âPythagorean theoremâ for projection onto convex set
Clash Royale CLAN TAG#URR8PPP
up vote
2
down vote
favorite
I'm going through the book on online convex optimization by Hazan, (http://ocobook.cs.princeton.edu/OCObook.pdf) and in the first chapter I saw this assertion (which Hazan calls the "pythagorean theorem"):
Let $K subset mathbbR^d$ be a convex set, $y in mathbbR^d$, and $x = Pi_K(y)$. Then for any $z in K$ we have:
$$
|y - z | geq |x - z|.
$$
It is presented without proof - what is a proof for this? Also, how does it relate to the pythagorean theorem?
convex-analysis convex-optimization
add a comment |Â
up vote
2
down vote
favorite
I'm going through the book on online convex optimization by Hazan, (http://ocobook.cs.princeton.edu/OCObook.pdf) and in the first chapter I saw this assertion (which Hazan calls the "pythagorean theorem"):
Let $K subset mathbbR^d$ be a convex set, $y in mathbbR^d$, and $x = Pi_K(y)$. Then for any $z in K$ we have:
$$
|y - z | geq |x - z|.
$$
It is presented without proof - what is a proof for this? Also, how does it relate to the pythagorean theorem?
convex-analysis convex-optimization
The notes are a bit sloppy. The set $K$ should be closed to guarantee that a projection (as defined in Chapter 2, not 1 as you wrote above). It is a bit disingenuous to label the result as 500 BC.
â copper.hat
May 31 at 4:51
add a comment |Â
up vote
2
down vote
favorite
up vote
2
down vote
favorite
I'm going through the book on online convex optimization by Hazan, (http://ocobook.cs.princeton.edu/OCObook.pdf) and in the first chapter I saw this assertion (which Hazan calls the "pythagorean theorem"):
Let $K subset mathbbR^d$ be a convex set, $y in mathbbR^d$, and $x = Pi_K(y)$. Then for any $z in K$ we have:
$$
|y - z | geq |x - z|.
$$
It is presented without proof - what is a proof for this? Also, how does it relate to the pythagorean theorem?
convex-analysis convex-optimization
I'm going through the book on online convex optimization by Hazan, (http://ocobook.cs.princeton.edu/OCObook.pdf) and in the first chapter I saw this assertion (which Hazan calls the "pythagorean theorem"):
Let $K subset mathbbR^d$ be a convex set, $y in mathbbR^d$, and $x = Pi_K(y)$. Then for any $z in K$ we have:
$$
|y - z | geq |x - z|.
$$
It is presented without proof - what is a proof for this? Also, how does it relate to the pythagorean theorem?
convex-analysis convex-optimization
asked May 31 at 1:47
Reginald
505
505
The notes are a bit sloppy. The set $K$ should be closed to guarantee that a projection (as defined in Chapter 2, not 1 as you wrote above). It is a bit disingenuous to label the result as 500 BC.
â copper.hat
May 31 at 4:51
add a comment |Â
The notes are a bit sloppy. The set $K$ should be closed to guarantee that a projection (as defined in Chapter 2, not 1 as you wrote above). It is a bit disingenuous to label the result as 500 BC.
â copper.hat
May 31 at 4:51
The notes are a bit sloppy. The set $K$ should be closed to guarantee that a projection (as defined in Chapter 2, not 1 as you wrote above). It is a bit disingenuous to label the result as 500 BC.
â copper.hat
May 31 at 4:51
The notes are a bit sloppy. The set $K$ should be closed to guarantee that a projection (as defined in Chapter 2, not 1 as you wrote above). It is a bit disingenuous to label the result as 500 BC.
â copper.hat
May 31 at 4:51
add a comment |Â
2 Answers
2
active
oldest
votes
up vote
3
down vote
Suppose $x$ is the closest point to $y$ in the closed convex set $K$.
If $x=y$ there is nothing to prove, so we can suppose $xneq y$.
The we have that $langle x-y, z -x rangle ge 0$ for all $z in K$ (this is essentially the dual problem).
If $z in K$, we can write $z-y = t(x-y)+ d$, where $d bot (x-y)$. Then the above gives
$langle x-y, t(x-y)+ d +y -x rangle = (t-1)|x-y|^2ge 0$ from which we
get $t ge 1$.
