Finding the positive root of $x^3 +x^2 =0.1$ by numerical methods.
$begingroup$
The positive root of $x^3 +x^2 =0.1$ is denoted to be $A$.
$(a)$ Find the first approximation to $A$ by linear interpolation on the interval $(0,1)$
For this, I got $x_1 =0.05$
$(b)$ Indicate why linear interpolation does not give a good approximation to $A$.
All I think of for this is that $x_1$ by linear interpolation greatly underestimates $A$ to a large extent. But wouldn't numerical methods like Newton-Raphson, Linear Interpolation and iteration $x_{n+1}=F(x_n)$ all give bad first approximations?
$(c)$ Find an alternative first approximation to $A$ by using the fact that if $x$ is small then $x^3$ is negligible compared with $x^2$
So, for this am I supposed to use Newton-Raphson with $x_1=0$ since $x$ is small?
numerical-methods roots
$endgroup$
add a comment |
$begingroup$
The positive root of $x^3 +x^2 =0.1$ is denoted to be $A$.
$(a)$ Find the first approximation to $A$ by linear interpolation on the interval $(0,1)$
For this, I got $x_1 =0.05$
$(b)$ Indicate why linear interpolation does not give a good approximation to $A$.
All I think of for this is that $x_1$ by linear interpolation greatly underestimates $A$ to a large extent. But wouldn't numerical methods like Newton-Raphson, Linear Interpolation and iteration $x_{n+1}=F(x_n)$ all give bad first approximations?
$(c)$ Find an alternative first approximation to $A$ by using the fact that if $x$ is small then $x^3$ is negligible compared with $x^2$
So, for this am I supposed to use Newton-Raphson with $x_1=0$ since $x$ is small?
numerical-methods roots
$endgroup$
add a comment |
$begingroup$
The positive root of $x^3 +x^2 =0.1$ is denoted to be $A$.
$(a)$ Find the first approximation to $A$ by linear interpolation on the interval $(0,1)$
For this, I got $x_1 =0.05$
$(b)$ Indicate why linear interpolation does not give a good approximation to $A$.
All I think of for this is that $x_1$ by linear interpolation greatly underestimates $A$ to a large extent. But wouldn't numerical methods like Newton-Raphson, Linear Interpolation and iteration $x_{n+1}=F(x_n)$ all give bad first approximations?
$(c)$ Find an alternative first approximation to $A$ by using the fact that if $x$ is small then $x^3$ is negligible compared with $x^2$
So, for this am I supposed to use Newton-Raphson with $x_1=0$ since $x$ is small?
numerical-methods roots
$endgroup$
The positive root of $x^3 +x^2 =0.1$ is denoted to be $A$.
$(a)$ Find the first approximation to $A$ by linear interpolation on the interval $(0,1)$
For this, I got $x_1 =0.05$
$(b)$ Indicate why linear interpolation does not give a good approximation to $A$.
All I think of for this is that $x_1$ by linear interpolation greatly underestimates $A$ to a large extent. But wouldn't numerical methods like Newton-Raphson, Linear Interpolation and iteration $x_{n+1}=F(x_n)$ all give bad first approximations?
$(c)$ Find an alternative first approximation to $A$ by using the fact that if $x$ is small then $x^3$ is negligible compared with $x^2$
So, for this am I supposed to use Newton-Raphson with $x_1=0$ since $x$ is small?
numerical-methods roots
numerical-methods roots
asked Nov 26 '18 at 13:27
Arc NeoepiArc Neoepi
405413
405413
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
$begingroup$
(a) Correct. (b) Yes. It is also bad because $1$ is far away from the root, as can be seen comparing the function values. And the function is very non-linear around $x=0$, making linear approximations relatively bad.
(c) No, you are supposed to solve $x^2=0.1$, disregarding the $x^3$ term. Or to put it into inequalities, as $0<x<1$ you also have
$$
x^2le 0.1le 2x^2implies sqrt{0.05}le x le sqrt{0.1}.
$$
Then iterate further, for instance using $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$.
k x[k]
------------------
1 0.316227766017
2 0.275635071544
3 0.279986295554
4 0.279509993492
5 0.279562012937
6 0.279556330208
7 0.279556950986
8 0.279556883172
9 0.27955689058
$endgroup$
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
1
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
1
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
add a comment |
$begingroup$
Just for your curiosity.
We can approximate functions using Padé approximants much better than with Taylor series (remember that Newton method is equivalent to an $O(x^2)$ Taylor expansion).
Since we know how to solve easily quadratic equations, let us consider the $[2,2]$ Padé approximant. It will be
$$x^3+x^2 simfrac{x^2}{x^2-x+1}$$ If you develop the rhs of the above as a Taylor series, you would get $x^2+x^3-x^5+Oleft(x^6right)$ (pretty close, isn't it ?).
