Why is the Absolute value / modulus function used?
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Why is the absolute value function or modulus function $|x|$ used ? What are its uses?
For example the square of a modulus number will always be positive, but why is it used when for example the square of any number whether positive or negative is always positive ? For example, $X^2$, will give a positive number whether negative or positive where $X$ is any number positive or negative.
absolute-value
$endgroup$
add a comment |
$begingroup$
Why is the absolute value function or modulus function $|x|$ used ? What are its uses?
For example the square of a modulus number will always be positive, but why is it used when for example the square of any number whether positive or negative is always positive ? For example, $X^2$, will give a positive number whether negative or positive where $X$ is any number positive or negative.
absolute-value
$endgroup$
4
$begingroup$
$x^2ne |x|$ so if I want the positive value of $x$ , how would I "just do" $x^2$?
$endgroup$
– John Douma
Dec 22 '18 at 19:26
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I’m just giving an example. What is the use of the modulus function ?
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– Dan
Dec 22 '18 at 19:27
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Obviously: Getting the absolute value of a number.
$endgroup$
– Henrik
Dec 22 '18 at 19:33
4
$begingroup$
It has many uses. Have you had Calculus? It is used in definitions where we only care about the distance between two points regardless of which one is greater. e.g. $|x-c|ltdeltaimplies |f(x)-f(c)|ltepsilon$.
$endgroup$
– John Douma
Dec 22 '18 at 19:33
7
$begingroup$
The absolute value has use in absolutely every application of mathematics, pun intended.
$endgroup$
– Matt Samuel
Dec 22 '18 at 20:47
add a comment |
$begingroup$
Why is the absolute value function or modulus function $|x|$ used ? What are its uses?
For example the square of a modulus number will always be positive, but why is it used when for example the square of any number whether positive or negative is always positive ? For example, $X^2$, will give a positive number whether negative or positive where $X$ is any number positive or negative.
absolute-value
$endgroup$
Why is the absolute value function or modulus function $|x|$ used ? What are its uses?
For example the square of a modulus number will always be positive, but why is it used when for example the square of any number whether positive or negative is always positive ? For example, $X^2$, will give a positive number whether negative or positive where $X$ is any number positive or negative.
absolute-value
absolute-value
edited Dec 22 '18 at 19:30
Dan
asked Dec 22 '18 at 19:20
Dan Dan
92631129
92631129
4
$begingroup$
$x^2ne |x|$ so if I want the positive value of $x$ , how would I "just do" $x^2$?
$endgroup$
– John Douma
Dec 22 '18 at 19:26
$begingroup$
I’m just giving an example. What is the use of the modulus function ?
$endgroup$
– Dan
Dec 22 '18 at 19:27
$begingroup$
Obviously: Getting the absolute value of a number.
$endgroup$
– Henrik
Dec 22 '18 at 19:33
4
$begingroup$
It has many uses. Have you had Calculus? It is used in definitions where we only care about the distance between two points regardless of which one is greater. e.g. $|x-c|ltdeltaimplies |f(x)-f(c)|ltepsilon$.
$endgroup$
– John Douma
Dec 22 '18 at 19:33
7
$begingroup$
The absolute value has use in absolutely every application of mathematics, pun intended.
$endgroup$
– Matt Samuel
Dec 22 '18 at 20:47
add a comment |
4
$begingroup$
$x^2ne |x|$ so if I want the positive value of $x$ , how would I "just do" $x^2$?
$endgroup$
– John Douma
Dec 22 '18 at 19:26
$begingroup$
I’m just giving an example. What is the use of the modulus function ?
$endgroup$
– Dan
Dec 22 '18 at 19:27
$begingroup$
Obviously: Getting the absolute value of a number.
$endgroup$
– Henrik
Dec 22 '18 at 19:33
4
$begingroup$
It has many uses. Have you had Calculus? It is used in definitions where we only care about the distance between two points regardless of which one is greater. e.g. $|x-c|ltdeltaimplies |f(x)-f(c)|ltepsilon$.
$endgroup$
– John Douma
Dec 22 '18 at 19:33
7
$begingroup$
The absolute value has use in absolutely every application of mathematics, pun intended.
$endgroup$
– Matt Samuel
Dec 22 '18 at 20:47
4
4
$begingroup$
$x^2ne |x|$ so if I want the positive value of $x$ , how would I "just do" $x^2$?
