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I've been trying to understand the principles behind the Lagrangian multipliers and I think I've got a rough understanding of it. Would appreciate it if you guys could help me answer a few questions!

I've pretty much self-studied this from here and here.

As far as I understand it, the Lagrangian multiplier essentially works by ensuring the gradient of the function is equal to the gradient of the restraint. Assuming that $g(x,y) = c$ is the restraint, it also ensures the point satisfies the restraint.

However, I don't understand the reasoning behind some of the statements in the ideashop link:

1) "The most important thing to know about gradients is that they always point in the direction of a function's steepest slope at a given point." . Apparently a gradient is a collection of partial first derivatives but I don't understand how one gets from a collection of partial first derivatives to 'Always pointing in the direction of the steepest slope'. Wouldn't it be changing direction as one went around a 3D surface?

2) Why is the gradient always perpendicular to the level curve?

3) How does one get from $$\nabla L=\begin{bmatrix}\frac{\partial f}{\partial x_1}-\lambda\frac{\partial g}{\partial x_1}\\\frac{\partial f}{\partial x_2}-\lambda\frac{\partial g}{\partial x_2}\\g(x_1,x_2)-c\end{bmatrix}=0$$ to $$L(x_1,x_2,\lambda)=f(x_1,x_2)-\lambda(g(x_1,x_2)-c)$$ (Both sourced from ideashop) ?

Thank you for your help in advance!

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3 Answers 3

The most intuitive way I've found to understand it is to think of the Lagrangian formulation as a computationally more tractable way of applying the implicit function theorem. It requires more mental effort than just pulling a lagrangian out of thin air and chugging through proofs, but it gets to the same place and ultimately I think it provides a better motivation.

The basic idea is this: you eliminate the constraint by composing the cost functional with the solution operator to the constraint equation. Then you can take derivatives and find extrema of the composed system by using the implicit function theorem. The system of equations you have to solve based on a naieve application of the implicit function theorem is wasteful, and a smart simplification to the system yields the lagrange multipliers in a natural way.

Suppose you have

  • a cost functional $F:X \rightarrow \mathbb{R}$,
  • a parameterized constraint function $G:Q \times X \rightarrow X$, ($q \in Q$ is the parameter)
  • a solution operator $S:Q \rightarrow X$ such that $G(q,S(q))=b$ for any given $q\in Q$,

and you want to find the sensitivity of $F$ with respect to the parameter $q$, going through the solution operator: $$\frac{d}{dq} F(S(q)).$$

In your case, the spaces and operators are

  • $X=\mathbb{R}^2$, $Q=\mathbb{R}^1$,
  • $F(x_1,x_2)=f(x_1,x_2)$,
  • $G(q,(x_1,x_2))=(g(x_1,x_2), q-x_1)^T$
  • $b=(c,0)^T$.

The implicit function theorem tells us how to compute this total derivative in terms of partial derivatives of things we know: $$\frac{d}{dq} F(S(q)) \cdot \delta q = F^{'}_x(S(q)) \circ [G^{'}_x (q,S(q))]^{-1} \circ G^{'}_q (q,S(q)) \cdot \delta q.$$

This is theoretically all you need to find the critical points for a smooth optimization problem, but there is a major issue: to use it you need to compute the matrix inverse $[G^{'}_x (q,S(q))]^{-1}$, which may be very difficult!

On the other hand, computing the full inverse is wasteful since $[G^{'}_x ]^{-1}:X \rightarrow X$ (n-by-n matrix), whereas the operator on it's left is $F^{'}_x : X \rightarrow \mathbb{R}$ (1-by-n vector). We can safely ignore the action of $[G^{'}_x ]^{-1}$ in any direction that is in the kernel of $F^{'}_x$, since vectors in that kernel get sent to zero anyways.

Thus is it natural to look for a Riesz-representor $\lambda$ for the combined operator $F_x \circ [G^{'}_x]^{-1}: X \rightarrow \mathbb{R}$. Ie, a vector $\lambda$ such that $$F_x \circ [G^{'}_x]^{-1} \cdot x = (\lambda,x) ~~~ \forall x \in X$$ Or turning the equation around, we want $\lambda$ that solves $$G^{'}_x \lambda = F^{'}_x,$$ the familiar lagrange multiplier equation. We have to solve a n-by-n system once for a single right hand side, rather than fully inverting it for any possible right hand side. Then evaluating the implicit derivatives becomes much simpler, with the familiar formula $$\frac{d}{dq} F(S(q)) \cdot \delta q = (\lambda, G^{'}_q (q,S(q)) \cdot \delta q).$$

Thus the "Lagrangian" can be seen as a convenient shorthand for the object whose gradient gives you the correct equations for computing implicit derivatives efficiently.

