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Error bounds for solutions of linear equations and inequalities

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Abstract

Given a system of linear equations and inequalities inn variables, a famous result due to A. J. Hoffman (1952) says that the distance of any point in ℝn to the solution set of this system is bounded above by the product of a positive constant and the absolute residual. We shall discuss explicit representations of this constant in dependence upon the pair of norms used for the estimation. A method for computing a special form of Hoffman constants is proposed. Finally, we use these results in the analysis of Lipschitz continuity for solutions of parametric quadratic programs.

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References

  • Al-Sultan KS, Murty KG (1992) Exterior point algorithms for nearest points and convex quadratic programs. Math Programming 57:145–161

    Google Scholar 

  • Beer K, Käschel J (1979) Column generation in quadratic programming. Math Operationsforsch Stat, Series Optimization 10:179–184

    Google Scholar 

  • Bergthaller C, Singer I (1992) The distance to a polyhedron. Linear Algebra Appl 169:111–129

    Google Scholar 

  • Cook W, Gerards MH, Schrijver A, Tardos E (1986) Sensitivity theorems in integer linear programming. Math Programming 34:251–264

    Google Scholar 

  • Eaves BC (1971) On quadratic programming. Management Sci 17:698–711

    Google Scholar 

  • Fujishige S, Zhan P (1990) A dual algorithm for finding the minimum-norm point in a polytope. J Oper Res Soc Japan 33:188–195

    Google Scholar 

  • Guddat J (1976) Stability in convex quadratic programming. Math Operationsforsch Stat 7:223–245

    Google Scholar 

  • Hoffman AJ (1952) On approximate solutions of systems of linear inequalities. J Res Nat Bur Standards 49:263–265

    Google Scholar 

  • Jongen HT, Klatte D, Tammer K (1990) Implicit functions and sensitivity of stationary points. Math Programming 49:123–138

    Google Scholar 

  • Kall P (1976) Stochastic linear programming. Springer Berlin Heidelberg New York

    Google Scholar 

  • Klatte D (1983) Eine Bemerkung zur parametrischen quadratischen Optimierung. Seminarbericht 50:174–185. Sektion Mathematik Humboldt-Universität Berlin

    Google Scholar 

  • Klatte D (1984) Beiträge zur Stabilitätsanalyse nichtlinearer Optimierungsprobleme. Dissertation B (Habilitationsschrift), Sektion Mathematik Humboldt-Universität Berlin

    Google Scholar 

  • Klatte D (1985) On the Lipschitz behavior of optimal solutions in parametric problems of quadratic optimization and linear complementarity. Optimization 16:819–831

    Google Scholar 

  • Klatte D (1987) Lipschitz continuity of infima and optimal solutions in parametric optimization: The polyhedral case. In: Guddat J, Jongen HT, Kummer B, Nožička F (eds) Parametric Optimization and Related Topics 229–248. Akademie-Verlag Berlin

    Google Scholar 

  • Klatte D, Kummer B (1985) Stability properties of infima and optimal solutions of parametric optimization problems. In: Demyanov V, Pallaschke D (eds) Nondifferentiable Optimization: Motivations and Applications 215–229. Springer Berlin

    Google Scholar 

  • Klatte D, Thiere G (1988) Über Fehlerschranken für lineare Ungleichungssysteme. Wissenschaftliche Zeitschrift der PH Halle-Köthen XXVI 8:13–17

    Google Scholar 

  • Klatte D, Thiere G (1994) A note on Lipschitz constants for solutions of linear inequalities and equations. Manuscript Institut für Operations Research Universität Zürich

    Google Scholar 

  • Kleinmann P (1978) Quantitative Sensitivitätsanalyse bei parametrischen Optimierungsaufgaben. Seminarbericht Nr. 9 Sektion Mathematik Humboldt-Universität Berlin

    Google Scholar 

  • Künzi HP, Krelle W (1962) Nichtlineare Programmierung. Springer Berlin Göttingen Heidelberg

    Google Scholar 

  • Li W (1993) The sharp Lipschitz constants for feasible and optimal solutions of a perturbed linear program. Linear Algebra Appl 187:15–40

    Google Scholar 

  • Li W (1994) Sharp Lipschitz constants for basic optimal solutions and basic feasible solutions of linear programs. SIAM J Control Optim. 32:140–153

    Google Scholar 

  • Luo ZQ, Tseng P (1991) Perturbation analysis of a condition number of linear systems. Manuscript Dept of Mathematics Univ of Washington Seattle, Washington USA

    Google Scholar 

  • Mangasarian OL (1981) A condition number of linear inequalities and equalities. Methods of Oper Res 43:3–15

    Google Scholar 

  • Mangasarian OL (1981a) A stable theorem of the alternative: An extension of the Gordan theorem. Linear Algebra and Appl 41:209–223

    Google Scholar 

  • Mangasarian OL (1990) Error bounds for nondegenerate monotone linear complementarity problems. Math Programming 48:437–445

    Google Scholar 

  • Mangasarian OL, Shiau TH (1986) Error bounds for monotone linear complementarity problems. Math Programming 36:81–89

    Google Scholar 

  • Mangasarian OL, Shiau TH (1987) Lipschitz continuity of solutions of linear inequalities, programs and complementarity problems. SIAM J Control Optim 25:583–595

    Google Scholar 

  • Murty KG, Fathi Y (1982) A critical index algorithm for the nearest point problem on simplicial cones. Math Programming 23:206–215

    Google Scholar 

  • Nožička F, Guddat J, Hollatz H (1972) Theorie der linearen Optimierung. Akademie-Verlag Berlin

    Google Scholar 

  • Robinson SM (1973) Bounds for error in the solution set of a perturbed linear program. Linear Algebra Appl 6: 69–81

    Google Scholar 

  • Robinson SM (1981) Some continuity properties of polyhedral multifunctions. Math Programming Study 14:206–214

    Google Scholar 

  • Schwartz B, Käschel J (1981) Verfahren der zulässigen Richtungen für Optimierungsaufgaben mit nichtdifferenzierbaren Funktionen und Dekomposition. Wiss Schriftenreihe TH Karl-Marx-Stadt Nr 11

  • Sekitani K, Yamamoto Y (1993) A recursive algorithm for finding the minimum norm point in a polytope and a pair of closest points in two polytopes. Math Programming 61:233–249

    Google Scholar 

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

    Google Scholar 

  • Thiere G (1989) Untersuchungen zum Vergleich und zur Berechnung von Fehlerschranken für lineare Ungleichungssysteme. Dissertation A (PhD Thesis) Pädagogische Hochschule Halle-Köthen

  • Walkup D, Wets RJ-B (1969) A Lipschitzian characterization of convex polyhedra. Proceed Amer Math Soc 23:167–173

    Google Scholar 

  • Wolfe P (1976) Finding the nearest point in a polytope. Math Programming 2:126–149

    Google Scholar 

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Klatte, D., Thiere, G. Error bounds for solutions of linear equations and inequalities. ZOR - Mathematical Methods of Operations Research 41, 191–214 (1995). https://doi.org/10.1007/BF01432655

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