$endgroup$
– John Douma
Dec 22 '18 at 19:26
$begingroup$
$x^2ne |x|$ so if I want the positive value of $x$ , how would I "just do" $x^2$?
$endgroup$
– John Douma
Dec 22 '18 at 19:26
$begingroup$
I’m just giving an example. What is the use of the modulus function ?
$endgroup$
– Dan
Dec 22 '18 at 19:27
$begingroup$
I’m just giving an example. What is the use of the modulus function ?
$endgroup$
– Dan
Dec 22 '18 at 19:27
$begingroup$
Obviously: Getting the absolute value of a number.
$endgroup$
– Henrik
Dec 22 '18 at 19:33
$begingroup$
Obviously: Getting the absolute value of a number.
$endgroup$
– Henrik
Dec 22 '18 at 19:33
4
4
$begingroup$
It has many uses. Have you had Calculus? It is used in definitions where we only care about the distance between two points regardless of which one is greater. e.g. $|x-c|ltdeltaimplies |f(x)-f(c)|ltepsilon$.
$endgroup$
– John Douma
Dec 22 '18 at 19:33
$begingroup$
It has many uses. Have you had Calculus? It is used in definitions where we only care about the distance between two points regardless of which one is greater. e.g. $|x-c|ltdeltaimplies |f(x)-f(c)|ltepsilon$.
$endgroup$
– John Douma
Dec 22 '18 at 19:33
7
7
$begingroup$
The absolute value has use in absolutely every application of mathematics, pun intended.
$endgroup$
– Matt Samuel
Dec 22 '18 at 20:47
$begingroup$
The absolute value has use in absolutely every application of mathematics, pun intended.
$endgroup$
– Matt Samuel
Dec 22 '18 at 20:47
add a comment |
4 Answers
4
active
oldest
votes
$begingroup$
In the context of real numbers the absolute value of a number is used in many ways but perhaps very elementarily it is used to write numbers in a canonical form. Every real number $ane 0$ is uniquely equal to $pm left |aright|$. So if we define the sign function $scolon mathbb Rsetminus{0}to {+,-}$ given by $s(a)=+$ if $a>0$ and $s(a)=-$ if $a<0$, then: for all $ane 0$ in $mathbb R$ we have $a=sign(a)cdot left | a right |$. In a sense this is a way to build all the reals from the positive ones. This is all just a special case of the polar representation of complex numbers, a representation of utmost importance.
$endgroup$
add a comment |
$begingroup$
One use of it is to define the distance between numbers. For example, in Calculus, you may want to say "the distance between $x$ and $y$ is less than $1$". The way to write that mathematically is $|x-y|<1$. And you want to write it mathematically so you can work with it mathematically.
$endgroup$
add a comment |
$begingroup$
The notation $vert xvert$ for absolute value of $x$ was introduced by Weierstrass in 1841:
K. Weierstrass, Mathematische Werke, Vol. I (Berlin, 1894), p. 67.
Quoted from [1]
...There has been a real need in analysis for a convenient symbolism for
"absolute value" of a given number, or "absolute number," and the two
vertical bars introduced in 1841 by Weierstrass, as in $vert zvert$, have met with wide adoption;...
Extra information: Absolute is from the Latin absoluere, "to free from"; hence suggesting, to free from its sign.
[1] Florian Cajori, A History of Mathematical Notations (Two volumes bound as one), Dover Publications, 1993.
My take on a usage example of absolute value:
$$
min(x,y)=frac{1}{2}(|x+y|-|x-y|)
$$
$$
max(x,y)=frac{1}{2}(|x+y|+|x-y|)
$$
$endgroup$
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
add a comment |
$begingroup$
Because both of them are useful.
You explicitly mentioned the square function. Therefore, I want to give some examples. The main idea is that the non-differentiability of $|cdot|$ is useful in minimization problem.
Estimators
We know that the arithmetic mean $hat{mu}=sum_{i=1}^n x_i$ gives
$$min_{mu} ,(x_i-mu)^2$$
but it is less well-known that the median gives
$$min_{mu} , |x_i-mu|.$$
Signal Processing
Let's use image processing as an example. Suppose $g$ is a given, noisy image. We want to find some smoother image $f$ which looks like $g$.
