , 4 , 373-395

75. Kelley, C .T ., (1999): Detect ion and reme diation of st agnation in t he Nelder­Mead algorit hm using a sufficient decre ase condit ion . SIAM J . Opt ., 10, 43­55.

76. Kh achiyan , L.G. (1979): A polynomial algor it hm in linear programming. So­viet Mathem atics Doklad y, 20 , 191-194

77. Klee, V. , Minty, G.J . (1972): How good is the simplex algor it hm? In : Shisha ,O. (ed) Inequalities. Acad em ic P ress, New York 159-1 75

78. Knobloch , H.W ., Kappei , F . (1974) : Gewöhnliche Differentialgleichungen.Teubner Verlag, Stut tgar t

79. Kocvara , M., Stingl, M. (2001) Au gmen ted Lagran gian Method for Semidefi­nite Programming. Repo rt , Insti tute of Applied Mathematics, Univers ity ofErlangen-Nürnberg

80. Koehl er , J. R . and Owen , A. B. (1996): Computer experiments. In Ghosh , S.and Rao , C. R ., editors, Handbook of Stati sti cs, Volume 13, 261-308. ElsevierScien ce, New York

Literaturverzeichnis 467

81. Kolmogorov, A.N., Fomin, S.V., (1975) : Reelle Funktionen und Funktional­analysis. VEB Deutscher Verlag der Wi ssen schaften, Berlin

82. Kojima, M., Mizuno, S., Yoshi se, A. (1989) : A primal-dual interior-point algo­rithm for line ar programming. In : Megiddo, N. (ed) Progress in Mathem aticalP rogramming: In terior-Point and Rela ted Methods, 29-47. Sprin ger Verlag,New York

83. Koj ima, M., Sh indoh, S. , Har a , S. (1997): In terior-point methods for the mo­notone semidefinite linear complementarity problem in symmet ric matrices.SIAM J . Op tim. 7 (1) , 86-125

84. Lagari as, J .C ., Reeds, J .A ., Wright , M.H., Wri ght P.E . (1998) : Convergencepropertie s of the Nelder-Mead sim plex method in low dimension s. SIA M J .Op t .9 (1) , 112-147

85. Leibfri tz , F . (2001) : A LMI-based algor ithrn for designing suboptimal static/ output feedback controllers . SIAM J . Contr. Op t. , 39 (6) , 1711-1735

86. Lovasz, L., Schrijver , A. (1991): Con es of Matrices and Setfun ct ions, and 0-1Op tirniza t ion. SIA M J . Opt., 1 (2)

87. Lovasz, L. (1979) On the Shannon capacity of a graph. IE EE Tr ansactionson Info rrn a tion Theory 25 1-7

88. Luenberger , D.G . (1973) : In troduction to line ar and nonlinear programming.Addison Wesley

89. Luo , Z.-Q ., Sturm, J .F ., and Zhang, S. (2000) : Conic convex programmingand self-dua l embedding. Optimization Methods and Software , 14 169-218

90. Lustig, LJ ., Marsten , R. E., Shanno, D.F . (1992) : On implementing Mehrot ra'spredictor-corrector int erior-point method for line ar programming. SIAM J .Op tim . 2 435-449.

91. Lyapunov, A.M. (1949) : The general problem of st ability of motion . Ann.math. st udies, 11. P rinceton (auf Russisch: Moskau 1935)

92. Maratos, N. (1978) : Exact penalty function algorit hms for finite dimension aland control op timiza t ion algori thms. Ph.D. T hesis , Imperial College, London

93. Mehrot ra, S. (1992) . On t he implement ation of a primal-dual interior-pointmethod. SIA M J. Op tim., 2 575-601

94. Monteiro, R .D.C., Zhang, Y. (1998) : A unifi ed analysis for a d ass of long­step pr irnal-dual path-following interior-point algori thrns for sernidefinite pro­gramming. Math . Prog. Ser. A, 81 (3) , 281-299

95. More , J .J ., Toraldo, G. (1991): On the solut ion of quadra t ic programmingproblems with bound const raint s. SIA M J . on Op t . 1 , 93- 113

96. Neider, J.A. and Mea d , R. (1965): A simplex method for function minimiza­tion . Computer J ., 7 , 308-313

97. Nesterov, Y.E. (1998) : Semidefini te relaxation and non conv ex qu ad rat ic op­t imizat ion . Op tim. Meth. Software , 9, 141-160

98. Nesterov, J. E. , Nernirovsky A.S. (1988) : A general approach to polynornial­t ime algorit hms design for convex programming. Repo rt , Central Economicaland Ma thematical Insti tu te, USSR Acad. Sci. , Moscow, Russia