Then $|z-y|^2 = |d|^2+ t^2 |x-y|^2 ge |x-y|^2$ which is the desired result.
Addendum: To see the first condition, suppose $|z-y| ge |y-x|$ for all $z in K$.
We have $|z-y|^2 = |z-x+x-y|^2 = |z-x|^2 + |x-y|^2 + 2 langle z-x,x-y rangle $ (this is where Pythagoras appears) which gives
$|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ for all $z in K$.
Since $w(t)=x+t(z-x) in K$ for all $t in [0,1]$, we have
$t^2 |z-x|^2 + 2 t langle z-x,x-y rangle ge 0$, dividing across by $t$ and letting $t downarrow 0$ yields the desired result.
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
add a comment |Â
up vote
1
down vote
Relevant is the so-called "obtuse angle criterion":
Let $mathcal K subseteq mathbb R^d $ be a convex set, $mathbf y in mathbb R^d setminus mathcal K$, and $mathbf x = Pi_mathcal K(mathbf y)$ (that is, $mathbf x$ is the closest point in the set $mathcal K$ to $mathbf y$) . Then for any point $mathbf z in mathcal K$, the angle between $mathbf z-mathbf x$ and $mathbf y - mathbf x$ is obtuse or right.
(We assume that $mathbf x$ exists; it is guaranteed to exist if $mathcal K$ is closed and nonempty.)
It's often convenient to state this in inner-product form as
$$langle mathbf z - mathbf x, mathbf y - mathbf x rangle le 0.$$
To prove this, start by observing that for every $t in [0,1]$, $(1-t)mathbf x + t mathbf z in mathcal K$, and so the function $phi(t) = |(1-t)mathbf x + t mathbf z - mathbf y|^2$ is minimized on $[0,1]$ by $t=0$. Then do some calculus to $phi(t)$ to figure out when this happens, and the inner-product condition comes out as the result.
(We can expand $phi(t) = |mathbf y - mathbf x - t(mathbf z - mathbf x)|^2$ to $|mathbf y - mathbf x|^2 - 2t langle mathbf y - mathbf x, mathbf z - mathbf xrangle + t^2 |mathbf z - mathbf x|^2$. This parabola has vertex at $t = -fracb2a = fraclangle mathbf y - mathbf x, mathbf z - mathbf xrangle2$, and we want this to be less than or equal to $0$, giving us the obtuse angle criterion.)
The theorem that $|mathbf y - mathbf z| ge |mathbf x - mathbf z|$ is a geometric consequence of the obtuse angle criterion. Consider the triangle with vertices $mathbf x, mathbf y, mathbf z$. Then because the angle at $mathbf x$ is right or obtuse, it is the largest angle of the triangle. Therefore the opposite side of the triangle - the side from $mathbf y$ to $mathbf z$ - is its longest side.
I am guessing that this is where the connection to the Pythagorean theorem comes from. More precisely, we can deduce this inequality about triangles from the Law of Cosines, a generalization of the Pythagorean theorem. In a triangle $triangle ABC$, we have $$AB^2 = AC^2 + BC^2 - 2cdot AB cdot BC cdot cos angle C,$$ and if $angle C$ is right or obtuse, this implies that $AB^2 ge AC^2 + BC^2$. So in particular, $AB^2 ge AC^2$ and $AB^2 ge BC^2$: the side opposite $angle C$ is the longest side.
(Of course, the attribution of this result to Pythagoras is not serious.)
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
 |Â
show 6 more comments
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
3
down vote
Suppose $x$ is the closest point to $y$ in the closed convex set $K$.
If $x=y$ there is nothing to prove, so we can suppose $xneq y$.
The we have that $langle x-y, z -x rangle ge 0$ for all $z in K$ (this is essentially the dual problem).
If $z in K$, we can write $z-y = t(x-y)+ d$, where $d bot (x-y)$. Then the above gives
$langle x-y, t(x-y)+ d +y -x rangle = (t-1)|x-y|^2ge 0$ from which we
get $t ge 1$.
Then $|z-y|^2 = |d|^2+ t^2 |x-y|^2 ge |x-y|^2$ which is the desired result.
Addendum: To see the first condition, suppose $|z-y| ge |y-x|$ for all $z in K$.