The Harmonic L$^2$ minimization model solves
$$-bigtriangleup f + f = g $$
and it turns out to be equivalent to solving a minimization problem:
$$min_{f} ,(int_{Omega} (f(x,y)-g(x,y))^2 dxdy + int_{Omega} |nabla{f(x,y)}|^2 dxdy).$$
An enhanced version is the ROF model. It solves
$$min_{f} ,(frac{1}{2} int_{Omega} (f(x,y)-g(x,y))^2 dxdy + lambda int_{Omega} |nabla{f(x,y)}| dxdy).$$
Notice that for appropriate $lambda$, these two models only differ by a square. Another remark is that $|cdot|$ gives the Euclidean norm when the argument is a vector. However, the idea still applies since the norm is non-zero
Model Selection
In classical model selection problem, we are given a set of predictors and a response (in vector form). We want to decide which predictors are useful. One way is to choose a "good" subset of predictors. Another way is to shrink the regression coefficients.
The classical regression model solves the following minimization problem:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2$$
The Ridge Regression solves the following:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p {beta_j}^2$$
, so that larger $beta_j$ gives penalty.
Another version is Lasso, which solves
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p |beta_j|.$$
$endgroup$
add a comment |
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4 Answers
4
active
oldest
votes
4 Answers
4
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
In the context of real numbers the absolute value of a number is used in many ways but perhaps very elementarily it is used to write numbers in a canonical form. Every real number $ane 0$ is uniquely equal to $pm left |aright|$. So if we define the sign function $scolon mathbb Rsetminus{0}to {+,-}$ given by $s(a)=+$ if $a>0$ and $s(a)=-$ if $a<0$, then: for all $ane 0$ in $mathbb R$ we have $a=sign(a)cdot left | a right |$. In a sense this is a way to build all the reals from the positive ones. This is all just a special case of the polar representation of complex numbers, a representation of utmost importance.
$endgroup$
add a comment |
$begingroup$
In the context of real numbers the absolute value of a number is used in many ways but perhaps very elementarily it is used to write numbers in a canonical form. Every real number $ane 0$ is uniquely equal to $pm left |aright|$. So if we define the sign function $scolon mathbb Rsetminus{0}to {+,-}$ given by $s(a)=+$ if $a>0$ and $s(a)=-$ if $a<0$, then: for all $ane 0$ in $mathbb R$ we have $a=sign(a)cdot left | a right |$. In a sense this is a way to build all the reals from the positive ones. This is all just a special case of the polar representation of complex numbers, a representation of utmost importance.
$endgroup$
add a comment |
$begingroup$
In the context of real numbers the absolute value of a number is used in many ways but perhaps very elementarily it is used to write numbers in a canonical form. Every real number $ane 0$ is uniquely equal to $pm left |aright|$. So if we define the sign function $scolon mathbb Rsetminus{0}to {+,-}$ given by $s(a)=+$ if $a>0$ and $s(a)=-$ if $a<0$, then: for all $ane 0$ in $mathbb R$ we have $a=sign(a)cdot left | a right |$. In a sense this is a way to build all the reals from the positive ones. This is all just a special case of the polar representation of complex numbers, a representation of utmost importance.
$endgroup$
In the context of real numbers the absolute value of a number is used in many ways but perhaps very elementarily it is used to write numbers in a canonical form. Every real number $ane 0$ is uniquely equal to $pm left |aright|$. So if we define the sign function $scolon mathbb Rsetminus{0}to {+,-}$ given by $s(a)=+$ if $a>0$ and $s(a)=-$ if $a<0$, then: for all $ane 0$ in $mathbb R$ we have $a=sign(a)cdot left | a right |$. In a sense this is a way to build all the reals from the positive ones. This is all just a special case of the polar representation of complex numbers, a representation of utmost importance.
answered Dec 22 '18 at 19:48
Ittay WeissIttay Weiss
63.7k6101183
63.7k6101183
add a comment |
add a comment |
$begingroup$
One use of it is to define the distance between numbers. For example, in Calculus, you may want to say "the distance between $x$ and $y$ is less than $1$". The way to write that mathematically is $|x-y|<1$. And you want to write it mathematically so you can work with it mathematically.
$endgroup$
add a comment |
$begingroup$
One use of it is to define the distance between numbers. For example, in Calculus, you may want to say "the distance between $x$ and $y$ is less than $1$". The way to write that mathematically is $|x-y|<1$. And you want to write it mathematically so you can work with it mathematically.