99. Nes terov, J .E. , Nemirovsky A.S. (1989): Self-concordan t functions and poly­nomial- time methods in convex programming. Report CEMI, USSR Academyof Sciences, Moscow

100. Nesterov, J .E. , Nemirovsky A.S. (1994) : Int erior Point Polynomial Methodsin Convex P rograrnming: Theory and Applica t ions. SIAM, Philad elphia

101. Nes terov Y.E. , Todd, M.J . (1997) : Self-scaled barri ers and interior-point me­thods for convex programming. Math. Op er. Res. 22 (1) , 1-42

468 Literaturverzeichnis

102. Nest erov Y.E ., Todd, M.J . (1998) : Primal-dual interior-point methods forself-scaled cones. SIAM J . Optim. 8 , 324-364

103. Nocedal, J. , Wr igh t , S.J. (1999): Numerical Op timization, Springer , Berlin104. Poljak, S., Rendl , F ., Wolkowicz, H. (1995) : A recipe for semidefinit e relaxa­

t ion for (O,l)-quad rat ic prograrnrn ing. J . of Global Op t. , 7 , 51- 73105. Helmberg. C., Rendl, F ., Wolkowi cz, H. , Vanderbei, R .J . (1996) : An interio r

point method for semidefinit e programming. SIAM .J. Op tim., 6, No. 2, pp .342-361

106. P iet rzy kowski, T . (1970) : The potential method for condit iona l maxima inthe locally compact metric spaces. Numer. Math ., 14, No. 4, pp . 325-329

107. Powell, M.J.D. (1978) : A fast algorithm for nonlinearl y constrained op tirniza­tion ca lculations . Lecture Notes in Mathematics 630, Springer-Verlag , Berlin,144-157

108. Powell, M.J .D. (1978): T he converge nce of variable rnetr ic methods for non­linearl y const ra ined optimzation calculations . In : O.L. Mangasarian , R.R.Meyer , S.M. Robinson eds, Nonlinear Programming, 3 . Aca demic Press, NewYork , 27-63.

109. Powell, M.J .D . (1984) : The perform an ce of two subroutines for const rainedop timizaton . In : P.T . Boggs, R.T. Byrd, R .B . Schnabel, eds, Numerica l Op­timization . SIAM Publications, Philadelphia.

110. Powell, M.J .D . (1998) : Direct sea rch algorithrns for op t irniza tion calculations .In : A. Iserl es ed , Act a Numerica . Cambridge Universit y Press, Cambridge,287-336

111. Ro ckafellar , R.T. (1970): Convex Analysis. P rin ceton University P ress, P rin­ceton , N.J .

112. Roos, C., Terlaky, T ., Vial , J.P. (1997): T heory and Algori thms for LinearOp timizat ion , An In t erior Point Approach . John Wil ey & Sons, Chichester

113. Roo s, C ., Vial , J .P . (1992) : A polynomial method of approximate centers fort he linear programrning problern. Ma Ul. P rog., 54 295- 306

114. Sacks, J ., Welch , W .J ., MicheIl , T .J ., Wynn, H.P. (1989) : Design and analysisof computer experiment s. St atisti cal Scien ce, 4 , 409-435

115. Scherer, C. (1999) : Lower bounds in mult i-object ive H 2 / H oo probl ems. P roc.38t h IEEE Conf. Decision and Con trol , Phoenix, Ari zona

116. Schit tkow ski , K. (1981): T he nonlinear programrning rnethod of Wil son , Han,and Powell with an au gm ented Lagrangian type line search fun ct ion , par ts 1and 2, Numer . Math . 38,83-127

117. Schittkowski, K. (1985/86) : NLP QL: A Fortran subr outine for solving cons­trained nonlinear prograrnrning problem s. Annals of Op erations Res ., 5 , 485­500

118. Schrijver , A. (1986): Theory of Linear and In teger P rograrnming. John Wil ey& Sons

119. Shapiro, A., Scheinberg, K. (2000) : Du ali ty and Op timali ty Condit ions . inH. Wolkowicz, R . Saigal , L. Vandenb erghe eds, Handbook of Semidefini teP rogramming: Theory, Algori thrns and Applica t ions, Kluwers In terna tionalSeries

120. Shor , N.Z. (1987) : Quadratic Op timization Problems Soviet Journal of Cir­cuits and Systems Sciences, 25 (6) , 1-11

121. Sonnevend, G. (1986) : An 'analyt ical cent re' for polyhedrons and new classesof glob al algorit hms for line ar (smooth , convex) programming. in : System