(As an aside, there is also a completely different motivation for the lagrangian from game theory - hopefully someone else will post more about it.)

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I'll explain it in a three-dimensional setting. This means a priori you are given a "temperature" function $f:\,{\mathbb R}^3\to{\mathbb R}$.

Consider a fixed point ${\bf p}\in{\mathbb R}^3$ and a point $t\mapsto{\bf x}(t)$ $\ (t\geq0)$ moving away from ${\bf p}$ in direction ${\bf u}\,$; so ${\bf x}(0)={\bf p}$, $\,{\bf x}'(0)={\bf u}$. The temperature at the moving point is a function of $t$ and is given by $\phi(t):=f\bigl({\bf x}(t)\bigr)$. According to the chain rule we have $$\phi'(0)=\nabla f\bigl({\bf x}(0)\bigr)\cdot{\bf x}'(0)=\nabla f({\bf p})\cdot{\bf u}\ .\qquad(1)$$ If $\nabla f({\bf p})\ne{\bf 0}$ then there will always be direction vectors ${\bf u}$ such that the scalar product on the right is positive and other such vectors where it is negative. In this case the temperature $f$ can neither be locally minimal nor locally maximal at ${\bf p}$. In particular, for $|{\bf u}|=1$ the increase $\phi'(0)$ is maximal ($=|\nabla f({\bf p})|$) if ${\bf u}$ points in the direction of $\nabla f({\bf p})$, the temperature remains stationary, i.e., $\phi'(0)=0$, for directions ${\bf u}\perp\nabla f({\bf p})$, and decreases fastest in the direction $-\nabla f({\bf p})$.

When the moving point stays on the isothermal surface $S_f$ through ${\bf p}$ then $\phi'(0)=0$. Looking at $(1)$ we see that all "$f$-isothermal tangent directions" are orthogonal to $\nabla f({\bf p})$. This implies that $\nabla f({\bf p})$ spans the orthogonal complement of the tangent plane $T_{f,{\bf p}}$ of $S_f$.

Assume now that in addition to $f$ we have a constraint $g({\bf x})=0$ defining an "admissible" surface $S_g\subset{\mathbb R}^3$, and assume that our point ${\bf p}$ satisfies the constraint. In this case only "$g$-isothermal tangent directions" ${\bf u}$ are allowed for the moving point.

If ${\bf p}$ aspires to be a conditionally extremal point, whence a conditionally stationary point, of $f$ then we should have $\phi'(0)=0$ for all allowed directions. This means by $(1)$ that $\nabla f({\bf p})$ should be orthogonal to all "$g$-isothermal tangent directions", or that $\nabla f({\bf p})$ is in the orthogonal complement of the tangent plane $T_{g,{\bf p}}$. As the latter is spanned by $\nabla g({\bf p})$ (assumed $\ne{\bf 0}$) we would have an equality of the form $\nabla f({\bf p})=\lambda\, \nabla g({\bf p})$ for some $\lambda\in{\mathbb R}$. This in term implies that the point ${\bf p}$ will come to the fore when we solve the equations $$\nabla f({\bf x})=\lambda\, \nabla g({\bf x}),\quad g({\bf x})=0\qquad(2)$$ for ${\bf x}$ (and $\lambda$).

Applying the "Lagrange method" means no more and no less than solving the equations $(2)$.

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Could you extend your explanation to multiple admissible surfaces? –  SPRajagopal Sep 11 '14 at 9:08
@SPRajagopal: I suggest you ask a question explaining what you have in mind. I don't know what a "multiple surface" is. –  Christian Blatter Sep 11 '14 at 11:58

1) You're exactly right. The gradient must be evaluated at a given coordinate, to give a vector pointing in the direction of greatest change "starting from" that point. The key is to remember that a derivative is a function, and the gradient a "vector of functions", so you can "move it around" by evaluating the component functions at different points. And the surface you're dealing with can be shaped differently at different points, so the direction of greatest change can vary. The gradient keeps track of this.

2) Here is a nice explanation of this point.

3) Things seem reversed here. If you compute the gradient of $L(x_1,x_2,\lambda)=f(x_1,x_2)-\lambda (g(x_1,x_2)-c)$ you get the vector $\nabla L$ as above. I.e. you must compute $\partial L/\partial x_1 = \frac{\partial f}{\partial x_1}(x_1,x_2) - \lambda\frac{\partial g}{\partial x_1},\partial L/\partial x_2 = \frac{\partial f}{\partial x_2}(x_1,x_2) - \lambda\frac{\partial g}{\partial x_2},$ and $\partial L/\partial\lambda = -(g(x_1,x_2)-c).$ Then the condition that must be satisfied is $\nabla L=0.$

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