$endgroup$
add a comment |
$begingroup$
One use of it is to define the distance between numbers. For example, in Calculus, you may want to say "the distance between $x$ and $y$ is less than $1$". The way to write that mathematically is $|x-y|<1$. And you want to write it mathematically so you can work with it mathematically.
$endgroup$
One use of it is to define the distance between numbers. For example, in Calculus, you may want to say "the distance between $x$ and $y$ is less than $1$". The way to write that mathematically is $|x-y|<1$. And you want to write it mathematically so you can work with it mathematically.
edited Dec 23 '18 at 12:17
amWhy
1
1
answered Dec 22 '18 at 19:34
OviOvi
12.4k1038112
12.4k1038112
add a comment |
add a comment |
$begingroup$
The notation $vert xvert$ for absolute value of $x$ was introduced by Weierstrass in 1841:
K. Weierstrass, Mathematische Werke, Vol. I (Berlin, 1894), p. 67.
Quoted from [1]
...There has been a real need in analysis for a convenient symbolism for
"absolute value" of a given number, or "absolute number," and the two
vertical bars introduced in 1841 by Weierstrass, as in $vert zvert$, have met with wide adoption;...
Extra information: Absolute is from the Latin absoluere, "to free from"; hence suggesting, to free from its sign.
[1] Florian Cajori, A History of Mathematical Notations (Two volumes bound as one), Dover Publications, 1993.
My take on a usage example of absolute value:
$$
min(x,y)=frac{1}{2}(|x+y|-|x-y|)
$$
$$
max(x,y)=frac{1}{2}(|x+y|+|x-y|)
$$
$endgroup$
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
add a comment |
$begingroup$
The notation $vert xvert$ for absolute value of $x$ was introduced by Weierstrass in 1841:
K. Weierstrass, Mathematische Werke, Vol. I (Berlin, 1894), p. 67.
Quoted from [1]
...There has been a real need in analysis for a convenient symbolism for
"absolute value" of a given number, or "absolute number," and the two
vertical bars introduced in 1841 by Weierstrass, as in $vert zvert$, have met with wide adoption;...
Extra information: Absolute is from the Latin absoluere, "to free from"; hence suggesting, to free from its sign.
[1] Florian Cajori, A History of Mathematical Notations (Two volumes bound as one), Dover Publications, 1993.
My take on a usage example of absolute value:
$$
min(x,y)=frac{1}{2}(|x+y|-|x-y|)
$$
$$
max(x,y)=frac{1}{2}(|x+y|+|x-y|)
$$
$endgroup$
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
add a comment |
$begingroup$
The notation $vert xvert$ for absolute value of $x$ was introduced by Weierstrass in 1841:
K. Weierstrass, Mathematische Werke, Vol. I (Berlin, 1894), p. 67.
Quoted from [1]
...There has been a real need in analysis for a convenient symbolism for
"absolute value" of a given number, or "absolute number," and the two
vertical bars introduced in 1841 by Weierstrass, as in $vert zvert$, have met with wide adoption;...
Extra information: Absolute is from the Latin absoluere, "to free from"; hence suggesting, to free from its sign.
[1] Florian Cajori, A History of Mathematical Notations (Two volumes bound as one), Dover Publications, 1993.
My take on a usage example of absolute value:
$$
min(x,y)=frac{1}{2}(|x+y|-|x-y|)
$$
$$
max(x,y)=frac{1}{2}(|x+y|+|x-y|)
$$
$endgroup$
The notation $vert xvert$ for absolute value of $x$ was introduced by Weierstrass in 1841:
K. Weierstrass, Mathematische Werke, Vol. I (Berlin, 1894), p. 67.
Quoted from [1]
...There has been a real need in analysis for a convenient symbolism for
"absolute value" of a given number, or "absolute number," and the two
vertical bars introduced in 1841 by Weierstrass, as in $vert zvert$, have met with wide adoption;...
Extra information: Absolute is from the Latin absoluere, "to free from"; hence suggesting, to free from its sign.
[1] Florian Cajori, A History of Mathematical Notations (Two volumes bound as one), Dover Publications, 1993.
