Skip to main content

Part of the book series: Nonconvex Optimization and Its Applications ((NOIA,volume 2))

Abstract

Lipschitz optimization solves global optimization problems in which the objective function and constraint left-hand sides may be given by oracles (or explicitly) and have a bounded slope. The problems of finding an optimal solution, an ε-optimal one, all optimal solutions, and a small volume enclosure of all optimal solutions within hyperrectangles (possibly containing only a-optimal points) are investigated. In the univariate case, necessary conditions for finite convergence are studied as well as bounds on the number of iterations and convergence of e-optimal algorithms. Methods of Piyayskii, Evtushenko, Timonov, Schoen, Galperin, Shen and Zhu, and Hansen, Jaumard and Lu are discussed and compared experimentally. The same is done in the multivariate case for the algorithms of Piyayskii; Mladineo; Jaumard, Herrmann and Ribault; Pinter; Galperin; Meewella and Mayne; Wood; Gourdin, Hansen and Jaumard.

Constrained Lipschitz optimization is then discussed focusing on extensions of Piyayskii’s algorithm and methods which consider all constraints simultaneously, i.e., those of Thach and Tuy, as well as the recent method of Gourdin, Jaumard and MacGibbon. Heuristic algorithms, some of which involve estimation of the Lipschitz constant, are then briefly reviewed. A representative sample of the many existing and potential applications is discussed. An evaluation of the scope, strength and limitations of Lipschitz optimization completes the paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 469.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 599.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 599.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Archetti, F. and Betrò, B.: 1978, ‘A Priori Analysis of Deterministic Strategies for Global Optimization Problems’, in Towards Global Optimization 2, L.C.W. Dixon and G.P. Szegö eds., North Holland.

    Google Scholar 

  2. Asplund, E.: 1973, ‘Differentiability of the Metric Projection in Finite Dimensional Euclidean Space’, Proceedings of the American Mathematical Society 38 pp. 218–219.

    MathSciNet  MATH  Google Scholar 

  3. Atkinson, M.D., Sack, J.-R., Santoro, N. and Strotholte, Th.: 1986, ‘Min-max Heaps and Generalized Priority Queues’, Communications of the ACM 29 pp. 996–1000.

    MATH  Google Scholar 

  4. Avriel, M.: 1976, ‘Nonlinear Programming: Analysis and Methods’, Prentice-Hall, Englewood Cliffs, NJ.

    MATH  Google Scholar 

  5. Baoping, Z., Wood, G.R., and Baritompa, W.P.: 1993, ‘Multidimensional Bisection: The Performance and the Context’, Mathematical Programming 3 pp. 337–358.

    MATH  Google Scholar 

  6. Baritompa, W.: 1993, ‘Customized Methods for Global Optimization — A Geometric Viewpoint’, Journal of Global Optimization 3 pp. 193–212.

    MathSciNet  MATH  Google Scholar 

  7. Basso, P.: 1982, ‘Iterative Method for the Localization of the Global Maximum’, SIAM Journal on Numerical Analysis 19 pp. 781–792.

    MathSciNet  MATH  Google Scholar 

  8. Basso, P.: 1985, ‘Optimal Search for the Global Maximum of Function with Bounded Semi-norm’, SIAM Journal of Numerical Analysis 22 pp. 888–903.

    MathSciNet  MATH  Google Scholar 

  9. Biggs, M.C.: 1971, ‘Minimization Algorithms Making use of Non-Quadratic Properties of the Objective Function’, Journal Inst. Math. Applies. 8.

    Google Scholar 

  10. Bracken, J. and McCormick, G.P.: 1968, Selected Applications of Nonlinear Programming, John Wiley & Sons, New York.

    MATH  Google Scholar 

  11. Branin, F.H. Jr.: 1972, ‘Widely Convergent Method for Multiple Solution of Simultaneous Nonlinear Equations’, IBM Journal of Research and Development 16 pp. 504–522.

    MathSciNet  MATH  Google Scholar 

  12. Brent, R.P.: 1973, Algorithms for Minimization without Derivatives, Prentice-Hall, Englewood Cliffs, NJ.

    MATH  Google Scholar 

  13. Brooks, S.H.: 1958, ‘Discussion of Random Methods for Locating Surface Maxima’, Operations Research 6 pp. 244–251.

    MathSciNet  Google Scholar 

  14. Butz, A.R.: 1968, ‘Space Filling Curves and Mathematical Programming’, Information and Control 12, pp. 319–330.

    MathSciNet  Google Scholar 

  15. Chuyan, O.P.: 1986, ‘An Optimal Single-step Algorithm for Maximizing Doubly Differentiable Functions’, USSR Comput. Maths. Math. Phys. 26 pp. 37–47.

