# Pseudoconvex Function

Let $f:S\rightarrow \mathbb{R}$ be a differentiable function and S be a non-empty convex set in $\mathbb{R}^n$, then f is said to be pseudoconvex if for each $x_1,x_2 \in S$ with $\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )\geq 0$, we have $f\left ( x_2 \right )\geq f\left ( x_1 \right )$, or equivalently if $f\left ( x_1 \right )>f\left ( x_2 \right )$ then $\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )<0$

## Pseudoconcave function

Let $f:S\rightarrow \mathbb{R}$ be a differentiable function and S be a non-empty convex set in $\mathbb{R}^n$, then f is said to be pseudoconvex if for each $x_1, x_2 \in S$ with $\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )\geq 0$, we have $f\left ( x_2 \right )\leq f\left ( x_1 \right )$, or equivalently if $f\left ( x_1 \right )>f\left ( x_2 \right )$ then $\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )>0$

### Remarks

• If a function is both pseudoconvex and pseudoconcave, then is is called pseudolinear.

• A differentiable convex function is also pseudoconvex.

• A pseudoconvex function may not be convex. For example,

$f\left ( x \right )=x+x^3$ is not convex. If $x_1 \leq x_2,x_{1}^{3} \leq x_{2}^{3}$

Thus,$\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )=\left ( 1+3x_{1}^{2} \right )\left ( x_2-x_1 \right ) \geq 0$

And, $f\left ( x_2 \right )-f\left ( x_1 \right )=\left ( x_2-x_1 \right )+\left ( x_{2}^{3} -x_{1}^{3}\right )\geq 0$

$\Rightarrow f\left ( x_2 \right )\geq f\left ( x_1 \right )$

Thus, it is pseudoconvex.

A pseudoconvex function is strictly quasiconvex. Thus, every local minima of pseudoconvex is also global minima.

## Strictly pseudoconvex function

Let $f:S\rightarrow \mathbb{R}$ be a differentiable function and S be a non-empty convex set in $\mathbb{R}^n$, then f is said to be pseudoconvex if for each $x_1,x_2 \in S$ with $\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )\geq 0$, we have $f\left ( x_2 \right )> f\left ( x_1 \right )$,or equivalently if $f\left ( x_1 \right )\geq f\left ( x_2 \right )$ then $\bigtriangledown f\left ( x_1 \right )^T\left ( x_2-x_1 \right )<0$

## Theorem

Let f be a pseudoconvex function and suppose $\bigtriangledown f\left ( \hat{x}\right )=0$ for some $\hat{x} \in S$, then $\hat{x}$ is global optimal solution of f over S.

### Proof

Let $\hat{x}$ be a critical point of f, ie, $\bigtriangledown f\left ( \hat{x}\right )=0$

Since f is pseudoconvex function, for $x \in S,$ we have

$$\bigtriangledown f\left ( \hat{x}\right )\left ( x-\hat{x}\right )=0 \Rightarrow f\left ( \hat{x}\right )\leq f\left ( x\right ), \forall x \in S$$

Hence, $\hat{x}$ is global optimal solution.

### Remark

If f is strictly pseudoconvex function, $\hat{x}$ is unique global optimal solution.

## Theorem

If f is differentiable pseudoconvex function over S, then f is both strictly quasiconvex as well as quasiconvex function.

### Remarks

• The sum of two pseudoconvex fucntions defined on an open set S of $\mathbb{R}^n$ may not be pseudoconvex.

• Let $f:S\rightarrow \mathbb{R}$ be a quasiconvex function and S be a non-empty convex subset of $\mathbb{R}^n$ then f is pseudoconvex if and only if every critical point is a global minima of f over S.

• Let S be a non-empty convex subset of $\mathbb{R}^n$ and $f:S\rightarrow \mathbb{R}$ be a function such that $\bigtriangledown f\left ( x\right )\neq 0$ for every $x \in S$ then f is pseudoconvex if and only if it is a quasiconvex function.