Abstract

A standard approach to the minimization of a constrained objective function in the presence of equality constraints in Mathematical Programming or of a state equation in Control Theory is to introduce Lagrange multipliers or an adjoint state. The initial minimization problem is equivalent to the minimax of the associated Lagrangian. In this paper, the targeted application is the computation of shape and topological derivatives (Sensitivity Analysis) where the control variable belongs to a metric space of geometries. In that context, it is natural to consider the right-hand side differentiability of the minimax of a Lagrangian with respect to a positive parameter as a first step towards the computation of semidifferentials and differentials on a metric space. By using the new notion of averaged adjoint introduced by K. Sturm in two recent publications [On shape optimization with non-linear partial differential equations, Doctoral thesis, Technische Universität Berlin, Germany (2014); Minimax Lagrangian approach to the differentiability of non-linear PDE constrained shape functions without saddle point assumption, SIAM J. Control Optimization 53 (2015) 2017–2039], the minimax problem need not be related to a saddle point as in a paper by R. Correa and A. Seeger [Directional derivatives of a minimax function, Nonlinear Anal. Theory Methods Appl. 9 (1985) 13–22] and the so-called dual problem need not make sense. We extend Sturm's results from the single valued case to the case where the solutions of the state/averaged adjoint state equations are not unique. In such a case, non-differentiability can occur even if the functions at hand are infinitely differentiable as was illustrated by the seesaw problem in the seminal paper of J. M. Danskin [The theory of max-min, with applications, SIAM J. Applied Mathematics 14(4) (1966) 641–644].

Author information

Contact details are reproduced from the original publication and may be historical.

Michel C. Delfour

Dép. de Mathématiques et de Statistique, Université de Montréal, CP 6128 / succ. Centre-ville, Montréal, Quebec, Canada H3C 3J7
and: Centre de Recherches Mathématiques, Université de Montréal, CP 6128, succ. Centre-ville, Montréal, Canada H3C 3J7

delfour@crm.umontreal.ca

Kevin Sturm

Fakultät für Mathematik, Universität Duisburg-Essen, Thea-Leymann-str. 9, 45127 Essen, Germany

kevin.sturm@uni-due.de

Keywords

  • Minimax
  • Lagrangian
  • sensitivity analysis
  • semidifferential
  • averaged adjoint
  • shape
  • topological derivatives
  • optimal control
  • mathematical programming

Mathematics Subject Classification

49K20, 49K27, 49K35, 49K40, 49Q10, 49Q12

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Suggested citation

M. C. Delfour, K. Sturm. “Parametric Semidifferentiability of Minimax of Lagrangians: Averaged Adjoint Approach.” Journal of Convex Analysis 24 (2017), No. 4, 1117–1142. https://doi.org/10.68381/jca24066

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