Is $f(x,y) = xlog(x)+ylog(y)$ a coercive function?
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From Peressini, Sullivan, Uhl, the mathematics of nonlinear programming,
A function is coercive if
$limlimits_{|x| to infty} f(x) to infty$
and super-coercive if,
$limlimits_{|x| to infty} dfrac{f(x)}{|x|} to infty$
I want to know if $f(x,y) = xlog(x)+ylog(y)$ is a (super) coercive function
If I apply the definition, I get,
$limlimits_{|(x,y)| to infty} xlog(x)+ylog(y)$
or
$limlimits_{|(x,y)| to infty} dfrac{xlog(x)+ylog(y)}{sqrt{x^2 + y^2}}$
But there is no obvious dependence on $|(x,y)|$ for $f(x,y)$. Is there any way I can argue that this function is or isn't coercive.
real-analysis optimization convex-analysis convex-optimization nonlinear-optimization
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up vote
1
down vote
favorite
From Peressini, Sullivan, Uhl, the mathematics of nonlinear programming,
A function is coercive if
$limlimits_{|x| to infty} f(x) to infty$
and super-coercive if,
$limlimits_{|x| to infty} dfrac{f(x)}{|x|} to infty$
I want to know if $f(x,y) = xlog(x)+ylog(y)$ is a (super) coercive function
If I apply the definition, I get,
$limlimits_{|(x,y)| to infty} xlog(x)+ylog(y)$
or
$limlimits_{|(x,y)| to infty} dfrac{xlog(x)+ylog(y)}{sqrt{x^2 + y^2}}$
But there is no obvious dependence on $|(x,y)|$ for $f(x,y)$. Is there any way I can argue that this function is or isn't coercive.
real-analysis optimization convex-analysis convex-optimization nonlinear-optimization
add a comment |
up vote
1
down vote
favorite
up vote
1
down vote
favorite
From Peressini, Sullivan, Uhl, the mathematics of nonlinear programming,
A function is coercive if
$limlimits_{|x| to infty} f(x) to infty$
and super-coercive if,
$limlimits_{|x| to infty} dfrac{f(x)}{|x|} to infty$
I want to know if $f(x,y) = xlog(x)+ylog(y)$ is a (super) coercive function
If I apply the definition, I get,
$limlimits_{|(x,y)| to infty} xlog(x)+ylog(y)$
or
$limlimits_{|(x,y)| to infty} dfrac{xlog(x)+ylog(y)}{sqrt{x^2 + y^2}}$
But there is no obvious dependence on $|(x,y)|$ for $f(x,y)$. Is there any way I can argue that this function is or isn't coercive.
real-analysis optimization convex-analysis convex-optimization nonlinear-optimization
From Peressini, Sullivan, Uhl, the mathematics of nonlinear programming,
A function is coercive if
$limlimits_{|x| to infty} f(x) to infty$
and super-coercive if,
$limlimits_{|x| to infty} dfrac{f(x)}{|x|} to infty$
I want to know if $f(x,y) = xlog(x)+ylog(y)$ is a (super) coercive function
If I apply the definition, I get,
$limlimits_{|(x,y)| to infty} xlog(x)+ylog(y)$
or
$limlimits_{|(x,y)| to infty} dfrac{xlog(x)+ylog(y)}{sqrt{x^2 + y^2}}$
But there is no obvious dependence on $|(x,y)|$ for $f(x,y)$. Is there any way I can argue that this function is or isn't coercive.
real-analysis optimization convex-analysis convex-optimization nonlinear-optimization
real-analysis optimization convex-analysis convex-optimization nonlinear-optimization
asked Dec 3 at 3:15
Squaring the Circle is Easy
585
585
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2 Answers
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up vote
1
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Let $x = rcos t, y = rsin t$ with $0leq tleq frac pi 2.$ Then:
$$dfrac{f(x,y)}{r} = dfrac{rcos tlog(rcos t)+rsin tlog(rsin t)}{r}=$$
$$=log(r)(cos t+sin t)+cos tlogcos t+sin tlogsin t.$$
Can you take it from here?
You can prove that:
$$g(t) = cos tlogcos t+sin tlogsin t$$ is bounded below when $0<t<pi/2$ by some simple calculus.
add a comment |
up vote
0
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Since $f(x,y)=g(x)+g(y)$ where $g(x)=xlog(x)$ you can bound it with a one-dimensional function.
First, presume without loss of generality that $xgeq ygeq0$. Then $left|(x,y)right|=sqrt{x^2+y^2}leqsqrt{2}x$.
Secondly, we have $g(x)$ is bounded below hence $f(x,y)=g(x)+g(y)geq g(x)+C$ for some finite constant $C=min_{xgeq0}g(x)$.
Now we can get
$$lim_{stackrel{left|(x,y)right|rightarrowinfty}{xgeq y}}frac{f(x,y)}{left|(x,y)right|}geqlim_{xrightarrowinfty}frac{g(x)+C}{sqrt{2}x}$$
and you can simply prove that $g(x)$ (hence $g(x)/sqrt{2}$) is (super-)coercive in one dimension.
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
up vote
1
down vote
Let $x = rcos t, y = rsin t$ with $0leq tleq frac pi 2.$ Then:
$$dfrac{f(x,y)}{r} = dfrac{rcos tlog(rcos t)+rsin tlog(rsin t)}{r}=$$
$$=log(r)(cos t+sin t)+cos tlogcos t+sin tlogsin t.$$
Can you take it from here?