Literaturverzeichnis 469

Modelling and Optimizai ion (Budapest , 1985) , Lecture Notes in Control andInformation Scien ces, 84. Springer , Berlin, 866-875

122. Sonnevend, G. , Sto er , J . (1990) : Global ellipsoida l ap proximations and homo­topy methods for solving convex analyt ic programs. Appl. Math . and Op t .,21 , 139-165

123. Stern , R.J. , Wolkowi cz, H. (1995) : Indefin ite trust region subprobl ems andnon symmetric eigenvalue perturbations. SIA M J Op timization 5 (2) , 286­313

124. Sto er , J ., Buli rsch , R . (1991) : Numerische Mathematik 1 und 2. Springer ,Berlin

125. Sto er , J ., Wi t zgall , C. (1970) : Convexity and Op timization in F init e Dimen­sions . Grundleh ren der Mathematischen Wi ssenschaften 163, Sprin ger , Berlin

126. Sturm, J .F . (1999) : Using SeDuMi 1.02, a MATLAB toolbox for optimizationover symmetrie cones . Op tim . Methods Softw. 11 -12, 625-653

127. Todd, M.J ., (1999) : On search direct ions in interior-point methods for semi­definite programming. Optim. Met h . Softw . 11, 1-46

128. Todd, M.J ., Toh , K .C., Tiitüncii, R. R ., (1998) : On the Nesterov-Todd direc­tion in semidefinite programming, SIA M J. Op tim. 8 , 769-796

129. Tuan, H.D., Apkarian , P., Nakashima, Y. (2000): A New Lagran gian Du­al Global Op timization Algori thm for Solving Bilinear Matrix Inequalities.Internat . J . of Robust and Nonlinear Contr. , 10, 561-578

130. Ulbrieh, M., Ublrieh, S. , Vieen te, L.N. (2000) : A globally converge nt prim al­du al interior-point filt er method for nonlinear programming. Preprint 00-11 ,Dept . of Mat hematics, Univ . of Coimbra , Portugal , revised 2002

131. Vandenb erghe, L., Boyd , S. (1996) : Semidefini te P rogramming. SIAM Re­view , 38(1) , 49-95.

132. Vanderbei , R .J . (1997) : LOQO User 's Manua l - Version 3.10. Report SOR97-08 , P rin ceton Un iversity, P rinceton , NJ 08544,

133. Vanderbei , R ..J., Benson , H., Shanno, D. (2000) : Int erior-Point Methods forNon convex Nonlinear P rogramming : Filter Methods and Merit Functions.Report ORFE 00-06 , P rinceton Univers ity, P rinceton, NJ 08544

134. Web st er , R. (1994) : Convexity, Oxford Universit y Press135. Wolkowi cz, H., Saigal , R. , Vandenberghe , L. eds (2000) : Handbook of Semi­

defini te P rogramming, Theory, Algori thms, and Applications. Kluwer Boston136. Wright , S.J . (2001) : On the convergence of the Newtou /Iog-b arrier method.

Mat h . Prog. Series A, 90, 71-100.137. Wright , S.J ., J arr e, F ., (1998): The role of linear obj ective fun ctions in barri er

methods, Math . Prog. Series A, 84, 357-373 undhttp:/ /www-unix .mcs.anl. govrwright /pap ers/P485_corrections.ps

138. Ye, Y. , Todd , M.J ., Mizuno, S. (1994) : An O(ynL)-itera tion homogeneou sand self-dual linear programming algorit hm . Mathematics of Op erations Re­search, 19(1)

139. Y.-X. Yuan (1995) : On the convergence of a new tru st region algor it hm .Numer . Matll. 70 , 515- 539

Index

A-konjugiert , 149

Phase I der Simplexmethode, 44

Abl eitung- Frechetsche - , 164- Gäteaux'sche - , 164- höh ere - , 164Ab stiegsmethoden, 135, 139Accessibili ty Lemma, 213Adjazenzmatrix , 104affin invariant , 170, 362affine Hülle, 207ak tiv , 49- Ungleichung , 9, 29an alytisches Zen trum, 76, 358Armijo line search , 145Augmented Lagr angian , 299Au sgleichsproblem- nichtlineares, 184

Barri erefunktion , 76, 316Barrieremethode, 318Barrieremethoden, 315Basis, 23- zulässige, 25Basislösung, 25Basisvari able, 23Baum, 107Bedingung 1. Ordnung- (notwendige -) , 249Bedingung 2.0rdnung- hinreichende - , 260- notwendige - , 258BFGS-Verfahren, 180bip artit- -er Gr aph, 428box-constraints, 273