    MathSciNet  MATH  Google Scholar 

  16. Cornelius, H. and Lohner, R.: 1984, ‘Computing the Range of Values of Real Function with Accuracy Higher than Second Order’, Computing 33 pp. 331–347.

    MathSciNet  MATH  Google Scholar 

  17. Curral, C.: 1992, ‘Concave and Lipschitz Optimization’, Undergraduate Research Report, Institut d’Informatique d’Entreprise, Evry, France.

    Google Scholar 

  18. Danilin, Y.M.: 1971, ‘Estimation of the Efficiency of an Absolute-Minimum-Finding Algorithm’, USSR Computational Mathematics and. Mathematical Physics 11, pp. 261–267.

    Google Scholar 

  19. Danilin, Y.M. and Piyayskii, S.A.: 1967, ‘On an Algorithm for Finding an Absolute Minimum’, in Theory of Optimal Solutions, Kiev, IK AN USSR (in Russian), pp. 25–37.

    Google Scholar 

  20. de Palma, A., Hansen, P. and Labbé, M.: 1990, ‘Commuter’s Paths with Penalties for Early or Late Arrival Time’, Transportation Science 24 pp. 276–286.

    MathSciNet  MATH  Google Scholar 

  21. de Palma, A. and Hansen, P.: 1990, ‘Optimal Departure Times for Commuters in Congested Networks’, Annals of Operations Research 25 pp. 279–290.

    MathSciNet  MATH  Google Scholar 

  22. Dixon, L.C.W.: 1978, ‘Global Optima Without Convexity’, Technical report, Numerical Optimization Centre, Hatfield Polytechnic, Hatfield, England.

    Google Scholar 

  23. Dixon, L.C.W. and Szego, G.P., eds.: 1976, Towards Global Optimization, North-Holland, Amsterdam, Holland.

    Google Scholar 

  24. Drezner, Z.: 1985, ‘A Solution to the Weber Location Problem on the Sphere, Journal of Operational Research Society 36 pp. 333–334.

    MATH  Google Scholar 

  25. Eason, E.D. and Fenton, R.G.: 1974, ‘A Comparison of Numerical Optimization Methods for Engineering Design’, Transactions of the ASME 96 Series B, No. 1.

    Google Scholar 

  26. Erlenkotter, D.: 1978, ‘A Dual-based Procedure for Uncapacitated Facility Location’, Operations Research, pp. 378–386.

    Google Scholar 

  27. Evtushenko, Y.G.: 1971, ‘Numerical Methods for Finding Global Extrema (case of a nonuniform mesh)’, USSR Computational Mathematics and Mathematical Physics 11 pp. 3854.

    Google Scholar 

  28. Evtushenko, Y.G. and Rat’kin: 1988, ‘The Method of Half-Division for Global Optimization of a Function of Many Variables’, Technique of Cybernetics 1 pp. 75–83.

    Google Scholar 

  29. Ferrari, A. and Galperin, E.A.: 1993, ‘Numerical Experiments with One-Dimensional Adaptive Cubic Algorithm’, Computers Math. Applic. 25(10/11) pp. 47–56.

    MathSciNet  MATH  Google Scholar 

  30. Fichtenholz, G.M.: 1964, Differential -und Integralrechnung I, Berlin.

    Google Scholar 

  31. Gaffney, P.W.: 1978, ‘The Range of Possible Values of f(x)’, Journal of the Institute of Mathematics and Its Applications 21 pp. 211–226.

    MathSciNet  MATH  Google Scholar 

  32. Galperin, E.A.: 1984, ‘Un algorithme cubique pour optimisation non linéaire’, Annales de Sciences Mathématiques du Québec 8(2) pp. 155–160.

    MathSciNet  MATH  Google Scholar 

  33. Galperin, E.A.: 1985, ‘The Cubic Algorithm’, Journal of Mathematical Analysis and Applications 112(2) pp. 635–640.

    MathSciNet  MATH  Google Scholar 

  34. Galperin, E.A.: 1987, ‘Two Alternatives for the Cubic Algorithm’, Journal of Mathematical Analysis and Applications 126 pp. 229–237.