You can prove that:
$$g(t) = cos tlogcos t+sin tlogsin t$$ is bounded below when $0<t<pi/2$ by some simple calculus.
add a comment |
up vote
1
down vote
Let $x = rcos t, y = rsin t$ with $0leq tleq frac pi 2.$ Then:
$$dfrac{f(x,y)}{r} = dfrac{rcos tlog(rcos t)+rsin tlog(rsin t)}{r}=$$
$$=log(r)(cos t+sin t)+cos tlogcos t+sin tlogsin t.$$
Can you take it from here?
You can prove that:
$$g(t) = cos tlogcos t+sin tlogsin t$$ is bounded below when $0<t<pi/2$ by some simple calculus.
add a comment |
up vote
1
down vote
up vote
1
down vote
Let $x = rcos t, y = rsin t$ with $0leq tleq frac pi 2.$ Then:
$$dfrac{f(x,y)}{r} = dfrac{rcos tlog(rcos t)+rsin tlog(rsin t)}{r}=$$
$$=log(r)(cos t+sin t)+cos tlogcos t+sin tlogsin t.$$
Can you take it from here?
You can prove that:
$$g(t) = cos tlogcos t+sin tlogsin t$$ is bounded below when $0<t<pi/2$ by some simple calculus.
Let $x = rcos t, y = rsin t$ with $0leq tleq frac pi 2.$ Then:
$$dfrac{f(x,y)}{r} = dfrac{rcos tlog(rcos t)+rsin tlog(rsin t)}{r}=$$
$$=log(r)(cos t+sin t)+cos tlogcos t+sin tlogsin t.$$
Can you take it from here?
You can prove that:
$$g(t) = cos tlogcos t+sin tlogsin t$$ is bounded below when $0<t<pi/2$ by some simple calculus.
edited Dec 3 at 3:55
answered Dec 3 at 3:39
dezdichado
5,9341929
5,9341929
add a comment |
add a comment |
up vote
0
down vote
Since $f(x,y)=g(x)+g(y)$ where $g(x)=xlog(x)$ you can bound it with a one-dimensional function.
First, presume without loss of generality that $xgeq ygeq0$. Then $left|(x,y)right|=sqrt{x^2+y^2}leqsqrt{2}x$.
Secondly, we have $g(x)$ is bounded below hence $f(x,y)=g(x)+g(y)geq g(x)+C$ for some finite constant $C=min_{xgeq0}g(x)$.
Now we can get
$$lim_{stackrel{left|(x,y)right|rightarrowinfty}{xgeq y}}frac{f(x,y)}{left|(x,y)right|}geqlim_{xrightarrowinfty}frac{g(x)+C}{sqrt{2}x}$$
and you can simply prove that $g(x)$ (hence $g(x)/sqrt{2}$) is (super-)coercive in one dimension.
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
add a comment |
up vote
0
down vote
Since $f(x,y)=g(x)+g(y)$ where $g(x)=xlog(x)$ you can bound it with a one-dimensional function.
First, presume without loss of generality that $xgeq ygeq0$. Then $left|(x,y)right|=sqrt{x^2+y^2}leqsqrt{2}x$.
Secondly, we have $g(x)$ is bounded below hence $f(x,y)=g(x)+g(y)geq g(x)+C$ for some finite constant $C=min_{xgeq0}g(x)$.
Now we can get
$$lim_{stackrel{left|(x,y)right|rightarrowinfty}{xgeq y}}frac{f(x,y)}{left|(x,y)right|}geqlim_{xrightarrowinfty}frac{g(x)+C}{sqrt{2}x}$$
and you can simply prove that $g(x)$ (hence $g(x)/sqrt{2}$) is (super-)coercive in one dimension.
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
add a comment |
up vote
0
down vote
up vote
0
down vote
Since $f(x,y)=g(x)+g(y)$ where $g(x)=xlog(x)$ you can bound it with a one-dimensional function.
First, presume without loss of generality that $xgeq ygeq0$. Then $left|(x,y)right|=sqrt{x^2+y^2}leqsqrt{2}x$.
Secondly, we have $g(x)$ is bounded below hence $f(x,y)=g(x)+g(y)geq g(x)+C$ for some finite constant $C=min_{xgeq0}g(x)$.
Now we can get
$$lim_{stackrel{left|(x,y)right|rightarrowinfty}{xgeq y}}frac{f(x,y)}{left|(x,y)right|}geqlim_{xrightarrowinfty}frac{g(x)+C}{sqrt{2}x}$$
and you can simply prove that $g(x)$ (hence $g(x)/sqrt{2}$) is (super-)coercive in one dimension.
Since $f(x,y)=g(x)+g(y)$ where $g(x)=xlog(x)$ you can bound it with a one-dimensional function.
First, presume without loss of generality that $xgeq ygeq0$. Then $left|(x,y)right|=sqrt{x^2+y^2}leqsqrt{2}x$.
Secondly, we have $g(x)$ is bounded below hence $f(x,y)=g(x)+g(y)geq g(x)+C$ for some finite constant $C=min_{xgeq0}g(x)$.
Now we can get
$$lim_{stackrel{left|(x,y)right|rightarrowinfty}{xgeq y}}frac{f(x,y)}{left|(x,y)right|}geqlim_{xrightarrowinfty}frac{g(x)+C}{sqrt{2}x}$$
and you can simply prove that $g(x)$ (hence $g(x)/sqrt{2}$) is (super-)coercive in one dimension.
edited Dec 3 at 4:00
answered Dec 3 at 3:32
obscurans
60519
60519
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
add a comment |
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
$g(y)$ can be negative for small $y,$ so the lower bound may not hold.
– dezdichado
Dec 3 at 3:45
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
Thanks, need bounded below instead
– obscurans
Dec 3 at 3:50
add a comment |
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