Neha Patil (Editor)

K convex function

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K-convex functions, first introduced by Scarf, are a special weakening of the concept of convex function which is crucial in the proof of the optimality of the ( s , S ) policy in inventory control theory. The policy is characterized by two numbers s and S, S ≥ s , such that when the inventory level falls below level s, an order is issued for a quantity that brings the inventory up to level S, and nothing is ordered otherwise.

Contents

Definition

Two equivalent definitions are as follows:

Definition 1 (The original definition)

A function g : R → R is K-convex if

g ( u ) + z [ g ( u ) − g ( u − b ) b ] ≤ g ( u + z ) + K

for any u , z ≥ 0 , and b > 0 .

Definition 2 (Definition with geometric interpretation)

A function g : R → R is K-convex if

g ( λ x + λ ¯ y ) ≤ λ g ( x ) + λ ¯ [ g ( y ) + K ]

for all x ≤ y , λ ∈ [ 0 , 1 ] , where λ ¯ = 1 − λ .

This definition admits a simple geometric interpretation related to the concept of visibility. Let a ≥ 0 . A point ( x , f ( x ) ) is said to be visible from ( y , f ( y ) + a ) if all intermediate points ( λ x + λ ¯ y , f ( λ x + λ ¯ y ) ) , 0 ≤ λ ≤ 1 lie below the line segment joining these two points. Then the geometric characterization of K-convexity can be obtain as:

A function g is K-convex if and only if ( x , g ( x ) ) is visible from ( y , g ( y ) + K ) for all y ≥ x .

Proof of Equivalence

It is sufficient to prove that the above definitions can be transformed to each other. This can be seen by using the transformation

λ = z / ( b + z ) , x = u − b , y = u + z .

Property 1

If g : R → R is K-convex, then it is L-convex for any L ≥ K . In particular, if g is convex, then it is also K-convex for any K ≥ 0 .

Property 2

If g 1 is K-convex and g 2 is L-convex, then for α ≥ 0 , β ≥ 0 , g = α g 1 + β g 2 is ( α K + β L ) -convex.

Property 3

If g is K-convex and ξ is a random variable such that E | g ( x − ξ ) | < ∞ for all x , then E g ( x − ξ ) is also K-convex.

Property 4

If g : R → R is a continuous K-convex function and g ( y ) → ∞ as | y | → ∞ , then there exit scalars s and S with s ≤ S such that

  • g ( S ) ≤ g ( y ) , for all y ∈ R ;
  • g ( S ) + K = g ( s ) < g ( y ) , for all y < s ;
  • g ( y ) is a decreasing function on ( − ∞ , s ) ;
  • g ( y ) ≤ g ( z ) + K for all y , z with s ≤ y ≤ z .
  • References

    K-convex function Wikipedia


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