    MathSciNet  MATH  Google Scholar 

  35. Galperin, E.A.: 1987, ‘The Beta-Algorithm“, Journal of Mathematical Analysis and Applications 126 pp. 455–468.

    MathSciNet  MATH  Google Scholar 

  36. Galperin, E.A.: 1988, ‘Precision, Complexity, and Computational Schemes of the Cubic Algorithm’, Journal of Optimization Theory and Applications 57 pp. 223–238.

    MathSciNet  MATH  Google Scholar 

  37. Galperin, E.A.: 1988, ‘The Beta-Algorithm for Mathematical Programming’, in Advances in Optimization and Control, H.A. Eiselt and G. Pederzoli eds., Springer-Verlag. (Lecture Notes in Economics and Mathematical Systems # 302, pp. 38–48.)

    Google Scholar 

  38. Galperin, E.A.: 1991, ‘The Integer Cubic Algorithm’, International J. Computers and Mathematics with Applications, 21(6/7) pp. 215–224.

    MathSciNet  MATH  Google Scholar 

  39. Galperin, E.A.: 1993, ‘The Fast Cubic Algorithm’, Computers and Mathematics with Applications 25(10/11) pp. 147–160.

    MathSciNet  MATH  Google Scholar 

  40. Galperin, E.A.: 1993, ‘The Alpha Algorithm and the Application of the Cubic Algorithm in Case of Unknown Lipschitz Constant’, Computers and Mathematics with Applications 25(10/11) pp. 71–78.

    MathSciNet  MATH  Google Scholar 

  41. Galperin, E.A., and Zheng, Q.: 1987, ‘Nonlinear Observation via Global Optimization Methods: Measure Theory Approach’, Journal of Optimization Theory and Applications 54(1) pp. 63–92.

    MathSciNet  MATH  Google Scholar 

  42. Gergel, V.P., Strongin, L.G. and Strongin, R.G.: 1987, ‘The Vicinity Method in Pattern Recognition’, Engineering Cybernetics (Transl. from Izv. Acad. Nauk USSR Techn. Kibernetika 4, 14–22).

    Google Scholar 

  43. Goldstein, A.A. and Price, J.-F.: 1971, ‘On Descent from Local Minima’, Mathematics of Computation 25 pp. 569–574.

    MathSciNet  MATH  Google Scholar 

  44. Gourdin, E., Hansen, P. and Jaumard, B.: 1992, ‘A Strong Heuristic for Maximum Likelihood of the Three-Parameter Weibull Distribution’, Les Cahiers du GERAD, G-92–26 revised: February 1993, 19 pages.

    Google Scholar 

  45. Gourdin, E., Hansen, P. and Jaumard, B.: 1994, ‘Finding Maximum Likelihood Estimators for the Three Parameter Weibull Distribution’, to appear in Journal of Global Optimization.

    Google Scholar 

  46. Gourdin, E., Hansen, P. and Jaumard, B.: 1994, ‘Global Optimization of Multivariate Lipschitz Functions: Survey and Computational Comparison’, Les Cahiers du GERAD May 1994.

    Google Scholar 

  47. Gourdin, E., Jaumard, B. and MacGibbon, B.: 1994, ‘Global Optimization Decomposition Methods for Bounded Parameter Minimax Risk Evaluation’, SIAM Journal on Scientific and Statistical Computing 15 pp. 16–35.

    MathSciNet  MATH  Google Scholar 

  48. Gourdin, E., Jaumard, B., and Ellaia, R.: 1994, ‘Optimization of Hölder Functions’, Les Cahiers du GERAD May 1994.

    Google Scholar 

  49. Hanjoul, P., Hansen, P., Peeters, D. and Thisse, J.-F.: 1990, ‘Uncapacited Plant Location Under Alternative Space Price Policies’, Management Science 36 pp. 41–57.

    MathSciNet  MATH  Google Scholar 

  50. Hansen, E.R.: 1979, ‘Global Optimum Using Interval Analysis: The One-Dimensional Case’, Journal of Optimization Theory and Applications 29 pp. 331–344.

    MathSciNet  MATH  Google Scholar 

  51. Hansen, P.: 1974, ‘Programmes mathématiques en variables 0–1,’ Thèse d’agrégation, Université libre de Bruxelles.

    Google Scholar 

  52. Hansen, P.: 1975, ‘Les procédures d’optimisation et d’exploration par séparation et évaluation’, in Combinatorial Programming B. Roy ed., Reidel, Dordrecht, pp. 19–65.