Broydensche ß-Klasse, 182

Cau chy-Schwarz 'sehe Un gleichung- verallgem einer te, 396- verallgem einer te - , 367cg-Verfah ren , 148, 150Cholesky-Zerlegung, 78, 152chromat ische Zahl- eines Gr aphen , 427Clique- maximale, 427cons traint qu alifica tion- 2. Ordnung, 260- Fritz John , 229- LICQ, 253- von Slater , 228CPM , 121

Dantzig- Methode von , 117DFP-Verfahren , 181Dijkstra- Methode von , 119Direkte Suchverfahren , 129direk tes Verfahren , 152Diätprobl em , 10Dr eieckszerlegungsm ethode, 47dual- - er Simplexschri t t , 55- zulässige Basi s, 55dualer Kegel, 216Du alität- - ssa tz der linear en Op timierung, 52,

54bei konvexen P rogrammen inkoni scher Form, 235

Du alität slücke , 75dünn besetzt , 13, 92

472 Index

Ecke, 19Eigenwertoptimierung, 419Ellipse- äußere - , 371, 375- Innere, 364entartet , 29Extremalmenge, 19Extremalpunkt , 19

Farkas Lemma, 65Fehlerquadrats umme, 185Filte r-Verfahren, 349Finsler- Lemma von - , 301freie Variabl e, 14, 26Frit z John constraint qu alification , 229Fritz-John-Bedingung, 229Frobeniusmat rix, 33, 39Frechet-differenzierbar , 164, 196Fulkerson- Methode von , 120Färbung- eines Graphen , 427Fejer- Sat z von , 238, 403

Gauß-Newton-Verfah ren , 187Givensro tation, 48glob ales Minimum, 127Goeman s-Willi am son- Verfahren, 439gold ener Schnitt- Verfah ren des - , 130Graph,103Graphenparti t ioni erung, 442Gr aphentheorie , 101Gäte au x-differenzierbar , 164, 196

H-Norm, 364Hessematrix, 128Hir sch- Vermutung von - , 47Höldersche Un gleichung, 312

Indexvektor, 23induzier ter Graph, 106Innere-Punkte-Methoden- für konvexe P rogramme, 355- für lineare Programme, 67Innere-Punkte-Verfahren

- un zulässiges, 386, 388Inverse-Basis-Methode, 47Inz idenzmatrix , 103

J acobi-Matrix, 73

Kan te, 20, 103Karush- Sat z von - .Kuhn und Tucker , 223Kegel, 49, 204- duale, 216- pol are , 216KK T-Bedingungen, 230Klee-Minty-Probleme, 65Knoten , 103Komplem entari tä t , 50Komplementgraph, 427komplementär- Indexvektor, 23- st r ikt, 262koni sche Form- P roblem in - , 51- von konvexen P rogrammen , 233Konjugier te Gr adienten-Verfahren, 148konkav- - e Funktion , 218Konvergenzraten , 68konve x- -e Funktion , 16, 218- -e Menge, 16, 204- - e quadra t ische Funktion , 146- streng - , 16, 76- st reng -e Funktion, 218konvexe Hülle, 205kreisfreier Graph, 120Kriging-Verfah ren , 456Kulm und Tucker- Sat z von Karush , - , 223Kuhn-Tucker Punkt , 249Kurz-S chri t t-Algorithmus, 80kün stliche Vari able, 44

Lagrangedualität , 421, 424, 433Lagran gefunktion, 231, 245- erweiterte - , 299Lagran gemultiplikator, 249least-square s-Problem- nichtlineares, 184lexikoposit iv , 41

line search, 129- Armijo, 145- exakte - , 145, 150linear- - e Konvergenz , 71line arisierter Kegel, 246Lipschitzbedingung- relative, 414lokale s Minimum, 127Lovasz-Zahl, 432Lyapunovungleichung, 417Lösung- Optimal-, 2- zul ässige, 2, 14Löwner- Halbordnung, 380

Ma ratos-Effekt , 336, 349Max-Cut P roblem , 434meri t fun ction, 333Methode der Zentren , 357Minkowski-Funktional , 414monotoner Op era to r , 275

NC P-Fu nkt ion, 325Nelder-Mead-Verfahren, 453Netzwer k, 101Newt on-Kanto rovi ch- Satz von - , 163Newt on-Verfahren , 68, 368 , 369, 383- Minimier ung mi t nichtlinearen

Gleichungsrestriktionen , 322Nicht basis, 24Nicht basisvariable, 24nichtentartet , 29- du al - , 56Niveaume nge, 143Norm- lubx, , 212- lub2-, 142- Frobenius- , 142Normalgleichunge n, 185NW-Eckenregel, 109

