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Part of the book series: Algorithms and Combinatorics ((AC,volume 12))

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Abstract

Gaius Julius Caesar (101–44 B.C.) made military use of it when his legions conquered Gaulle, Machiavelli coined it as a political doctrine for Florentine princes to improve his princes’ control over their subjects, and mathematicians employ it e.g. when they use binary search to locate the optimum of a p-unimodal function over a compact convex subset of ℝ1. Divide and conquer, or more precisely divide and reign, is what Niccolo’s doctrine translates to. The compact convex subset of ℝ1 is, of course, some finite interval of the real line and the basic idea of binary search consists in successively halving this interval — like we did in Chapter 7.5.3. Utilizing the p-unimodality (see below) of the function to be optimized, we then discard one half of the original interval forever and continue the search in the other half of the original interval — which is again a compact convex subset of ℝ1. Consequently, the basic idea can be reapplied and we can iterate. Since the length of the left-over interval is half of the length of the previous interval, the 1-dimensional “volume” of the remaining compact convex subset of ℝ1 to be searched shrinks at a geometric rate and we obtain fast convergence of the iterative scheme.

Divide et impera!

Niccolo Machiavelli (1469–1527 A.D.)

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References

  • Bland, R.G., D. Goldfarb and M.J. Todd [ 1981 ] “The ellipsoid method: a survey”, Operations Research 29 1039–1091.

    Article  MathSciNet  MATH  Google Scholar 

  • Gâcs, P. and L. Lovasz [ 1981 ] “Khachian’s algorithm for linear programming”, Mathematical Programming Study 14 61–68.

    Article  MATH  Google Scholar 

  • Grötschel, M., L. Lovâsz and A. Schrijver [ 1981 ] “The ellipsoid method and its consequences in combinatorial optimization”, Combinatorica 1 169–197.

    Article  MathSciNet  MATH  Google Scholar 

  • Grötschel, M., L. Lovâsz and A. Schrijver [ 1988 ] Geometric Algorithms and Combinatorial Optimization, Springer-Verlag, Berlin.

    Book  MATH  Google Scholar 

  • Karp, R.M. and C.H. Papadimitriou [ 1982 ] “On linear characterization of combinatorial optimization problems” SIAM Journal on Computing 11 620–632.

    Article  MathSciNet  MATH  Google Scholar 

  • Khachian, L.G. [ 1979 ] “A polynomial algorithm in linear programming”, Soviet Mathematics Doklady 20 191–194.

    Google Scholar 

  • Khachian, L.G. [ 1980 ] “Polynomial algorithms in linear programming”, USSR Comp. Math. and Math. Phys. 20 53–72.

    Article  Google Scholar 

  • Khachian, L.G. [ 1982 ] “On exact solutions of systems of linear inequalities and LP problems”, USSR Comp. Math. and Math. Phys. 22 239–242.

    Article  Google Scholar 

  • Khachian, L.G. [ 1982 ] “Convexity and computational complexity in polynomial programming”, Engineering Cybernetics 22 46–56.

    Google Scholar 

  • Kiefer, J. [ 1952 ] “Sequential minimax search for a maximum”, Proc. Amer. Math. Soc. 4 502–506.

    Article  MathSciNet  Google Scholar 

  • Korte, B. and R. Schrader [ 1980 ] “A note on convergence proofs for Shor-Khachian methods”, in Auslender, Oettli, Stoer (eds) Optimization and Optimal Control, Lecture Notes in Control and Information Sciences Vol. 30, Springer, Berlin, New York, 51–57.

    Google Scholar 

  • Levin, A.Y. [ 1965 ] “On an algorithm for convex function minimization”, Soviet Mathematics Doklady 6 286–290.

    Google Scholar 

  • Newman, D.J. [ 1965 ] “Location of the maximum on unimodal surfaces”, Journal of the Association for Computing Machinery 12 395–398.

    Article  MathSciNet  MATH  Google Scholar 

  • Padberg, M. and M.R.Rao [ 1980 ] “The Russian method: Part I, Part I I, Part III”, Graduate School of Business Administration, New York University, New York.

    Google Scholar 

  • Padberg, M. and G. Rinaldi [ 1991 ] “A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems”, SIAM Review 33 60–100.

    Article  MathSciNet  MATH  Google Scholar 

  • Schrader, R. [ 1982 ] “Ellipsoid methods”, in D. Korte (ed) Modern Applied Mathematics–Optimization and Operations Research, North-Holland, Amsterdam, 265–311.

    Google Scholar 

  • Schrijver, A. [ 1986 ] Theory of Linear and Integer Programming, Wiley, Chichester.