    Google Scholar 

  53. Hansen, P., Jaumard, B. and Lu, S.-H.: 1989, ‘Global Minimization of Univariate Functions by Sequential Polynomial Approximation’, International Journal of Computer Mathematics 28 pp. 183–193.

    MATH  Google Scholar 

  54. Hansen, P., Jaumard, B. and Lu, S.-H.: 1991, ‘On the Number of Iterations of Piyayskii’s Global Optimization Algorithm’, Mathematics of Operations Research 16 pp. 334–350.

    MathSciNet  MATH  Google Scholar 

  55. Hansen, P., Jaumard, B. and Lu, S.-H.: 1991, ‘An Analytical Approach to Global Optimization’, Mathematical Programming 52 pp. 227–254.

    MathSciNet  MATH  Google Scholar 

  56. Hansen, P., Jaumard, B. and Lu, S.-H.: 1991, ‘On Timonov’s Algorithm for Global Optimization of Univariate Lipschitz Functions’, Journal of Global Optimization 1 pp. 37–46.

    MathSciNet  MATH  Google Scholar 

  57. Hansen, P., Jaumard, B. and Lu, S.-H.: 1992, ‘Global Optimization of Univariate Lipschitz Functions: I. Survey and Properties’, Mathematical Programming 55 pp. 251–272.

    MathSciNet  MATH  Google Scholar 

  58. Hansen, P., Jaumard, B. and Lu, S.-H.: 1992, ‘Global Optimization of Univariate Lipschitz Functions: II. New Algorithms and Computational Comparison’, Mathematical Programming 55 pp. 273–292.

    MathSciNet  MATH  Google Scholar 

  59. Hansen, P., Jaumard, B. and Lu, S.-H.: 1992, ‘On the Use of Estimates of the Lipschitz Constant in Global Optimization’, Journal of Optimization Theory and Applications 75 pp. 195–200.

    MathSciNet  MATH  Google Scholar 

  60. Hansen, P., Peeters, D. and Thisse, J.F.: 1992, ‘Facility Location Under Zone Pricing’, Les Cahiers du GERAD G-92–29 July 1992, 22 pages.

    Google Scholar 

  61. Hansen, P., Thisse, J.F. and Hanjoul, P.: 1981, ‘Simple Plant Location Under Uniform Delivered Pricing’, European Journal of Operations Research 6 pp. 94–103.

    MATH  Google Scholar 

  62. Hartman, J.K.: 1973, ‘Some Experiments in Global Optimization’, Naval Research Logistics Quarterly 20 pp. 569–576.

    MATH  Google Scholar 

  63. Hasham, A. and Sack, J.-R.: 1987, `Bounds for Min-max Heaps’, BIT 27 pp. 315–323.

    MathSciNet  MATH  Google Scholar 

  64. Himmelblau, D.M.: 1972, Applied Nonlinear Programming McGraw-Hill Book Company.

    Google Scholar 

  65. Horst, R.: 1986, ‘A General Class of Branch-and-Bound Methods in Global Optimization with Some New Approaches for Concave Minimization’, Journal of Optimization Theory and Applications 51 pp. 271–291.

    MathSciNet  MATH  Google Scholar 

  66. Horst, R.: 1987, ‘On Solving Lipschitzian Optimization Problems’, in Essays on Nonlinear Analysis and Optimization Problems National Center for Scientific Research, Hanoi, pp. 73–88.

    Google Scholar 

  67. Horst, R. and Thy, H.: 1987, ‘On the Convergence of Global Methods in Multiextremal Optimization’, Journal of Optimization Theory and Applications 54 pp. 253–271.

    MathSciNet  MATH  Google Scholar 

  68. Horst, R.: 1988, ‘Deterministic Global Optimization with Partition Sets whose Feasibility if not Known. Applications to Concave Minimization, Reverse Convex Constraints, D.C. Programming and Lipschitzian Optimization’, Journal of Optimization Theory and its Applications 58 pp. 11–37.

    Google Scholar 

  69. Horst, R., and Thoai, N.V.: 1988, ‘Branch-and-bound Methods for Solving Systems of Lipschitzian Equations and Inequalities’, Journal of Optimization Theory and Applications 58 pp. 139–146.

    MathSciNet  MATH  Google Scholar 

  70. Horst, R., and Thy, H.: 1992, Global Optimization - Deterministic Approaches 2nd Edition, Berlin: Springer.

    Google Scholar 

  71. Ivanov, V.V.: 1972, ‘On Optimal Algorithms of Minimization in the Class of Functions with the Lipschitz Condition’, Information Processing 71 pp. 1324–1327.