O-Notation, 71Op timali tä tsb edingung- für allgemei ne Optimierungsproble­

me , 243- für das Transshipmentproblem , 115

Index 473

- für konvexe Optimierungsprobleme,225

Oren-Luenberger-Kl asse, 182Orthogonalproj ek tion, 78- auf eine konvexe Menge, 241Orthonormalbasis, 179

Pen alty- Funktion, 293- differenzierbare - , 298- exakte - , 296PERT, 121Pivotelem en t , 36Pol ak -Ribiere- Verfahren von - , 154po larer Kegel , 216Polyed er , 16po lynomiale Laufz eit , 67, 87, 91Pol ytop- Max-Cut-, 436- metrisches, 439- stabile-Mengen-, 429Powell- Updateforme l von , 309P redikto r-Korr ek to r-Verfahren- primales - , 389Prim al- -duales Innere-Punkte-Verfahren , 319profi table Richtung, 279Programm- lineares, 9- nichtlineares, 2Projek tion- auf konvexe Menge, 275Projektionsverfahren, 273proj izierter Gradient , 279Prädiktor-Korrektor-Verfahren- von Mehrotra , 88Präkonditionierung. 153PSB-Verfahren, 190

Q-quadra tis ch- - e Konvergenz, 71Quasi-Newton- - Bedingung, 176- - Verfahren , 173, 176, 189

R-quadratisch- - e Konvergenz, 71Rang-1-Verfahren

474 Index

- von Broyden, 176reduzierte Kosten, 34Regular itätsbedingung- 2. Ordnung, 260- Fritz John , 229- von Robinson , 248- von Sla ter , 228rel ati v innere Punkte, 211Rel ative Lipschi t z-Bedingung, 365relativer Randpunkt , 211Relaxi erung- semidefinit e, 422, 433 , 440Residuum, 77, 79rezessiver Kegel , 397Robinson- Regularitätsbedingung, 248Ros enbrock-Funkt ion- vera llgemeinert e - , 139

Sattelpunkt , 231Scha t tenpreise. 31, 61schiefsym met rische Matrix, 93Schlupfvari able, 14Schurkomplem ent , 420second ord er correct ion , 336Selbstbeschränkung, 372selbst duales line ares Programm, 93Selb stkonkordanz , 359, 360sem idefinit e P rogramme, 237 , 363- nichtlineare, 447sem idefinites P rogramm, 403Sem idefinitheitsb edingung, 363Sen siti vit ät sanalyse- bei linearen Programmen, 58- bei nichtlinearen P rogrammen , 266Sequenti al Quadrati c Programs, 327Sherman-Morrison-vVoodbury-Formel ,

61, 307Shifted Penalty Mul t ipli er Method , 309Simplex , 453Simplexform, 26- allgeme inere- , 40Simplexmethode- lexikographische, 41- Name der - , 453- von Neider und Mead, 453Simplexschrit t , 36Skalierungsinvarianz, 415Sp okt ral radius, 188

Spi ralfunkt ion , 138SQP-Verfahren , 327, 449st abil- Differentialgleichung, 417stabile Menge- in einem Graphen , 427St andardform- eines linearen Programmes, 14- eines linearen P rogrammes, 13stationärer Punkt , 128, 274, 341- sing ulärer , 342- un zul ässiger, 342steilster Abstieg- Kurve des - , 136- Verfahren des - , 146Straffunktion , 293st rikt komplem entäre Lösung, 93, 100,

264Subgradien t , 221Suchrichtung- AHO , 408 , 412 , 416- HKM, 408, 412- Klasse MZ , 408- NT , 408 , 413superl ineare Konvergenz, 174Symmetrisierung- bei semidefinit en P rogrammen , 408

Tableau , 25Tangentialkegel, 243Taylor- Satz von , 68Tr ansportpr oblem , 101Transsh ipment-Problem , 113Tr ennung , 208- eigentliche, 208- strikte, 208Tr ennungss atz, 203Trilinear form, 68- Spektralradius von symmet r ischer - ,

367Tr ust-Regi on Verfahren , 155Trust-Region-Verfahren- bei Nebe nbeding ungen , 339, 340

unimodal- -e Funktion , 129un imodulare Matrix , 113, 122universale Barrierefunkt ion , 374

unterhalbstetig, 281

Weg , 106- kürzester, 117- läng ster, 117- ungerichtet , 106

zentraler

- Pfad , 406zent ra ler Pfad , 74Zielfunktion, 2Zoutendijk , 338zuläss ige Richtung, 279zusammenhängend, 106Zwischenwert , 69Zyklus, 106

Index 475


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