We have $|z-y|^2 = |z-x+x-y|^2 = |z-x|^2 + |x-y|^2 + 2 langle z-x,x-y rangle $ (this is where Pythagoras appears) which gives
$|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ for all $z in K$.
Since $w(t)=x+t(z-x) in K$ for all $t in [0,1]$, we have
$t^2 |z-x|^2 + 2 t langle z-x,x-y rangle ge 0$, dividing across by $t$ and letting $t downarrow 0$ yields the desired result.
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
add a comment |Â
up vote
3
down vote
Suppose $x$ is the closest point to $y$ in the closed convex set $K$.
If $x=y$ there is nothing to prove, so we can suppose $xneq y$.
The we have that $langle x-y, z -x rangle ge 0$ for all $z in K$ (this is essentially the dual problem).
If $z in K$, we can write $z-y = t(x-y)+ d$, where $d bot (x-y)$. Then the above gives
$langle x-y, t(x-y)+ d +y -x rangle = (t-1)|x-y|^2ge 0$ from which we
get $t ge 1$.
Then $|z-y|^2 = |d|^2+ t^2 |x-y|^2 ge |x-y|^2$ which is the desired result.
Addendum: To see the first condition, suppose $|z-y| ge |y-x|$ for all $z in K$.
We have $|z-y|^2 = |z-x+x-y|^2 = |z-x|^2 + |x-y|^2 + 2 langle z-x,x-y rangle $ (this is where Pythagoras appears) which gives
$|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ for all $z in K$.
Since $w(t)=x+t(z-x) in K$ for all $t in [0,1]$, we have
$t^2 |z-x|^2 + 2 t langle z-x,x-y rangle ge 0$, dividing across by $t$ and letting $t downarrow 0$ yields the desired result.
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
add a comment |Â
up vote
3
down vote
up vote
3
down vote
Suppose $x$ is the closest point to $y$ in the closed convex set $K$.
If $x=y$ there is nothing to prove, so we can suppose $xneq y$.
The we have that $langle x-y, z -x rangle ge 0$ for all $z in K$ (this is essentially the dual problem).
If $z in K$, we can write $z-y = t(x-y)+ d$, where $d bot (x-y)$. Then the above gives
$langle x-y, t(x-y)+ d +y -x rangle = (t-1)|x-y|^2ge 0$ from which we
get $t ge 1$.
Then $|z-y|^2 = |d|^2+ t^2 |x-y|^2 ge |x-y|^2$ which is the desired result.
Addendum: To see the first condition, suppose $|z-y| ge |y-x|$ for all $z in K$.
We have $|z-y|^2 = |z-x+x-y|^2 = |z-x|^2 + |x-y|^2 + 2 langle z-x,x-y rangle $ (this is where Pythagoras appears) which gives
$|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ for all $z in K$.
Since $w(t)=x+t(z-x) in K$ for all $t in [0,1]$, we have
$t^2 |z-x|^2 + 2 t langle z-x,x-y rangle ge 0$, dividing across by $t$ and letting $t downarrow 0$ yields the desired result.
Suppose $x$ is the closest point to $y$ in the closed convex set $K$.
If $x=y$ there is nothing to prove, so we can suppose $xneq y$.
The we have that $langle x-y, z -x rangle ge 0$ for all $z in K$ (this is essentially the dual problem).
If $z in K$, we can write $z-y = t(x-y)+ d$, where $d bot (x-y)$. Then the above gives
$langle x-y, t(x-y)+ d +y -x rangle = (t-1)|x-y|^2ge 0$ from which we
get $t ge 1$.
Then $|z-y|^2 = |d|^2+ t^2 |x-y|^2 ge |x-y|^2$ which is the desired result.
Addendum: To see the first condition, suppose $|z-y| ge |y-x|$ for all $z in K$.
We have $|z-y|^2 = |z-x+x-y|^2 = |z-x|^2 + |x-y|^2 + 2 langle z-x,x-y rangle $ (this is where Pythagoras appears) which gives
$|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ for all $z in K$.
Since $w(t)=x+t(z-x) in K$ for all $t in [0,1]$, we have
$t^2 |z-x|^2 + 2 t langle z-x,x-y rangle ge 0$, dividing across by $t$ and letting $t downarrow 0$ yields the desired result.
edited May 31 at 5:37
answered May 31 at 5:31
copper.hat
123k557156
123k557156
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
add a comment |Â
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@ copper.hat: Could you please explain the last two lines, especially getting the inequality in terms of $t$ from $w(t)$? Also, in Addendum, why do you need two inequalities, one without $t$ and one with $t$?
