Perturbation function

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In mathematical optimization, the perturbation function is any function which relates to primal and dual problems. The name comes from the fact that any such function defines a perturbation of the initial problem. In many cases this takes the form of shifting the constraints.[1]

In some texts the value function is called the perturbation function, and the perturbation function is called the bifunction.[2]


Given two dual pairs separated locally convex spaces \left(X,X^*\right) and \left(Y,Y^*\right). Then given the function f: X \to \mathbb{R} \cup \{+\infty\}, we can define the primal problem by

\inf_{x \in X} f(x). \,

If there are constraint conditions, these can be built into the function f by letting f = f + I_\mathrm{constraints} where I is the indicator function. Then F: X \times Y \to \mathbb{R} \cup \{+\infty\} is a perturbation function if and only if F(x,0) = f(x).[1][3]

Use in duality[edit]

The duality gap is the difference of the right and left hand side of the inequality

\sup_{y^* \in Y^*} -F^*(0,y^*) \le \inf_{x \in X} F(x,0),

where F^* is the convex conjugate in both variables.[3][4]

For any choice of perturbation function F weak duality holds. There are a number of conditions which if satisfied imply strong duality.[3] For instance, if F is proper, jointly convex, lower semi-continuous with 0 \in \operatorname{core}(\operatorname{Pr}_Y(\operatorname{dom}F)) (where \operatorname{core} is the algebraic interior and \operatorname{Pr}_Y is the projection onto Y defined by \operatorname{Pr}_Y(x,y) = y) and X, Y are Fréchet spaces then strong duality holds.[1]



Let (X,X^*) and (Y,Y^*) be dual pairs. Given a primal problem (minimize f(x)) and a related perturbation function (F(x,y)) then the Lagrangian L: X \times Y^* \to \mathbb{R} \cup \{+\infty\} is the negative conjugate of F with respect to y (i.e. the concave conjugate). That is the Lagrangian is defined by

L(x,-y^*) = \inf_{y \in Y} \left\{F(x,y) - y^*(y)\right\}.

In particular the weak duality minmax equation can be shown to be

\sup_{y^* \in Y^*} -F^*(0,y^*) = \sup_{y^* \in Y^*} \inf_{x \in X} L(x,y^*) \leq \inf_{x \in X} \sup_{y^* \in Y^*} L(x,y^*) = \inf_{x \in X} F(x,0).

If the primal problem is given by

\inf_{x: g(x) \leq 0} f(x) = \inf_{x \in X} \tilde{f}(x)

where \tilde{f}(x) = f(x) + I_{\mathbb{R}^d_+}(-g(x)). Then if the perturbation is given by

\inf_{x: g(x) \leq y} f(x)

then the perturbation function is

F(x,y) = f(x) + I_{\mathbb{R}^d_+}(y - g(x)).

Thus the connection to Lagrangian duality can be seen, as L can be trivially seen to be

L(x,y^*) = \begin{cases} 
 f(x) + y^*(g(x)) & \text{if } y^* \in \mathbb{R}^d_+\\
 -\infty & \text{else}

Fenchel duality[edit]

Main article: Fenchel duality

Let (X,X^*) and (Y,Y^*) be dual pairs. Assume there exists a linear map T: X \to Y with adjoint operator T^*: Y^* \to X^*. Assume the primal objective function f(x) (including the constraints by way of the indicator function) can be written as f(x) = J(x,Tx) such that J: X \times Y \to \mathbb{R} \cup \{+\infty\}. Then the perturbation function is given by

F(x,y) = J(x,Tx - y).

In particular if the primal objective is f(x) + g(Tx) then the perturbation function is given by F(x,y) = f(x) + g(Tx - y), which is the traditional definition of Fenchel duality.[5]


  1. ^ a b c Radu Ioan Boţ; Gert Wanka; Sorin-Mihai Grad (2009). Duality in Vector Optimization. Springer. ISBN 978-3-642-02885-4. 
  2. ^ J. P. Ponstein (2004). Approaches to the Theory of Optimization. Cambridge University Press. ISBN 978-0-521-60491-8. 
  3. ^ a b c Zălinescu, C. (2002). Convex analysis in general vector spaces. River Edge, NJ,: World Scientific Publishing  Co., Inc. pp. 106–113. ISBN 981-238-067-1. MR 1921556. 
  4. ^ Ernö Robert Csetnek (2010). Overcoming the failure of the classical generalized interior-point regularity conditions in convex optimization. Applications of the duality theory to enlargements of maximal monotone operators. Logos Verlag Berlin GmbH. ISBN 978-3-8325-2503-3. 
  5. ^ Radu Ioan Boţ (2010). Conjugate Duality in Convex Optimization. Springer. p. 68. ISBN 978-3-642-04899-9.