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- Direction
- Convex & Concave Function
- Jensen's Inequality
- Differentiable Convex Function
- Sufficient & Necessary Conditions for Global Optima
- Quasiconvex & Quasiconcave functions
- Differentiable Quasiconvex Function
- Strictly Quasiconvex Function
- Strongly Quasiconvex Function
- Pseudoconvex Function
- Convex Programming Problem
- Fritz-John Conditions
- Karush-Kuhn-Tucker Optimality Necessary Conditions
- Algorithms for Convex Problems
Quasiconvex and Quasiconcave functions
Let f:S→R where S⊂Rn is a non-empty convex set. The function f is said to be quasiconvex if for each x1,x2∈S, we have f(λx1+(1−λ)x2)≤max{f(x1),f(x2)},λ∈(0,1)
For example, f(x)=x3
Let f:S→R where S⊂Rn is a non-empty convex set. The function f is said to be quasiconvex if for each x1,x2∈S, we have f(λx1+(1−λ)x2)≥min{f(x1),f(x2)},λ∈(0,1)
Remarks
- Every convex function is quasiconvex but the converse is not true.
- A function which is both quasiconvex and quasiconcave is called quasimonotone.
Theorem
Let f:S→R and S is a non empty convex set in Rn. The function f is quasiconvex if and only if Sα=(x∈S:f(x)≤α} is convex for each real number \alpha$
Proof
Let f is quasiconvex on S.
Let x1,x2∈Sα therefore x1,x2∈S and max{f(x1),f(x2)}≤α
Let λ∈(0,1) and let x=λx1+(1−λ)x2≤max{f(x1),f(x2)}⇒x∈S
Thus, f(λx1+(1−λ)x2)≤max{f(x1),f(x2)}≤α
Therefore, Sα is convex.
Converse
Let Sα is convex for each α
x1,x2∈S,λ∈(0,1)
x=λx1+(1−λ)x2
Let x=λx1+(1−λ)x2
For x1,x2∈Sα,α=max{f(x1),f(x2)}
⇒λx1+(1−λ)x2∈Sα
⇒f(λx1+(1−λ)x2)≤α
Hence proved.
Theorem
Let f:S→R and S is a non empty convex set in Rn. The function f is quasiconcave if and only if Sα={x∈S:f(x)≥α} is convex for each real number α.
Theorem
Let f:S→R and S is a non empty convex set in Rn. The function f is quasimonotone if and only if Sα={x∈S:f(x)=α} is convex for each real number α.