text: # 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.
explanation: The document discusses an optimization problem involving a double integral function \( F(x, y) \) and seeks to find the values of \( x \) and \( y \) that minimize \( F \) under certain constraints. The text includes a series of questions and comments from a user and a response providing additional details and a potential solution approach using gradient descent.
distilabel_metadata.content: Evaluate the user given text extract and determine if it contains a math question and optionally and answer to this question.
  
  Output rules:
  
  1. Briefly describe the document and mention if it contains a question or a question and answer.
  2. Finalize the output with: Label: no-question,question,question-and-answer,multiple-question,multiple-question-and-answer
distilabel_metadata.role: system
distilabel_metadata.content: # 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.
distilabel_metadata.role: user
distilabel_metadata.raw_output_q_a_detector_0: The document discusses an optimization problem involving a double integral function \( F(x, y) \) and seeks to find the values of \( x \) and \( y \) that minimize \( F \) under certain constraints. The text includes a series of questions and comments from a user and a response providing additional details and a potential solution approach using gradient descent.
  
  Label: multiple-question-and-answer