â Saeed
Aug 12 at 1:56
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
@Saeed: If you substitute $w(t)$ for $z$ in $|z-x|^2 + 2 langle z-x,x-y rangle ge 0$ you will get the last inequality. Then divide by $t$ and let $t to 0$. The goal is to show the first order condition of optimality, $langle x-y, z -x rangle ge 0$ for all $z in K$.
â copper.hat
Aug 12 at 8:02
add a comment |Â
up vote
1
down vote
Relevant is the so-called "obtuse angle criterion":
Let $mathcal K subseteq mathbb R^d $ be a convex set, $mathbf y in mathbb R^d setminus mathcal K$, and $mathbf x = Pi_mathcal K(mathbf y)$ (that is, $mathbf x$ is the closest point in the set $mathcal K$ to $mathbf y$) . Then for any point $mathbf z in mathcal K$, the angle between $mathbf z-mathbf x$ and $mathbf y - mathbf x$ is obtuse or right.
(We assume that $mathbf x$ exists; it is guaranteed to exist if $mathcal K$ is closed and nonempty.)
It's often convenient to state this in inner-product form as
$$langle mathbf z - mathbf x, mathbf y - mathbf x rangle le 0.$$
To prove this, start by observing that for every $t in [0,1]$, $(1-t)mathbf x + t mathbf z in mathcal K$, and so the function $phi(t) = |(1-t)mathbf x + t mathbf z - mathbf y|^2$ is minimized on $[0,1]$ by $t=0$. Then do some calculus to $phi(t)$ to figure out when this happens, and the inner-product condition comes out as the result.
(We can expand $phi(t) = |mathbf y - mathbf x - t(mathbf z - mathbf x)|^2$ to $|mathbf y - mathbf x|^2 - 2t langle mathbf y - mathbf x, mathbf z - mathbf xrangle + t^2 |mathbf z - mathbf x|^2$. This parabola has vertex at $t = -fracb2a = fraclangle mathbf y - mathbf x, mathbf z - mathbf xrangle2$, and we want this to be less than or equal to $0$, giving us the obtuse angle criterion.)
The theorem that $|mathbf y - mathbf z| ge |mathbf x - mathbf z|$ is a geometric consequence of the obtuse angle criterion. Consider the triangle with vertices $mathbf x, mathbf y, mathbf z$. Then because the angle at $mathbf x$ is right or obtuse, it is the largest angle of the triangle. Therefore the opposite side of the triangle - the side from $mathbf y$ to $mathbf z$ - is its longest side.
I am guessing that this is where the connection to the Pythagorean theorem comes from. More precisely, we can deduce this inequality about triangles from the Law of Cosines, a generalization of the Pythagorean theorem. In a triangle $triangle ABC$, we have $$AB^2 = AC^2 + BC^2 - 2cdot AB cdot BC cdot cos angle C,$$ and if $angle C$ is right or obtuse, this implies that $AB^2 ge AC^2 + BC^2$. So in particular, $AB^2 ge AC^2$ and $AB^2 ge BC^2$: the side opposite $angle C$ is the longest side.
(Of course, the attribution of this result to Pythagoras is not serious.)
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
 |Â
show 6 more comments
up vote
1
down vote
Relevant is the so-called "obtuse angle criterion":
Let $mathcal K subseteq mathbb R^d $ be a convex set, $mathbf y in mathbb R^d setminus mathcal K$, and $mathbf x = Pi_mathcal K(mathbf y)$ (that is, $mathbf x$ is the closest point in the set $mathcal K$ to $mathbf y$) . Then for any point $mathbf z in mathcal K$, the angle between $mathbf z-mathbf x$ and $mathbf y - mathbf x$ is obtuse or right.
(We assume that $mathbf x$ exists; it is guaranteed to exist if $mathcal K$ is closed and nonempty.)