    Google Scholar 

  72. Ivanov, V.V.: 1972, ‘On Optimal Algorithms of the Minimization of Functions of Certains Classes’, Kibernetika 4 pp. 81–94.

    Google Scholar 

  73. Jaumard, B., Herrmann, T. and Ribault, H.: 1994, ‘An On-line Cone Intersection Algorithm for Global Optimization of Multivariate Lipschitz Functions’, in preparation.

    Google Scholar 

  74. Kiefer, J.: 1953, ‘Sequential Minimax Search for a Maximum’, Proceedings of the American Mathematical Society 4 pp. 502–506.

    MathSciNet  MATH  Google Scholar 

  75. Korotkich, V.V.: 1989, ‘Multilevel Dichotomy Algorithm in Global Optimization’, in System Modelling and Optimization Proceedings of the 14th IFIP Conference, Leipzig, Springer-Verlag, pp. 161–169.

    Google Scholar 

  76. Levy, A.V. and Gomez, S.: 1985, ‘The Tunneling Method Applied to Global Optimization’, in Numerical Optimization 1984 SIAM P.T. Boggs ed., SIAM Philadelphia, pp. 213–244.

    Google Scholar 

  77. Levy, A.V., Montalvo, A., Gomez, S. and Calderon, A.: 1981, ‘Topics in Global Optimization’, in Lecture Notes on Mathematics 909 Springer-Verlag.

    Google Scholar 

  78. Love, R.F., Morris, J.G. and Wesolowsky, G.O.: 1988, Facilities Location, Models and Methods North-Holland, Amsterdam.

    MATH  Google Scholar 

  79. Luus, R. and Jaakola, T.H.I.: 1973, ‘Optimization by Direct Search and Systematic Reduction of the Size of the Search Region’, American Institute of Chemical Engineers Journal 19 pp. 760–766.

    Google Scholar 

  80. Markin and Strongin, R.G.: 1988, ‘A Method for Solving Multi-Extremal Problems with Non-Convex Constraint’, USSR Computational Mathematics and Mathematical Physics 27 pp. 33–39.

    Google Scholar 

  81. Marsden, J. and Weinstein, A.: 1985, Calculus I Springer-Verlag, New York.

    MATH  Google Scholar 

  82. Mayne, D.Q. and Polak E.: 1984, ‘Outer Approximation Algorithm for Nondifferentiable Optimization Problems’, Journal of Optimization Theory and Applications 42(1) pp. 1930.

    Google Scholar 

  83. Mayurova, I.V. and Strongin, R.G.: 1986, ‘Minimization of a Multi-Extremum Function with a Discontinuity’, USSR Computational Mathematics and Mathematical Physics 24 pp. 121–126.

    Google Scholar 

  84. McCormick, G.P.: 1976, ‘Computability of Global Solutions to Factorable Nonconvex Programs: Part 1 — Convex Underestimating Problems’, Mathematical Programming 10 pp. 147–175.

    MathSciNet  MATH  Google Scholar 

  85. Meewella, C.C. and Mayne, D.Q.: 1988, ‘An Algorithm for Global Optimization of Lipschitz Functions’, Journal of Optimization Theory and Applications 57 pp. 307–323.

    MathSciNet  MATH  Google Scholar 

  86. Meewella, C.C., and Mayne, D.Q.: 1989, ‘Efficient Domain Partitioning Algorithms for Global Optimization of Rational and Lipschitz Continuous Functions’, Journal of Optimization Theory and Applications 61(2) pp. 247–270.

    MathSciNet  MATH  Google Scholar 

  87. Mladineo, R.H.: 1986, ‘An Algorithm for Finding the Global Maximum of a Multimodal, Multivariate Function’, Mathematical Programming 34 pp. 188–200.

    MathSciNet  MATH  Google Scholar 

  88. Mladineo, R.H.: 1992, ‘Convergence Rates of a Global Optimization Algorithms’, Mathematical Programming 54 pp. 223–232.

    MathSciNet  MATH  Google Scholar 

  89. Mladineo, R.H.: 1992, ‘Stochastic Minimization of Lipschitz Functions’, in Recent Advances in Global Optimization, C. Floudas and P. Pardalos, eds., Princeton University Press, pp. 369–383.

    Google Scholar 

  90. Nefedov, V.N.: 1987, ‘Search for the Global Maximum of a Function of Several Variables on a Set Defined by Constraints of Inequality Type’, USSR Computational Mathematics and Mathematical Physics 27 pp. 23–32.