My take on a usage example of absolute value:
$$
min(x,y)=frac{1}{2}(|x+y|-|x-y|)
$$
$$
max(x,y)=frac{1}{2}(|x+y|+|x-y|)
$$
edited Dec 23 '18 at 9:27
WAF
1032
1032
answered Dec 22 '18 at 19:41
Picaud VincentPicaud Vincent
1,36439
1,36439
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
add a comment |
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
$begingroup$
@HansLundmark me too, that was the reason why I have another reference, let me write it down. Done.
$endgroup$
– Picaud Vincent
Dec 22 '18 at 19:50
add a comment |
$begingroup$
Because both of them are useful.
You explicitly mentioned the square function. Therefore, I want to give some examples. The main idea is that the non-differentiability of $|cdot|$ is useful in minimization problem.
Estimators
We know that the arithmetic mean $hat{mu}=sum_{i=1}^n x_i$ gives
$$min_{mu} ,(x_i-mu)^2$$
but it is less well-known that the median gives
$$min_{mu} , |x_i-mu|.$$
Signal Processing
Let's use image processing as an example. Suppose $g$ is a given, noisy image. We want to find some smoother image $f$ which looks like $g$.
The Harmonic L$^2$ minimization model solves
$$-bigtriangleup f + f = g $$
and it turns out to be equivalent to solving a minimization problem:
$$min_{f} ,(int_{Omega} (f(x,y)-g(x,y))^2 dxdy + int_{Omega} |nabla{f(x,y)}|^2 dxdy).$$
An enhanced version is the ROF model. It solves
$$min_{f} ,(frac{1}{2} int_{Omega} (f(x,y)-g(x,y))^2 dxdy + lambda int_{Omega} |nabla{f(x,y)}| dxdy).$$
Notice that for appropriate $lambda$, these two models only differ by a square. Another remark is that $|cdot|$ gives the Euclidean norm when the argument is a vector. However, the idea still applies since the norm is non-zero
Model Selection
In classical model selection problem, we are given a set of predictors and a response (in vector form). We want to decide which predictors are useful. One way is to choose a "good" subset of predictors. Another way is to shrink the regression coefficients.
The classical regression model solves the following minimization problem:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2$$
The Ridge Regression solves the following:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p {beta_j}^2$$
, so that larger $beta_j$ gives penalty.
Another version is Lasso, which solves
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p |beta_j|.$$
$endgroup$
add a comment |
$begingroup$
Because both of them are useful.
You explicitly mentioned the square function. Therefore, I want to give some examples. The main idea is that the non-differentiability of $|cdot|$ is useful in minimization problem.
Estimators
We know that the arithmetic mean $hat{mu}=sum_{i=1}^n x_i$ gives
$$min_{mu} ,(x_i-mu)^2$$
but it is less well-known that the median gives
$$min_{mu} , |x_i-mu|.$$
Signal Processing
Let's use image processing as an example. Suppose $g$ is a given, noisy image. We want to find some smoother image $f$ which looks like $g$.
The Harmonic L$^2$ minimization model solves
$$-bigtriangleup f + f = g $$
and it turns out to be equivalent to solving a minimization problem:
$$min_{f} ,(int_{Omega} (f(x,y)-g(x,y))^2 dxdy + int_{Omega} |nabla{f(x,y)}|^2 dxdy).$$
An enhanced version is the ROF model. It solves
$$min_{f} ,(frac{1}{2} int_{Omega} (f(x,y)-g(x,y))^2 dxdy + lambda int_{Omega} |nabla{f(x,y)}| dxdy).$$
Notice that for appropriate $lambda$, these two models only differ by a square. Another remark is that $|cdot|$ gives the Euclidean norm when the argument is a vector. However, the idea still applies since the norm is non-zero
Model Selection
In classical model selection problem, we are given a set of predictors and a response (in vector form). We want to decide which predictors are useful. One way is to choose a "good" subset of predictors. Another way is to shrink the regression coefficients.
The classical regression model solves the following minimization problem:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2$$
The Ridge Regression solves the following:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p {beta_j}^2$$
, so that larger $beta_j$ gives penalty.
Another version is Lasso, which solves
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p |beta_j|.$$
$endgroup$
add a comment |
$begingroup$
Because both of them are useful.
You explicitly mentioned the square function. Therefore, I want to give some examples. The main idea is that the non-differentiability of $|cdot|$ is useful in minimization problem.
Estimators
We know that the arithmetic mean $hat{mu}=sum_{i=1}^n x_i$ gives
$$min_{mu} ,(x_i-mu)^2$$
but it is less well-known that the median gives
$$min_{mu} , |x_i-mu|.$$
Signal Processing
Let's use image processing as an example. Suppose $g$ is a given, noisy image. We want to find some smoother image $f$ which looks like $g$.