It's often convenient to state this in inner-product form as
$$langle mathbf z - mathbf x, mathbf y - mathbf x rangle le 0.$$
To prove this, start by observing that for every $t in [0,1]$, $(1-t)mathbf x + t mathbf z in mathcal K$, and so the function $phi(t) = |(1-t)mathbf x + t mathbf z - mathbf y|^2$ is minimized on $[0,1]$ by $t=0$. Then do some calculus to $phi(t)$ to figure out when this happens, and the inner-product condition comes out as the result.
(We can expand $phi(t) = |mathbf y - mathbf x - t(mathbf z - mathbf x)|^2$ to $|mathbf y - mathbf x|^2 - 2t langle mathbf y - mathbf x, mathbf z - mathbf xrangle + t^2 |mathbf z - mathbf x|^2$. This parabola has vertex at $t = -fracb2a = fraclangle mathbf y - mathbf x, mathbf z - mathbf xrangle2$, and we want this to be less than or equal to $0$, giving us the obtuse angle criterion.)
The theorem that $|mathbf y - mathbf z| ge |mathbf x - mathbf z|$ is a geometric consequence of the obtuse angle criterion. Consider the triangle with vertices $mathbf x, mathbf y, mathbf z$. Then because the angle at $mathbf x$ is right or obtuse, it is the largest angle of the triangle. Therefore the opposite side of the triangle - the side from $mathbf y$ to $mathbf z$ - is its longest side.
I am guessing that this is where the connection to the Pythagorean theorem comes from. More precisely, we can deduce this inequality about triangles from the Law of Cosines, a generalization of the Pythagorean theorem. In a triangle $triangle ABC$, we have $$AB^2 = AC^2 + BC^2 - 2cdot AB cdot BC cdot cos angle C,$$ and if $angle C$ is right or obtuse, this implies that $AB^2 ge AC^2 + BC^2$. So in particular, $AB^2 ge AC^2$ and $AB^2 ge BC^2$: the side opposite $angle C$ is the longest side.
(Of course, the attribution of this result to Pythagoras is not serious.)
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
 |Â
show 6 more comments
up vote
1
down vote
up vote
1
down vote
Relevant is the so-called "obtuse angle criterion":
Let $mathcal K subseteq mathbb R^d $ be a convex set, $mathbf y in mathbb R^d setminus mathcal K$, and $mathbf x = Pi_mathcal K(mathbf y)$ (that is, $mathbf x$ is the closest point in the set $mathcal K$ to $mathbf y$) . Then for any point $mathbf z in mathcal K$, the angle between $mathbf z-mathbf x$ and $mathbf y - mathbf x$ is obtuse or right.
(We assume that $mathbf x$ exists; it is guaranteed to exist if $mathcal K$ is closed and nonempty.)
It's often convenient to state this in inner-product form as
$$langle mathbf z - mathbf x, mathbf y - mathbf x rangle le 0.$$
To prove this, start by observing that for every $t in [0,1]$, $(1-t)mathbf x + t mathbf z in mathcal K$, and so the function $phi(t) = |(1-t)mathbf x + t mathbf z - mathbf y|^2$ is minimized on $[0,1]$ by $t=0$. Then do some calculus to $phi(t)$ to figure out when this happens, and the inner-product condition comes out as the result.
(We can expand $phi(t) = |mathbf y - mathbf x - t(mathbf z - mathbf x)|^2$ to $|mathbf y - mathbf x|^2 - 2t langle mathbf y - mathbf x, mathbf z - mathbf xrangle + t^2 |mathbf z - mathbf x|^2$. This parabola has vertex at $t = -fracb2a = fraclangle mathbf y - mathbf x, mathbf z - mathbf xrangle2$, and we want this to be less than or equal to $0$, giving us the obtuse angle criterion.)
The theorem that $|mathbf y - mathbf z| ge |mathbf x - mathbf z|$ is a geometric consequence of the obtuse angle criterion. Consider the triangle with vertices $mathbf x, mathbf y, mathbf z$. Then because the angle at $mathbf x$ is right or obtuse, it is the largest angle of the triangle. Therefore the opposite side of the triangle - the side from $mathbf y$ to $mathbf z$ - is its longest side.
I am guessing that this is where the connection to the Pythagorean theorem comes from. More precisely, we can deduce this inequality about triangles from the Law of Cosines, a generalization of the Pythagorean theorem. In a triangle $triangle ABC$, we have $$AB^2 = AC^2 + BC^2 - 2cdot AB cdot BC cdot cos angle C,$$ and if $angle C$ is right or obtuse, this implies that $AB^2 ge AC^2 + BC^2$. So in particular, $AB^2 ge AC^2$ and $AB^2 ge BC^2$: the side opposite $angle C$ is the longest side.