    MATH  Google Scholar 

  91. Nelder, J.A. and Mead, R.: 1965, ‘A Simplex Method for Function Minimization’, Computer Journal 7 pp. 308–313.

    MATH  Google Scholar 

  92. Pardalos, P. and Rosen, J.B.: 1987, ‘Constrained Global Optimization: Algorithms and Applications’, Lecture Notes in Computer Science 268 Springer, Berlin.

    Google Scholar 

  93. Patwardhan, A.A., Karim, M.N. and Shah, R.: 1987, ‘Controller Tuning by a Least-squares Method’, AIChE Journal 33 pp. 1735–1737.

    Google Scholar 

  94. Phillips, D.T., Ravindran, A. and Solberg, J.J.: 1976, Operations Research Principles and Practice Wiley, New York.

    MATH  Google Scholar 

  95. Pinter, J.: 1986, ‘Globally Convergent Methods for n-dimensional Multiextremal Optimization’, Optimization 17 pp. 187–202.

    MathSciNet  MATH  Google Scholar 

  96. Pinter, J.: 1986, ‘Extended Univariate Algorithms for n-dimensional Global Optimization’, Computing 36 pp. 91–103.

    MathSciNet  MATH  Google Scholar 

  97. Pinter, J.: 1986, ‘Global Optimization on Convex Sets’, Operations Research Spektrum 8 pp. 197–202.

    MathSciNet  MATH  Google Scholar 

  98. Pinter, J.: 1988, ‘Branch-and-bound Algorithms for Solving Global Optimization Problems with Lipschitzian Structure’, Optimization 19 pp. 101–110.

    MathSciNet  MATH  Google Scholar 

  99. Pinter, J.: 1990, ‘On the Convergence of Adaptive Partition Algorithms in Global Optimization’, Optimization 21 pp. 231–235.

    MathSciNet  MATH  Google Scholar 

  100. Pinter, J.: 1990, ‘Solving Nonlinear Equation Systems via Global. Partition and Search: Some Experimental Results’, Computing 43 pp. 309–323.

    MathSciNet  MATH  Google Scholar 

  101. Pinter, J.: 1990, ‘Model Calibration: Problem Statement, So1mion Method and Implementation’, Research Report 90.024, Rijkswaterstaat RIZA, Lelystad.

    Google Scholar 

  102. Pinter, J.: 1990, ‘Simplicial Partition Strategies for Lipschitzian Global Optimization’, Working Paper, Rijkswaterstaat RIZA, Lelystad.

    Google Scholar 

  103. Pinter, J.: 1990, ‘Globally Optimized Calibration of Environmental Models’, Annals of Operations Research 25 pp. 211–222.

    MathSciNet  MATH  Google Scholar 

  104. Pinter, J.: 1991, ‘Global Convergence Revisited: Reply to A. Zilinskas’, Computing 46 pp. 87–91.

    MathSciNet  MATH  Google Scholar 

  105. Pinter, J.: 1992, ‘Convergence Qualification of Adaptive Partition Algorithms in Global Optimization’, Mathematical Programming 56 pp. 343–360.

    MathSciNet  MATH  Google Scholar 

  106. Pinter, J.: 1992, ‘Lipschitzian Global Optimization: Some Prospective Applications’, in Recent Advances in Global Optimization C. Floudas and P.M. Pardalos eds., Princeton University Press, pp. 399–432.

    Google Scholar 

  107. Pinter, J., P. Benedek, and Darâzs, A.: 1990, ‘Risk Management of Accidental Water Pollution: an Illustrative Application’, Water Science and Technology 22 pp. 265–274.

    Google Scholar 

  108. Pinter, J., and Cooke, R.: 1987, ‘Combining Expert Opinions: an Optimization Framework’, Research Report 87–84, Department of Mathemtics and Informatics, Delft University of Technology.

    Google Scholar 

  109. Pinter, J., and Pesti, G.: 1991, ‘Set Partition by Globally Optimized Cluster Seed Points’, European Journal of Operational Research 51 pp. 127–135.

    MATH  Google Scholar 

  110. Pinter, J., Szabd, J. and Somlyody, L.: 1986, ‘Multiextremal Optimization for Calibrating Water Resources Models’, Environmental Software 1 pp. 98–105.

    Google Scholar 

  111. Pinter, J., and Van der Molen: 1991, ‘Calibration under Different Problem Specifications: Applications to the Model SED’, Research Report 91.049, Rijkswaterstaat RIZA, Lelystad.

