Can’t find global minimizer from Lagrange Multiplier Rule

lagrange multiplieroptimization

I have an equality constraint function h and a function f to minimize.
\begin{array}{c}{h : \mathbb{R}^{2} \rightarrow \mathbb{R}, (x,y) \mapsto 2 x-y} \\ {f : \mathbb{R}^{2} \rightarrow \mathbb{R},(x, y) \mapsto 100\left(y-x^{2}\right)^{2}+(1-x)^{2}}\end{array}
By applying Lagrange Multiplier Rule, only single solution x=1 and y =2 is found to satisfy first order necessary condition. But when I check x=0 and y = 0, it also satisfy equality constraint h and even get smaller function value than solution(x=1 and y=2) from Lagrange Multiplier Rule. Does this mean that we can't rely on Lagrange Multiplier Rule to find global minimizer?

The Solution is as follow:

since $\nabla h(x) \neq 0, \nabla h(x)$ is linear independent and thus every $x \in \mathbb{R}^{2}$ with $h(x)=0$ is a
regular point.
Assume that $x$ satisfies the necessary first order condition for a local mimizer of $f$
subject to the constraint $h(x)=0 .$ Then

$$
\nabla f(x)+\lambda \nabla h(x)=0
$$

for a λ ∈ R. Because
$$
\nabla f(x)=\left(\begin{array}{c}{-400 x\left(y-x^{2}\right)+2 x-2} \\ {200\left(y-x^{2}\right)}\end{array}\right)
$$

this implies

$$
0=\left(\begin{array}{c}{-400 x\left(y-x^{2}\right)+2 x-2+2 \lambda} \\ {200\left(y-x^{2}\right)-\lambda}\end{array}\right)
$$

Hence
$$
\lambda=200\left(y-x^{2}\right)
$$

This implies, together with the previous equation,
$$
0=-2 \lambda x+2 x-2+2 \lambda=2\left(x(1-\lambda)-(1-\lambda)\right)
$$

Hence
$$
x=1
$$

since $h(x)=0,$ this implies
$y=2$

Best Answer

You forgot another possible case. From $$0=2(x(1-\lambda)-(1-\lambda))=2(x-1)(1-\lambda),$$ it does not necessarily follow that $x=1$. Namely, if $\lambda=1$, we can have $x\neq 1$. We then get $$y-x^2=\frac{1}{200},\hspace{0.5cm} 2x-y=0$$ and thus $$x^2+\frac{1}{200}=2x\Leftrightarrow (x-1)^2=\frac{199}{200}\Leftrightarrow x=1\pm\sqrt{\frac{199}{200}}$$ and therefore $$y=2\pm\sqrt{\frac{199}{50}}.$$

Plugging these two pairs of values into $f$ yields (in both cases) a value of $\frac{399}{400}$, which is the global minimizer since $\frac{399}{400}<f(1,2)=100$. This is also smaller than $f(0,0)=1$

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