The Harmonic L$^2$ minimization model solves
$$-bigtriangleup f + f = g $$
and it turns out to be equivalent to solving a minimization problem:
$$min_{f} ,(int_{Omega} (f(x,y)-g(x,y))^2 dxdy + int_{Omega} |nabla{f(x,y)}|^2 dxdy).$$
An enhanced version is the ROF model. It solves
$$min_{f} ,(frac{1}{2} int_{Omega} (f(x,y)-g(x,y))^2 dxdy + lambda int_{Omega} |nabla{f(x,y)}| dxdy).$$
Notice that for appropriate $lambda$, these two models only differ by a square. Another remark is that $|cdot|$ gives the Euclidean norm when the argument is a vector. However, the idea still applies since the norm is non-zero
Model Selection
In classical model selection problem, we are given a set of predictors and a response (in vector form). We want to decide which predictors are useful. One way is to choose a "good" subset of predictors. Another way is to shrink the regression coefficients.
The classical regression model solves the following minimization problem:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2$$
The Ridge Regression solves the following:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p {beta_j}^2$$
, so that larger $beta_j$ gives penalty.
Another version is Lasso, which solves
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p |beta_j|.$$
$endgroup$
Because both of them are useful.
You explicitly mentioned the square function. Therefore, I want to give some examples. The main idea is that the non-differentiability of $|cdot|$ is useful in minimization problem.
Estimators
We know that the arithmetic mean $hat{mu}=sum_{i=1}^n x_i$ gives
$$min_{mu} ,(x_i-mu)^2$$
but it is less well-known that the median gives
$$min_{mu} , |x_i-mu|.$$
Signal Processing
Let's use image processing as an example. Suppose $g$ is a given, noisy image. We want to find some smoother image $f$ which looks like $g$.
The Harmonic L$^2$ minimization model solves
$$-bigtriangleup f + f = g $$
and it turns out to be equivalent to solving a minimization problem:
$$min_{f} ,(int_{Omega} (f(x,y)-g(x,y))^2 dxdy + int_{Omega} |nabla{f(x,y)}|^2 dxdy).$$
An enhanced version is the ROF model. It solves
$$min_{f} ,(frac{1}{2} int_{Omega} (f(x,y)-g(x,y))^2 dxdy + lambda int_{Omega} |nabla{f(x,y)}| dxdy).$$
Notice that for appropriate $lambda$, these two models only differ by a square. Another remark is that $|cdot|$ gives the Euclidean norm when the argument is a vector. However, the idea still applies since the norm is non-zero
Model Selection
In classical model selection problem, we are given a set of predictors and a response (in vector form). We want to decide which predictors are useful. One way is to choose a "good" subset of predictors. Another way is to shrink the regression coefficients.
The classical regression model solves the following minimization problem:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2$$
The Ridge Regression solves the following:
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p {beta_j}^2$$
, so that larger $beta_j$ gives penalty.
Another version is Lasso, which solves
$$min_{beta_0,...,beta_p} sum_{i=1}^n (y_i-beta_0-sum_{j=1}^p beta_j x_{ij})^2+lambda sum_{j=1}^p |beta_j|.$$
answered Dec 24 '18 at 5:49
tonychow0929tonychow0929
29825
29825
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4
$begingroup$
$x^2ne |x|$ so if I want the positive value of $x$ , how would I "just do" $x^2$?
$endgroup$
– John Douma
Dec 22 '18 at 19:26
$begingroup$
I’m just giving an example. What is the use of the modulus function ?
$endgroup$
– Dan
Dec 22 '18 at 19:27
$begingroup$
Obviously: Getting the absolute value of a number.
$endgroup$
– Henrik
Dec 22 '18 at 19:33
4
$begingroup$
It has many uses. Have you had Calculus? It is used in definitions where we only care about the distance between two points regardless of which one is greater. e.g. $|x-c|ltdeltaimplies |f(x)-f(c)|ltepsilon$.
$endgroup$
– John Douma
Dec 22 '18 at 19:33
7
$begingroup$
The absolute value has use in absolutely every application of mathematics, pun intended.
$endgroup$
– Matt Samuel
Dec 22 '18 at 20:47