Maximizing the Lagrangian with Constraints to Solving Problems

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Homework Statement

L = - $\Sigma$ _x,y (P(x,y) log P(x,y)) + $$\lambda$$ $\Sigma$_y (P(x,y) - q(x))

This is the Lagrangian. I need to maximize the first term in the sum with respect to P(x,y), subject to the constraint in the second term.

The first term is a sum over all possible values of x,y.

The second term is a sum over all possible values of ONLY y.

Homework Equations

Given above.

The Attempt at a Solution

I know I have to differentiate L with respect to P(x',y') where x',y' are fixed values.

The idea is that the first term consists of a sum of P(x,y) over all possible x,y. So, we just need to differentiate L with respect to P(x',y') while keeping all other P(x,y) constant.

But how do I differentiate the second term with respect to P(x',y')? It is a sum over all possible y values.

I know the solution is supposed to be $$\lambda$$ when the second term is differentiated with respect to a particular P(x',y') but how do we obtain that?

THANKS! :)

 

[Ray Vickson]

Homework Statement

L = - $\Sigma$ _x,y (P(x,y) log P(x,y)) + $$\lambda$$ $\Sigma$_y (P(x,y) - q(x))

This is the Lagrangian. I need to maximize the first term in the sum with respect to P(x,y), subject to the constraint in the second term.

The first term is a sum over all possible values of x,y.

The second term is a sum over all possible values of ONLY y.

Homework Equations

Given above.

The Attempt at a Solution

I know I have to differentiate L with respect to P(x',y') where x',y' are fixed values.

The idea is that the first term consists of a sum of P(x,y) over all possible x,y. So, we just need to differentiate L with respect to P(x',y') while keeping all other P(x,y) constant.

But how do I differentiate the second term with respect to P(x',y')? It is a sum over all possible y values.

I know the solution is supposed to be $$\lambda$$ when the second term is differentiated with respect to a particular P(x',y') but how do we obtain that?

THANKS! :)

Do you want to solve $\max \sum_{x,y} P(x,y) \log P(x,y)$, subject to $\sum_{y} P(x,y) = q(x)$ for all x? That is very different from what you wrote; the correct Lagrangian would be $\sum_{x,y} P(x,y) \log P(x,y) + \sum_{x} \lambda_{x} [\sum_{y} P(x,y) - q(x)]$. Remember: in this problem the variables are P(x,y); the x and y themselves are just summation indices. (Probably that would be a lot clearer if we wrote $\sum_{i,j} P(i,j) \log P(i,j)$, etc.) So: for any fixed (x,y), $dL/dP(x,y) = \log P(x,y) + 1 + \lambda_{x}.$

RGV

 

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Do you want to solve $\max \sum_{x,y} P(x,y) \log P(x,y)$, subject to $\sum_{y} P(x,y) = q(x)$ for all x? That is very different from what you wrote; the correct Lagrangian would be $\sum_{x,y} P(x,y) \log P(x,y) + \sum_{x} \lambda_{x} [\sum_{y} P(x,y) - q(x)]$. Remember: in this problem the variables are P(x,y); the x and y themselves are just summation indices. (Probably that would be a lot clearer if we wrote $\sum_{i,j} P(i,j) \log P(i,j)$, etc.) So: for any fixed (x,y), $dL/dP(x,y) = log P(x,y) + 1 + \lambda_{x}.$

RGV

Yes thanks!

There is something else I don't get...

Page 10 of this lecture notes: http://pillowlab.cps.utexas.edu/teaching/CompNeuro10/slides/slides16_EntropyMethods.pdf

Says we should get the solution as P(x,y) = P(x) P(y). I have no idea how to get this.

Furthermore, they seem to have $$dL/dP(x,y) = \log P(x,y) + 1 + \Sigma \lambda_{x} P_x(x)$$. , which I think makes sense because the optimal P(x,y) should take into account the constraints?

The notes are for integrals but I am assuming it works for summations?

 

[Ray Vickson]

The formula P(x,y) = f(x)*f(y) (not P(x)*P(y) ) only works if f(x) = q(x) [assuming sum_{y} f(y) = 1]

 

[Ray Vickson]

The results on page 10 are for a different probl than the one you posed in this Forum. Your posed problem has the solution P(x,y) = q(x)/N for all y, where N = number of y-values [assuming a discrete problem]. This follows from the Lagrange condition
log P(x,y) + 1 + lambda_x = 0 for all (x,y). That is, the distribution is uniform over y. The problem on page 10 of your link is:
max -sum_{x,y} P(x,y) log P(x,y), subject to sum_{y} P(x,y) = r(x) for all x, and sum_{x} P(x,y) = s(y) for all y. In this case the Lagrange method gives P(x,y) = r(x)*s(y) for all x and y.

 

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The results on page 10 are for a different probl than the one you posed in this Forum. Your posed problem has the solution P(x,y) = q(x)/N for all y, where N = number of y-values [assuming a discrete problem]. This follows from the Lagrange condition
log P(x,y) + 1 + lambda_x = 0 for all (x,y). That is, the distribution is uniform over y. The problem on page 10 of your link is:
max -sum_{x,y} P(x,y) log P(x,y), subject to sum_{y} P(x,y) = r(x) for all x, and sum_{x} P(x,y) = s(y) for all y. In this case the Lagrange method gives P(x,y) = r(x)*s(y) for all x and y.

No, I meant to ask you about the problem on page 10. Your original reply solved my original problem.

To make it more precise:

Ignore everything that I have asked so far. The new problem is...

max - $\Sigma$_x,y P(x,y) log P(x,y), subject to $\Sigma$_y P(x,y) = r(x) for all x, and $\Sigma$_x P(x,y) = s(y) for all y.

Why isn't dL/dP(x,y) = 1 + log P(x,y) + 1 + $\Sigma$_i$\lambda$ {x_i} +$\Sigma$_i$\lambda$ {y_i}?

Since the constraint look like:

$\lambda$_xi$\Sigma$_y [ P(x_i,y) - r(x_i) ] in the lagrangian.

Shouldn't d / dP(x,y) of this be just $\lambda$_x?

Thanks Ray, you're of great help! :)

 

[Ray Vickson]

No, I meant to ask you about the problem on page 10. Your original reply solved my original problem.

To make it more precise:

Ignore everything that I have asked so far. The new problem is...

max - $\Sigma$_x,y P(x,y) log P(x,y), subject to $\Sigma$_y P(x,y) = r(x) for all x, and $\Sigma$_x P(x,y) = s(y) for all y.

Why isn't dL/dP(x,y) = 1 + log P(x,y) + 1 + $\Sigma$_i$\lambda$ {x_i} +$\Sigma$_i$\lambda$ {y_i}?

Since the constraint look like:

$\lambda$_xi$\Sigma$_y [ P(x_i,y) - r(x_i) ] in the lagrangian.

Shouldn't d / dP(x,y) of this be just $\lambda$_x?

Thanks Ray, you're of great help! :)

$$L = -\sum_{x,y} P(x,y) \log P(x,y) + \sum_{x} \lambda_x (\sum_{y} P(x,y) - r(x)) + \sum_{y} \mu_y (\sum_{x} P(x,y) - s(y))$$
$$\displaystyle \frac{dL}{dP{x,y)} = -1 - \log P(x,y) + \lambda_x + \mu_y = 0$$
Thus, $P(x,y) = \exp(-1+\lambda_x + \mu_y)$, so $r(x) = \exp(-1 + \lambda_x) \sum_{y} \exp(\mu_y)$, which means that $\exp(\lambda_x) = c_1 r(x)$, where $c_1 = \exp(1) / \sum_{y} \exp(\mu_y)$ is a constant, and similarly, the equation $\sum_{x} P(x,y) = s(y)$ leads to $\exp(\mu_y) = c_2 s(y)$ for a constant c_2. Altogether we get $P(x,y) = c_1 c_2 \exp(-1) r(x) s(y) = c r(x) s(y)$, where c is a constant. Normalizing gives c = 1.

RGV

 

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$$L = -\sum_{x,y} P(x,y) \log P(x,y) + \sum_{x} \lambda_x (\sum_{y} P(x,y) - r(x)) + \sum_{y} \mu_y (\sum_{x} P(x,y) - s(y))$$
$$\displaystyle \frac{dL}{dP{x,y)} = -1 - \log P(x,y) + \lambda_x + \mu_y = 0$$
Thus, $P(x,y) = \exp(-1+\lambda_x + \mu_y)$, so $r(x) = \exp(-1 + \lambda_x) \sum_{y} \exp(\mu_y)$, which means that $\exp(\lambda_x) = c_1 r(x)$, where $c_1 = \exp(1) / \sum_{y} \exp(\mu_y)$ is a constant, and similarly, the equation $\sum_{x} P(x,y) = s(y)$ leads to $\exp(\mu_y) = c_2 s(y)$ for a constant c_2. Altogether we get $P(x,y) = c_1 c_2 \exp(-1) r(x) s(y) = c r(x) s(y)$, where c is a constant. Normalizing gives c = 1.

RGV

Nice! Thanks a lot for the help!

Problem resolved. :D