2014 dxdy logo

Научный форум dxdy

Математика, Физика, Computer Science, Machine Learning, LaTeX, Механика и Техника, Химия,
Биология и Медицина, Экономика и Финансовая Математика, Гуманитарные науки




Начать новую тему Ответить на тему
 
 Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 00:33 


25/11/13
6
Hello,

I'm sorry for asking this question here in English since I don't know Russian. I heard that Russians are pretty good in mathematics and computer science. Any help would be greatly appreciated.

Considering two sets $A, B$ containing some $p$-dimensional points $x \in \mathbb{R}^p$. Let $d_x^S =$ min$_{x' \in S \setminus \{x\}} \lVert \mathbf{x - x'} \rVert$ denote the Euclidean distance from $x$ to its nearest point in $S$. We have a very simple algorithm:

1. $\forall x \in A$, if $d_x^A > d_x^B$ then move $x$ from $A$ to $B$.
2. $\forall x \in A$, if $d_x^A < d_x^B$ then move $x$ from $B$ to $A$.
3. Repeat (1) and (2) until convergence

Convergence is when there is no more $x \in A$ such that $d_x^A > d_x^B$, and there is no more $x \in B$ such that $d_x^A < d_x^B$.

How could I figure out which function does this algorithm minimize or maximize at each iteration ?
The function $\Phi(A)+\Phi(B) = \sum_{x \in A} d_x^A + \sum_{x \in B} d_x^B$ does not seem to decrease at each iteration.

Thanks for help

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 00:56 
Модератор
Аватара пользователя


11/01/06
5702
ShNaYkHs в сообщении #792715 писал(а):
The function $\Phi(A)+\Phi(B) = \sum_{x \in A} d_x^A + \sum_{x \in B} d_x^B$ does not seem to decrease at each iteration.

It does. For example, if $x\in A$ and $d_x^A > d_x^B$, then moving $x$ from $A$ to $B$ decreases $\Phi(A)+\Phi(B)$ by the value of $d_x^A - d_x^B$.

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 02:05 


25/11/13
6
maxal в сообщении #792722 писал(а):
ShNaYkHs в сообщении #792715 писал(а):
The function $\Phi(A)+\Phi(B) = \sum_{x \in A} d_x^A + \sum_{x \in B} d_x^B$ does not seem to decrease at each iteration.

It does. For example, if $x\in A$ and $d_x^A > d_x^B$, then moving $x$ from $A$ to $B$ decreases $\Phi(A)+\Phi(B)$ by the value of $d_x^A - d_x^B$.


I have checked both experimentally and analytically that $\Phi(A)+\Phi(B)$ is not decreased at each iteration. This is because when you move $x$ from $A$ to $B$, $\Phi(A)+\Phi(B)$ is not necessarily decreased by $d_x^A - d_x^B$, because A changes and some points in A becomes nearest to each other (while they where not before removing $x$). When you remove $x$ from $A$ and there is some $y \in A$ for which $x$ was the nearest point, then the nearest point of $y$ becomes another point in $A$. Thus $\Phi(A)+\Phi(B)$ do not necessarily decreases by $d_x^A - d_x^B$ in this case.

I can actually give you a counter example where it is not minimized. For example, let A={(0,0,0),(0,0,1),(0,0,−1)}, B={(0.99,0,0),(0.99,1,0),(0.99,−1,0)}. Then after the first iteration only (0,0,0) moves from A to B, and we can verify that the function increases.

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 08:02 
Модератор
Аватара пользователя


11/01/06
5702
You are right. Here is an appropriate potential function, which unfortunately is not easy to describe.

Let $M=A\cup B$ and for every $x\in M$ define $C_x = \{ y\in M : d(x,y) = \min_{z\ne x} d(x,z) \}$ as the set of points closest to $x$.
Define a graph $G$ whose vertices are the elements of $M$ and every vertex $x\in M$ has incident edges $(x,y)$ for every $y\in C_x$. For each edge, we assign length equal the distance between its endpoints.

Notice that graph $G$ does not depend on a particular partition of $M$ into sets $A$ and $B$.

Let $e_1, e_2, \dots, e_m$ be a fixed ordering of the edges of $G$ such that the length of edges does not increase.

Now, define the potential function associated with partition $M=A\cup B$ as
$$\sum_{k=1}^m 2^k \cdot c(e_k)$$
where the coefficient $c(e_k)=1$ if the endpoints of edge $e_k$ are in distinct parts (i.e., one in $A$ and the other in $B$); otherwise $c(e_k)=0$.

For example, suppose that $x\in A$ and $d_x^A > d_x^B$ so that we move $x$ from $A$ to $B$.
The inequality $d_x^A > d_x^B$ implies that $C_x \subset B$.
The potential here changes as follows:
(i) the coefficient of every edge $(x,y)$ for $y\in C_x$ turns from 1 to 0 (there is at least one such edge);
(ii) the coefficients of edges $(x,z)$ (if they present in $G$) for $z\in A$ turn from 0 to 1.
However, the edges of type (i) are strictly shorter than the edges of type (ii) (since $z\not\in C_x$), and thus edges of type (i) have higher indices, making the potential decrease.