    MATH  Google Scholar 

  • Shor, N.Z. [ 1970 ] “Utilization of the operation of space dilatation in the minimization of convex functions”, Cybernetics 6 7–15.

    Article  Google Scholar 

  • Shor, N.Z. [ 1970 ] “Convergence rate of the gradient method with dilatation of the space”, Cybernetics 6 102–108.

    Article  Google Scholar 

  • Shor, N.Z. [1977] “Cut-off method with space extension in convex programming problems, Cybernetics 13 94–96.

    Google Scholar 

  • Wolfe, P. [ 1980 ] “A bibliography for the ellipsoid algorithm”, IBM Research Center, Yorktown Heights, NY.

    Google Scholar 

  • Yudin, D.B. and Nemirovskii, A.S. [ 1976 ] “Informational complexity and efficient methods for the solution of convex extremal problems” Matekon 13 3–25.

    Google Scholar 

  • Golub, G.H. and C.F.Van Loan [ 1983 ] Matrix Computations, The Johns Hopkins University Press, Baltimore.

    MATH  Google Scholar 

  • Ostrowski, A.M. [ 1973 ] Solution of equations in Euclidean and Banach spaces, 3rd Ed., Academic Press, New York.

    MATH  Google Scholar 

  • Grötschel, M., L. Lovâsz and A. Schrijver [ 1988 ] Geometric Algorithms and Combinatorial Optimization, Springer-Verlag, Berlin.

    Book  MATH  Google Scholar 

  • John, F. [ 1948 ] “Extremum problems with inequalities as subsidiary conditions” in Studies and Essays, Courant anniversary volume, New York, Interscience.

    Google Scholar 

  • Khachian, L.G. [ 1979 ] “A polynomial algorithm in linear programming”, Soviet Mathematics Doklady 20 191–194.

    Google Scholar 

  • Padberg, M. and M.R.Rao [ 1979 ] “The Russian method for linear inequalities and linear optimization”, November 1979, Revised June 1980, Graduate School of Business Administration, New York University, New York.

    Google Scholar 

  • Padberg, M. and M.R.Rao [ 1979 ] “The Russian method for linear inequalities II: Approximate arithmetic”, January 1980, Graduate School of Business Administration, New York University, New York.

    Google Scholar 

  • Shor, N.Z. [ 1970 ] “Utilization of the operation of space dilatation in the minimization of convex functions”, Cybernetics 6 7–15.

    Article  Google Scholar 

  • Shor, N.Z. [ 1970 ] “Convergence rate of the gradient method with dilatation of the space”, Cybernetics 6 102–108.

    Article  Google Scholar 

  • Dantzig, G.B., D.R. Fulkerson and S.M. Johnson [ 1954 ] “Solution of a large-scale travelling salesman problem”, Operations Research 2 393–410.

    Article  MathSciNet  Google Scholar 

  • Padberg, M. and M.R. Rao [ 1982 ] “Odd Minimum Cut-Sets and b-Matchings”, Mathematics of Operations Research 7 67–80.

    Article  MathSciNet  MATH  Google Scholar 

  • Padberg, M. and G. Rinaldi [ 1991 ] “A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems”, SIAM Review 33 60–100.

    Article  MathSciNet  MATH  Google Scholar 

  • Cassels, J.W.S. [ 1965 ] An Introduction to Diophantine Approximation, Cambridge University Press, Cambridge.

    Google Scholar 

  • Courant, R. and H. Robbins [ 1978 ] What is Mathematics, Oxford University Press, New York.

    MATH  Google Scholar 

  • Grötschel, M., L. Lovâsz and A. Schrijver [ 1988 ] Geometric Algorithms and Combinatorial Optimization, Springer-Verlag, Berlin.

    Book  MATH  Google Scholar 

  • Grünbaum, B. [ 1967 ] Convex Polytopes, Wiley, London.

    MATH  Google Scholar 

  • Khinchin, A.Y. [ 1935 ] Continued Fractions (in Russian). English translation [1964], The University of Chicago Press, Chicago, Illinois.

    Google Scholar 

  • Stoer, J. and C. Witzgall [ 1970 ] Convexity and Optimization in Finite Dimensions I, Springer, Berlin.

    Book  Google Scholar 

Download references

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© 1999 Springer-Verlag Berlin Heidelberg

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Padberg, M. (1999). Ellipsoid Algorithms. In: Linear Optimization and Extensions. Algorithms and Combinatorics, vol 12. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-12273-0_9

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  • DOI: https://doi.org/10.1007/978-3-662-12273-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08511-6

  • Online ISBN: 978-3-662-12273-0

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