(Of course, the attribution of this result to Pythagoras is not serious.)
Relevant is the so-called "obtuse angle criterion":
Let $mathcal K subseteq mathbb R^d $ be a convex set, $mathbf y in mathbb R^d setminus mathcal K$, and $mathbf x = Pi_mathcal K(mathbf y)$ (that is, $mathbf x$ is the closest point in the set $mathcal K$ to $mathbf y$) . Then for any point $mathbf z in mathcal K$, the angle between $mathbf z-mathbf x$ and $mathbf y - mathbf x$ is obtuse or right.
(We assume that $mathbf x$ exists; it is guaranteed to exist if $mathcal K$ is closed and nonempty.)
It's often convenient to state this in inner-product form as
$$langle mathbf z - mathbf x, mathbf y - mathbf x rangle le 0.$$
To prove this, start by observing that for every $t in [0,1]$, $(1-t)mathbf x + t mathbf z in mathcal K$, and so the function $phi(t) = |(1-t)mathbf x + t mathbf z - mathbf y|^2$ is minimized on $[0,1]$ by $t=0$. Then do some calculus to $phi(t)$ to figure out when this happens, and the inner-product condition comes out as the result.
(We can expand $phi(t) = |mathbf y - mathbf x - t(mathbf z - mathbf x)|^2$ to $|mathbf y - mathbf x|^2 - 2t langle mathbf y - mathbf x, mathbf z - mathbf xrangle + t^2 |mathbf z - mathbf x|^2$. This parabola has vertex at $t = -fracb2a = fraclangle mathbf y - mathbf x, mathbf z - mathbf xrangle2$, and we want this to be less than or equal to $0$, giving us the obtuse angle criterion.)
The theorem that $|mathbf y - mathbf z| ge |mathbf x - mathbf z|$ is a geometric consequence of the obtuse angle criterion. Consider the triangle with vertices $mathbf x, mathbf y, mathbf z$. Then because the angle at $mathbf x$ is right or obtuse, it is the largest angle of the triangle. Therefore the opposite side of the triangle - the side from $mathbf y$ to $mathbf z$ - is its longest side.
I am guessing that this is where the connection to the Pythagorean theorem comes from. More precisely, we can deduce this inequality about triangles from the Law of Cosines, a generalization of the Pythagorean theorem. In a triangle $triangle ABC$, we have $$AB^2 = AC^2 + BC^2 - 2cdot AB cdot BC cdot cos angle C,$$ and if $angle C$ is right or obtuse, this implies that $AB^2 ge AC^2 + BC^2$. So in particular, $AB^2 ge AC^2$ and $AB^2 ge BC^2$: the side opposite $angle C$ is the longest side.
(Of course, the attribution of this result to Pythagoras is not serious.)
edited Aug 11 at 22:28
answered May 31 at 2:06
Misha Lavrov
35.9k54691
35.9k54691
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
 |Â
show 6 more comments
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
Does the proof of the inner-product condition hold for arbitrary normed spaces?
â Reginald
May 31 at 4:52
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
@Reginald: An arbitrary normed space does not have an inner product. Also. there is not necessarily a unique closest point in an arbitrary normed space (take the $l_1$ norm in the plane for example).
â copper.hat
May 31 at 5:35
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
Rather, arbitrary IP space
â Reginald
May 31 at 5:42
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
@Reginald: The result holds for closed convex sets in a Hilbert space.
â copper.hat
May 31 at 13:11
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
Regarding uniqueness: for this result to hold, we need not assume $mathbf x$ is the unique closest point, but a closest point. (Which implies uniqueness for closest points in convex sets, whenever they exist.)
â Misha Lavrov
May 31 at 14:42
 |Â
show 6 more comments
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2802569%2fpythagorean-theorem-for-projection-onto-convex-set%23new-answer', 'question_page');
);
Post as a guest
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
The notes are a bit sloppy. The set $K$ should be closed to guarantee that a projection (as defined in Chapter 2, not 1 as you wrote above). It is a bit disingenuous to label the result as 500 BC.
â copper.hat
May 31 at 4:51