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 20:48 


25/11/13
6
maxal в сообщении #792762 писал(а):
However, the edges of type (i) are strictly shorter than the edges of type (ii) (since $z\not\in C_x$), and thus edges of type (i) have higher indices, making the potential decrease.


Thanks for you answer. I have two questions:

1) Question 1) For a given $x \in A$ where $d_x^A > d_x^B$, we can imagine the case where we have only one edge $(x,y)$ of type (i) which is incident to $x$ and we have much more edges $(x,z)$ of type (ii) which are incident to $x$. In this situation, even if the edge of type (i) is strictly shorter than the edges of type (ii), it is possible that potential increases because there are too many edges $(x,z)$ whose coefficients turn from 0 to 1, and possibly making the potential increase.

2) Question 2) What if we define define $d_x^S$ as the mean distance from $x$ to its $n$ nearest points in $S$ ? Does your reasoning still the same (same graph and so on) ? Or do this reasoning works only if $n=1$ ?

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 21:18 
Модератор
Аватара пользователя


11/01/06
5702
ShNaYkHs в сообщении #793088 писал(а):
1) Question 1) For a given $x \in A$ where $d_x^A > d_x^B$, we can imagine the case where we have only one edge $(x,y)$ of type (i) which is incident to $x$ and we have much more edges $(x,z)$ of type (ii) which are incident to $x$. In this situation, even if the edge of type (i) is strictly shorter than the edges of type (ii), it is possible that potential increases because there are too many edges $(x,z)$ whose coefficients turn from 0 to 1, and possibly making the potential increase.

2) Question 2) What if we define define $d_x^S$ as the mean distance from $x$ to its $n$ nearest points in $S$ ? Does your reasoning still the same (same graph and so on) ? Or do this reasoning works only if $n=1$ ?


Answer 1: If you have a binary number and switch the $k$-th bit (counting from less significant to most significant bits) in it from 1 to 0, then it does not matter how you change bits with lower indices, the resulting number will be still smaller than the original one. Somewhat extreme example: consider number $2^k$, if you switch the $k$-th bit from 1 to 0, and all the other bits from 0 to 1, you'll get number $2^k-1$ which is smaller than $2^k$.

Answer 2: I guess the graph would need to be modified. I'll check later.

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение26.11.2013, 22:28 


25/11/13
6
maxal в сообщении #793109 писал(а):
Answer 1: If you have a binary number and switch the $k$-th bit (counting from less significant to most significant bits) in it from 1 to 0, then it does not matter how you change bits with lower indices, the resulting number will be still smaller than the original one. Somewhat extreme example: consider number $2^k$, if you switch the $k$-th bit from 1 to 0, and all the other bits from 0 to 1, you'll get number $2^k-1$ which is smaller than $2^k$.

Answer 2: I guess the graph would need to be modified. I'll check later.


Ok thank you, I understand the first answer now. I'll wait for your answer concerning the case where we define $d_x^S$ as the mean distance from $x$ to its $n$ nearest points in $S$.

 Профиль  
                  
 
 Re: Which potential function does this algorithm minimize ?
Сообщение03.12.2013, 00:00 


25/11/13
6
ShNaYkHs в сообщении #793161 писал(а):
maxal в сообщении #793109 писал(а):
Answer 1: If you have a binary number and switch the $k$-th bit (counting from less significant to most significant bits) in it from 1 to 0, then it does not matter how you change bits with lower indices, the resulting number will be still smaller than the original one. Somewhat extreme example: consider number $2^k$, if you switch the $k$-th bit from 1 to 0, and all the other bits from 0 to 1, you'll get number $2^k-1$ which is smaller than $2^k$.

Answer 2: I guess the graph would need to be modified. I'll check later.


Ok thank you, I understand the first answer now. I'll wait for your answer concerning the case where we define $d_x^S$ as the mean distance from $x$ to its $n$ nearest points in $S$.


Any idea for the case where $n > 1$ please ?

 Профиль  
                  
Показать сообщения за:  Поле сортировки  
Начать новую тему Ответить на тему  [ Сообщений: 8 ] 

Модераторы: Karan, Toucan, PAV, maxal, Супермодераторы



Кто сейчас на конференции

Сейчас этот форум просматривают: нет зарегистрированных пользователей


Вы не можете начинать темы
Вы не можете отвечать на сообщения
Вы не можете редактировать свои сообщения
Вы не можете удалять свои сообщения
Вы не можете добавлять вложения

Найти:
Powered by phpBB © 2000, 2002, 2005, 2007 phpBB Group