    Google Scholar 

  112. Piyayskii, S.A.: 1967, ‘An Algorithm for Finding the Absolute Minimum of a Function’, Theory of Optimal Solutions 2 Kiev, IK AN USSR (in Russian), pp. 13–24.

    Google Scholar 

  113. Piyayskii, S.A.: 1972, ‘An Algorithm for Finding the Absolute Extremum of a Function’, USSR Computational Mathematics and Mathematical Physics 12 pp. 57–67 ( Zh. vÿchisl Mat. mat. Fiz. 12(4) (1972) 888–896 ).

    Google Scholar 

  114. Ralston, P.A.S., Watson, K.R., Patwardhan, A.A. and Deshpande, P.B.: 1985, `A Computer Algorithm for Optimized Control’, Ind. Eng. Chem. Process Des. Dev. 24 p. 1132.

    Google Scholar 

  115. Ratschek, H. and Rokne, J.: 1984, Computer Methods for the Range of Functions Ellis Horwood, Chichester.

    MATH  Google Scholar 

  116. Ratschek, H. and Rokne,J.: 1988, New Computer Methods for Global Optimization Ellis Horwood, Chichester.

    MATH  Google Scholar 

  117. Rinnboy Kan, A.H.G., and Timmer, G.T.: Global Optimization in G.L. Nemhauser, A.H.G. Rinnooy Kan and M.J. Told, eds., Handbook of Operations Research, Volume 1: Optimization, pp. 631–662.

    Google Scholar 

  118. Rokne, J.: 1977, ‘Bounds for an Interval Polynomial’, Computing 18 pp. 225–240.

    MathSciNet  MATH  Google Scholar 

  119. Schittkowski, K.: 1987, More Test Examples for Nonlinear Programming Codes Lecture Notes in Economics and Mathematical Systems 282 Springer Verlag, Berlin, Heidelberg, New York.

    Google Scholar 

  120. Schoen, F.: 1982, ‘On a sequential Search Strategy in Global Optimization Problems’, Calcolo 19 pp. 321–334.

    MathSciNet  MATH  Google Scholar 

  121. Schwefel, H.: 1981, Numerical Optimization of Computer Models John Wiley and Sons, New York.

    MATH  Google Scholar 

  122. Sergeev, Ya.D., and Strongin, R.G.: 1990, ‘A Global Minimization Algorithm with Parallel Iterations’, USSR Comput. Maths. Math. Phys. 29(2) pp. 7–15.

    MathSciNet  Google Scholar 

  123. Shen, Z. and Zhu, Y.: 1987, ‘An Interval Version of Shubert’s Iterative Method for the Localization of the Global Maximum’, Computing 38 pp. 275–280.

    MathSciNet  MATH  Google Scholar 

  124. Shepilov, M.A.: 1987, ‘Determination of the Roots and of the Global Extremum of a Lipschitz Function’, Cybernetics 23 pp. 233–238.

    MATH  Google Scholar 

  125. Shubert, B.O.: 1972, ‘A Sequential Method Seeking the Global Maximum of a Function’, SIAM Journal on Numerical Analysis 9, pp. 379–388.

    MathSciNet  MATH  Google Scholar 

  126. Sobol, I.M.: 1987, ‘On Functions Satisfying a Lipschitz Condition in Multidimensional Problems of Computational Mathematics’, Soviet Mathematical Doklady 35 pp. 466–470.

    MATH  Google Scholar 

  127. Strigul, O.I.: 1986, ‘Search for a Global Extremum in a Certain Subclass of Functions with the Lipschitz Condition’, Cybernetics 21 pp. 812–819.

    Google Scholar 

  128. Strongin, R.G.: 1973, ‘On the Convergence of an Algorithm for Finding a Global Extremum’, Engineering Cybernetics 11, pp. 549–555.

    MathSciNet  Google Scholar 

  129. Strongin, R.G.: 1978, Numerical Methods in Multiextremal Problems, (in Russian) Nauka, Moscow.

    Google Scholar 

  130. Strongin, R.G.: 1984, ‘Numerical methods for multiextremal nonlinear programming problems with nonconvex constraints’, in Lecture Notes in Economics and Mathemtical Systems 255 pp. 278–282, Proceedings 1984, V.F. Demyanov and D. Pallaschke eds., Springer-Verlag, Berlin.

