## Introduction

The following section is about constrained optimization and lagrange multipliers. For more mathematical topics checkout other list.

## Prerequisites

## Lagrange multipliers Introduction

The Lagrange multiplier technique lets you find the maximum or minimum of a multivariable function $f(x, y, \dots)$ when there is some constraint $\color{red}{g(x,y,\dots)}$ on the input values $x$, $y$, $\dots$ you are allowed to use.

the gradient of $f$ evaluated at a point $(x_0, y_0)$ always gives a vector perpendicular to the contour line passing through that point. This means when the contour lines of two functions $\color{blue}f$ and $\color{red}g$ are tangent, their gradient vectors are parallel. This tangency means their gradient vectors align:

**Tangency Condition**
$$
\label{tangency}
\nabla \color{blue}{f(x_0,y_0)} = \color{green}{\lambda_0} \nabla \color{red}{g(x_0,y_0)}
$$

Here, $\color{green}{\lambda}$ represents some constant. Some authors use a negative constant, $-\color{green}{\lambda}$.

Let’s see what this looks like in our example where $\color{blue}{f(x,y)=2x+y}$ and $\color{red}{g(x,y)=x^2+y^2}$. The gradient of $f$ is

\[\nabla f(x,y) = \begin{bmatrix} \frac{\partial}{\partial x}(2x+y) \\ \frac{\partial}{\partial y}(2x+y) \end{bmatrix} = \begin{bmatrix} 2 \\ 1 \end{bmatrix}\]and the gradient of $g$ is

\[\nabla g(x,y) = \begin{bmatrix} \frac{\partial}{\partial x}(x^2+y^2) \\ \frac{\partial}{\partial y}(x^2+y^2) \end{bmatrix} = \begin{bmatrix} 2x \\ 2y \end{bmatrix}\]Therefore, the tangency condition from equation $\ref{tangency}$ ends up looking like this:

\[\begin{bmatrix} 2 \\ 2 \end{bmatrix} = \lambda \begin{bmatrix} 2x \\ 2y \end{bmatrix}\]In the example from above, this leads to the following three equations and two three unknowns $x_0$, $y_0$ and $\lambda_0$.

\[\color{red}{x_0^2 + y_0^2 = 1} \\\] \[\begin{align} 2 &= 2\color{green}{\lambda_0} x_0 \Rightarrow x_0 = \frac{1}{\lambda_0}\\ 1 &= 2\color{green}{\lambda_0} y_0 \Rightarrow y_0 = \frac{1}{2\lambda_0} \\ \end{align}\]Plugging $x_0$ and $y_0$ into the third equation results in a quadratic equation with two solutions for $\lambda_0$

\[\lambda_0 = \pm\frac{\sqrt{5}}{2}\]This leads to two solutions for the input pair $(x_0,y_0)$ which can be plugged into the original function $f(x,y)$. One input leads to the minimum and the other to the maximum value of $f$.

In general the we can write these conditions by saying we are looking for constants $x_0$, $y_0$ and $\lambda_0$ that satisfy the constraint and the tangency condition \ref{tangency}.

Joseph Louis Lagrange wrote down a special new function which takes in all the same input variables as $f$ and $g$, along with the new kid in town $\lambda$, thought of now as a variable rather than a constant.

**The Lagrangian**
$$
\mathcal{L}(x,y,\dots,\color{green}{\lambda}) = \color{blue}{f(x,y,\dots)} - \color{green}{\lambda}(\color{red}{g(x,y,\dots) - c})
$$
with the Lagrange multiplier $\color{green}{\lambda}$.

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