Abstract
When every feasible stable perturbation of data results in a non-improvement of the optimal value function, then we talk about an ‘optimal input’ or an ‘optimal selection of data”. In this paper we describe such data for convex programs using perturbed saddle points.
Similar content being viewed by others
References
R. Abrams and L. Kerzner, “A simplified test for optimality”,Journal of Optimization Theory and Applications 25 (1978), 161–170.
M. Avriel,Nonlinear programming: Analysis and methods, Prentice-Hall Series in Automatic Computation (Prentice-Hall, Englewood Cliffs, 1976).
M.S. Bazaraa and C.M. Shetty,Foundations of optimization (Springer, New York, 1976).
A. Ben-Israel, A. Ben-Tal and S. Zlobec,Optimality in nonlinear programming: A feasible directions approach (Wiley-Interscience, New York, 1981).
A. Ben-Tal and S. Zlobec, “Convex programming and the lexicographic multicriteria problem’.Mathematische Operationsforchung und Statistik, Series Optimization 8 (1977) 61–73.
B. Brosowski, “On parametric linear optimization”, in: R. Henn, B. Korte and W. Oettli, eds.,Optimization and operations research, Lecture Notes in Economics and Mathematical Systems, Vol. 157 (Springer, Berlin 1978) pp. 37–44.
G.B. Dantzig, J. Folkman and N. Shapiro, “On the continuity of the minimum set of a continuous function”,Journal of Mathematical Analysis and Applications 17 (1967), 519–548.
I.I. Eremin and N.N. Astafiev,Introduction to the theory of linear and convex programming (Nauka, Moscow, 1976), [In Russian.]
J.P. Evans and F.J. Gould, “Stability in nonlinear programming”,Operations Research 18 (1970) 107–118.
A.V. Fiacco, “Convergence properties of local solutions of sequences of mathematical programming problems in general spaces”,Journal of Optmization Theory and Applications 13 (1974) 1–12.
J. Gauvin, “The generalized gradient of a marginal function in mathematical programming”,Mathematics of Operations Research 4 (1979) 458–463.
B. Gollan, “On the marginal function in nonsmooth optimization”, Preprint No. 69, Mathematisches Institut der Julius-Maximilians-Universität Würzburg (October 1980).
E.G. Gol'stein, “Duality theory in mathematical programming and its applications (Nauka, Moscow, 1971). [In Russian; also translation published by Akademie-Verlag, Berlin, 1975].
H.J. Greenberg and W.P. Pierskalla, “Extensions of the Evans-Gould stability theorems for mathematical programs”,Operations Research 20 (1972) 143–153.
J. Guddat, “Stability in convex quadratic parametric programming”,Mathematische Operationsforschung und Statistik 7 (1976) 223–245.
M. Guignard, “Generalized Kuhn-Tucker conditions for mathematical programming problems in a Banach space”,SIAM Journal on Control 7 (1969) 232–241.
J.-B. Hiriart-Urruty, “Tangent cones, generalized gradients and mathematical programming in Banach spaces”,Mathematics of Operations Research 1 (1979) 79–97.
W. Krabs, “Stetige Abänderung der Daten bei nichtlinearer Optimierung und ihre Konsequenzen”,Operations Research Verfahren XXV 1 (1977) 93–113.
M.A. Krasnosel'skii, G.M. Vainikko, P.P. Zabreiko, Ya.B. Rutitskii and U.Ya. Stetsenko,Approximate solution of operator equations (Wolters-Noordhoff, Groningen, 1972).
O.L. Mangasarian,Nonlinear programming (McGraw-Hill, New York, 1969).
D.H. Martin, “On the continuity of the maximum in parametric linear programming”,Journal of Optimization Theory and Applications 17 (1975) 205–210.
M.Z. Nashed, “Perturbation analysis of ill-posed problems”, Presented at the Third Symposium on Mathematical Programming with Data Perturbations, The George Washington University. Washington, D.C. (May 21–22, 1981).
F. Nožiĉka, J. Guddat, H. Hollatz and B. Bank,Theorie der linearen parametrische Optimierung (Akademie-Verlag, Berlin, 1974).
V.V. Podinovskii and V.M. Gavrilov,Optimization with respect to successive criteria (Soviet Radio, Moscow, 1975). [In Russian].
S.M. Robinson, “A characterization of stability in linear programming”, MRC Technical Report 1542, University of Wisconsin, Madison (1975).
R.T. Rockafellar,Convex analysis (Princeton University Press, 1970).
I. Stakgold, “Branching of solutions of nonlinear equations”,SIAM Review 13 (1971) 289–332.
M.M. Vainberg and V.A. Trenogin,The theory of branching of solutions of non-linear equations (Noordhoff, Leyden, 1974).
A.C. Williams, “Marginal values in linear programming”,Journal of the Society of Industrial and Applied Mathematics 11 (1963) 82–94.
H. Wolkowicz, “Calculating the cone of directions of constancy”,Journal of Optimization Theory and Applications 25 (1978) 451–457.
S. Zlobec and A. Ben-Israel, “Perturbed convex programs: continuity of optimal solutions and optimal values”,Operations Research Verfahren XXXI 1 (1979), 737–749.
S. Zlobec and A. Ben-Israel, “Duality in convex programming: a linearization approach”,Mathematische Operationsforschung und Statistik, Series Optimization 10 (1979) 171–178.
S. Zlobec and B. Craven, “Stabilization and calculation of the minimal index set of binding constraints in convex programming”,Mathematische Operationsforschung und Statistik, Series Optimization 12 (1981), 203–220.
S. Zlobec, R. Gardner and A. Ben-Israel, “Regions of stability for arbitrarily perturbed convex programs”, in: A. Fiacco, ed.,Mathematical programming with data perturbation (M. Dekker, New York, 1981) pp. 69–89.
S. Zlobec, “Regions of stability for ill-posed convex programs”,Aplikace Matematiky 27 (1981).
S. Zlobec, “Optimal selection of data in convex programming”, Department of Mathematics, McGill University, Montreal, Quebec, Canada (February 1981).
S. Zlobec, “Marginal values for stable perturbations in convex optimization”,Glasnik Matematički (1982).
Author information
Authors and Affiliations
Additional information
Research partly supported by Natural Sciences and Engineering Council of Canada and le Ministère de l'Education du Québec (F.C.A.C.).
Presented in part at the Third Symposium on Mathematical Programming with Data Perturbations, The George Washington University, Washington, D.C. (May 21–22, 1981).
Rights and permissions
About this article
Cite this article
Zlobec, S. Characterizing an optimal input in perturbed convex programming. Mathematical Programming 25, 109–121 (1983). https://doi.org/10.1007/BF02591721
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02591721