# Lagrange multipliers intuition and derivation

May 10, 2022 2 min read

Lagrange multipliers are ubiquitous in optimization theory and natural sciences, such as mechanics and statistical physics. Here I work out its intuition and derivation.

### Lagrange multipliers in 2D case

Suppose that you need to find a maximum/minimum of a function $f(x, y)$, constrained on condition $g(x, y) = c$.

Direct solution of such a system would be as follows: using $g(x, y) = c$ condition we would express $y$ through $x$ as $y = h(x)$, substitute this $y$ into $f(x, y) = f(x, h(x))$ and find the optimum of $f(x, h(x))$ in just variable, $x$.

Lagrange multipliers provide a convenient alternative to this straightforward solution in the following form:

$\begin{cases} \nabla_{x, y} f(x, y) = \lambda \nabla_{x, y} g(x, y) \\ g(x, y) = c \end{cases} \iff \begin{cases} \frac{\partial f(x, y)}{\partial x} = \lambda \frac{\partial g(x, y)}{\partial x} \\ \frac{\partial f(x, y)}{\partial y} = \lambda \frac{\partial g(x, y)}{\partial y} \\ g(x, y) = c \end{cases}$

So, instead of substituting $y$, we introduce a new variable $\lambda$ and solve a system of 3 equations in 3 variables.

Why the solution of this new system is equivalent to the direct method?

The interpretation of these conditions is as follows: let us draw the levels of $f(x, y)$.

At the point of $g(x, y) = c$ curve, the gradient $\nabla f(x, y)$ should be orthogonal to the curve $g(x, y) = c$, otherwise, it would have continued to move along the $g(x, y) = c$ curve.

See illustration of 2D case:

In 3D case similarly we optimize a function $f(x, y, z)$ constrained on two conditions $g_1(x, y, z) = c_1$ and $g_2(x, y, z) = c_2$.

In this case intersection of curves $g_1(x, y, z) = c_1$ and $g_2(x, y, z) = c_2$ is approximated by a line.

Again, $\nabla f(x, y, z)$ should be orthogonal to this intersection line. See illustration in 3D case:

## References:

- https://math.stackexchange.com/questions/2578903/lagrange-multipliers-tangency - good stack overflow post on Lagrange multipliers derivation

Written by **Boris Burkov** who lives in Moscow, Russia, loves to take part in development of cutting-edge technologies, reflects on how the world works and admires the giants of the past. You can follow me in Telegram