    Google Scholar 

  131. Strongin, R.G.: 1992, ‘Algorithms for Multi-Extremal Mathematical Programming Problems Employing the Set of Joint Space-Filling Curves’, Journal of Global Optimization 2, pp. 357–378.

    MathSciNet  MATH  Google Scholar 

  132. Sukharev, A.G.: 1972, ‘Optimal Strategies of the Search for an Extremum’, USSR Computational Mathematics and Mathematical Physics, pp. 119–137 (Zh. vÿchisl Mat. mat. Fiz. 11(4) (1971) 910–924).

    Google Scholar 

  133. Sukharev, A.G.: 1972, ‘Best Sequential Search Strategies for Finding an Extremum’, Zh. Vychisl. Mat. mat. Fiz 12 pp. 35–50 [English transl: U.S.S.R. Comput. Maths. Math. Phys. 12, 39–59].

    Google Scholar 

  134. Thach, P.T.: 1993, ‘D.C. Sets, D.C. Functions and Nonlinear Equations’, Mathematical Programming 58 pp. 415–428.

    MathSciNet  MATH  Google Scholar 

  135. Thach, P.T., and Tuy, H.: 1987, ‘Global Optimization under Lipschitzian Constraints’, Japan Journal of Applied Mathematics 4 (2), pp. 205–217.

    MathSciNet  MATH  Google Scholar 

  136. Thach, P.T., and Thy, H.: 1990, ‘The Relief Indicator Method for Constrained Global Opti?mization’, Naval Research Logistics 37, pp. 473–497.

    MathSciNet  MATH  Google Scholar 

  137. Timonov, L.N.: 1977, ‘An Algorithm for Search of a Global Extremum’, An Algorithm for Search of a Global Extremum 15, pp. 38–44.

    Google Scholar 

  138. Thy, H.: 1994, ‘D-C Optimization: Theory, Methods and Algorithms’, in Handbook of Global Optimization R. Horst and P. Pardalos eds., Kluwer, Dordrecht.

    Google Scholar 

  139. Vysotskaya, I.N., and Strongin, R.: 1983, ‘A Method for Solving Nonlinear Equations using Prior Probabilistic Root Estimates’, USSR Computational Mathematics and Mathematical Physics 23, pp. 3–12 (in russian).

    MathSciNet  Google Scholar 

  140. Walster, G.W., Hansen, E.R. and Sengupta, S.: 1985, ‘Test Results for a Global Optimization Algorithm’, in Numerical Optimization 1984 SIAM P.T. Boggs ed., SIAM Philadelphia, pp. 272-287.

    Google Scholar 

  141. Wingo, D.R.: 1984, ‘Fitting Three-parameter Lognormal Models by Numerical Global Optimization — An Improved Algorithm’, Computational Statistics and Data Analysis 2, pp. 13–25.

    MATH  Google Scholar 

  142. Wingo, D.R.: 1985, ‘Global Minimizing Polynomials without Evaluating Derivatives’, International Journal of Computer Mathematics 17, pp. 287–294.

    MATH  Google Scholar 

  143. Wood, G.R.: 1991, ‘Multidimensional Bisection Applied to Global Optimization’, Computers and Mathematics with Applications 21, pp. 161–172 .

    MathSciNet  MATH  Google Scholar 

  144. Wood, G.R.: 1992, ‘The Bisection Method in Higher Dimensions’, Mathematical Programming 55, pp. 319–337 .

    MathSciNet  MATH  Google Scholar 

  145. Zhang, B., Wood, G.R. and Baritompa, W.P.: 1993, ‘Multidimensional Bisection: The Performance and the Context’, Journal of Global Optimization 3(3), pp. 337–358.

    MathSciNet  MATH  Google Scholar 

  146. Zilinskas, A.: 1986, Global Optimization, Aziomatics of Statistical Models, Algorithms and their Applications Mosklas, Vilnius (in Russian).

    Google Scholar 

  147. Zilinskas, A.: 1989, ‘A note on Pinter’s Paper’, Optimization 19, pp. 195.

    MathSciNet  Google Scholar 

  148. Zilinskas, A.: 1989, ‘A Note on “Extended Univariate Algorithms” by J. Pinter’, Computing 41, pp. 275–276.

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Hansen, P., Jaumard, B. (1995). Lipschitz Optimization. In: Horst, R., Pardalos, P.M. (eds) Handbook of Global Optimization. Nonconvex Optimization and Its Applications, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-2025-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-2025-2_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-5838-1

  • Online ISBN: 978-1-4615-2025-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics