# Optimization of Integral

$F(x,y)=\int_a^{b} \int_c^{d} G(x,y,s,t)dsdt$. I wish to find $x$ and $y$ (subject to some constraints) that minimize $F$. $F$ is not expressible in closed form. Is the only way to solve this problem as follows: "Replace different $x$ and $y$ values according to the constraints and evaluate the integral numerically each time"?

• What form are the constraints? Are they linear, quadratic form, non-linear? And similarly, what form is $G$? Apr 14 '13 at 4:34
• Additionally, is $G$ expressible in closed form? Is $G$ convex? Are $x$ and $y$ independent of $s$ and $t$? Apr 14 '13 at 4:37
• @John Yes, G is expressible in closed form - I need to check if it is convex. Yes, x and y are indpt of s and t. Also, I tried to simplify the situation. The $x$ and $y$ are actually $k$-dimensional vectors with $-1\leq x_{1}<x_{2}<\dots <x_{k}\leq 1$ and $0 < y_{i} < 1$ with $\Sigma y_{i} = 1$ Apr 14 '13 at 4:42

Since $G$ is in closed form (as per your comment above), you can express derivatives of $F$ as
$$\frac{\partial F}{\partial x_i} = \int_a^b \int_c^d \frac{\partial}{\partial x_i}G(x,y,s,t)\ ds\ dt$$
The same goes for $\frac{\partial F}{\partial y_i}$, so you can perform gradient descent if your $F$ is convex. (I don't recall what the conditions are for an integral of a function to be convex. It may be enough to know whether $G$ is convex just in $x$ and $y$.)
If $F$ is concave, then your solution will be at one of the vertices of your bounding region, since your bounding region is a convex polytope.
The passage discusses an optimization problem involving a double integral and asks several questions about the form of the constraints and the function $G$. It also provides some additional information about the variables and suggests a method for solving the problem using gradient descent if the function is convex.
multiple_questions
Given a text passage, determine:
    1. If it contains a mathematical question (including word problems or calculations)
    2. If an answer to that mathematical question is provided in the text

First, describe the content structure you found in 1-2 sentences.

Then provide your analysis in the following format:
TYPE: <one of the following values>
- NO_QUESTION
- SINGLE_QUESTION
- SINGLE_QUESTION_WITH_ANSWER
- MULTIPLE_QUESTIONS
- MULTIPLE_QUESTIONS_WITH_ANSWERS
system
# Optimization of Integral

$F(x,y)=\int_a^{b} \int_c^{d} G(x,y,s,t)dsdt$. I wish to find $x$ and $y$ (subject to some constraints) that minimize $F$. $F$ is not expressible in closed form. Is the only way to solve this problem as follows: "Replace different $x$ and $y$ values according to the constraints and evaluate the integral numerically each time"?

• What form are the constraints? Are they linear, quadratic form, non-linear? And similarly, what form is $G$? Apr 14 '13 at 4:34
• Additionally, is $G$ expressible in closed form? Is $G$ convex? Are $x$ and $y$ independent of $s$ and $t$? Apr 14 '13 at 4:37
• @John Yes, G is expressible in closed form - I need to check if it is convex. Yes, x and y are indpt of s and t. Also, I tried to simplify the situation. The $x$ and $y$ are actually $k$-dimensional vectors with $-1\leq x_{1}<x_{2}<\dots <x_{k}\leq 1$ and $0 < y_{i} < 1$ with $\Sigma y_{i} = 1$ Apr 14 '13 at 4:42

Since $G$ is in closed form (as per your comment above), you can express derivatives of $F$ as
$$\frac{\partial F}{\partial x_i} = \int_a^b \int_c^d \frac{\partial}{\partial x_i}G(x,y,s,t)\ ds\ dt$$
The same goes for $\frac{\partial F}{\partial y_i}$, so you can perform gradient descent if your $F$ is convex. (I don't recall what the conditions are for an integral of a function to be convex. It may be enough to know whether $G$ is convex just in $x$ and $y$.)
If $F$ is concave, then your solution will be at one of the vertices of your bounding region, since your bounding region is a convex polytope.
user
The passage discusses an optimization problem involving a double integral and asks several questions about the form of the constraints and the function $G$. It also provides some additional information about the variables and suggests a method for solving the problem using gradient descent if the function is convex.

TYPE: MULTIPLE_QUESTIONS