So, for small values of $a$, the approximate solution of $x^3+x^2=a$ is given by the solution of $(1-a) x^2+a x-a=0$ that is to say
$$x_pm=frac{apmsqrt{4 a-3 a^2}}{2 (a-1)}$$ So, for $a=frac 1 {10}$, the estimate would be $xapprox 0.282376$ which is quite close to the "exact" solution $xapprox 0.279557$
$endgroup$
add a comment |
$begingroup$
$newcommand{bbx}[1]{,bbox[15px,border:1px groove navy]{displaystyle{#1}},}
newcommand{braces}[1]{leftlbrace,{#1},rightrbrace}
newcommand{bracks}[1]{leftlbrack,{#1},rightrbrack}
newcommand{dd}{mathrm{d}}
newcommand{ds}[1]{displaystyle{#1}}
newcommand{expo}[1]{,mathrm{e}^{#1},}
newcommand{ic}{mathrm{i}}
newcommand{mc}[1]{mathcal{#1}}
newcommand{mrm}[1]{mathrm{#1}}
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newcommand{partiald}[3]{frac{partial^{#1} #2}{partial #3^{#1}}}
newcommand{root}[2]{,sqrt[#1]{,{#2},},}
newcommand{totald}[3]{frac{mathrm{d}^{#1} #2}{mathrm{d} #3^{#1}}}
newcommand{verts}[1]{leftvert,{#1},rightvert}$
- Start with the iteration
$$
x_{n} = root{0.1 - x_{n}^{3}},,qquad
x_{0} = root{0.1} approx 0.3162
$$
It seems to be that $ds{10}$ iterations are enough. Namely, $$
x_{10} approx 0.279564quadmbox{and}quadmrm{f}pars{x_{10}} approx 5.43129 times 10^{-6}
$$
where $bbox[10px,#ffd,border: 1px groove navy]{ds{mrm{f}pars{x} equiv x^{3} + x^{2} - 0.1}}$. - In addition, you can refine the above result by means of a Newton-Rapson Iteration:
$$
y_{n} = y_{n - 1} - {y_{n - 1}^{3} + y_{n - 1}^{2} - 0.1 over
3y_{n - 1}^{2} + 2y_{n - 1}},,qquad y_{0} = x_{10} approx 0.279564
$$
With about three iterations, I'll find
$$
y_{3} approx bbx{large 0.279957} implies mrm{f}pars{y_{3}} approx
2.77556 times10^{-17}
$$
begin{align}
end{align}
$endgroup$
add a comment |
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3 Answers
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active
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3 Answers
3
active
oldest
votes
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votes
$begingroup$
(a) Correct. (b) Yes. It is also bad because $1$ is far away from the root, as can be seen comparing the function values. And the function is very non-linear around $x=0$, making linear approximations relatively bad.
(c) No, you are supposed to solve $x^2=0.1$, disregarding the $x^3$ term. Or to put it into inequalities, as $0<x<1$ you also have
$$
x^2le 0.1le 2x^2implies sqrt{0.05}le x le sqrt{0.1}.
$$
Then iterate further, for instance using $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$.
k x[k]
------------------
1 0.316227766017
2 0.275635071544
3 0.279986295554
4 0.279509993492
5 0.279562012937
6 0.279556330208
7 0.279556950986
8 0.279556883172
9 0.27955689058
$endgroup$
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
1
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
1
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
add a comment |
$begingroup$
(a) Correct. (b) Yes. It is also bad because $1$ is far away from the root, as can be seen comparing the function values. And the function is very non-linear around $x=0$, making linear approximations relatively bad.
(c) No, you are supposed to solve $x^2=0.1$, disregarding the $x^3$ term. Or to put it into inequalities, as $0<x<1$ you also have
$$
x^2le 0.1le 2x^2implies sqrt{0.05}le x le sqrt{0.1}.
$$
Then iterate further, for instance using $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$.
k x[k]
------------------
1 0.316227766017
2 0.275635071544
3 0.279986295554
4 0.279509993492
5 0.279562012937
6 0.279556330208
7 0.279556950986
8 0.279556883172
9 0.27955689058
$endgroup$
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
1
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
1
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
add a comment |
$begingroup$
(a) Correct. (b) Yes. It is also bad because $1$ is far away from the root, as can be seen comparing the function values. And the function is very non-linear around $x=0$, making linear approximations relatively bad.
(c) No, you are supposed to solve $x^2=0.1$, disregarding the $x^3$ term. Or to put it into inequalities, as $0<x<1$ you also have
$$
x^2le 0.1le 2x^2implies sqrt{0.05}le x le sqrt{0.1}.
$$
Then iterate further, for instance using $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$.
k x[k]
------------------
1 0.316227766017
2 0.275635071544
3 0.279986295554
4 0.279509993492
5 0.279562012937
6 0.279556330208
7 0.279556950986
8 0.279556883172
9 0.27955689058
$endgroup$
(a) Correct. (b) Yes. It is also bad because $1$ is far away from the root, as can be seen comparing the function values. And the function is very non-linear around $x=0$, making linear approximations relatively bad.
(c) No, you are supposed to solve $x^2=0.1$, disregarding the $x^3$ term. Or to put it into inequalities, as $0<x<1$ you also have
$$
x^2le 0.1le 2x^2implies sqrt{0.05}le x le sqrt{0.1}.
$$
Then iterate further, for instance using $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$.
k x[k]
------------------
1 0.316227766017
2 0.275635071544
3 0.279986295554
4 0.279509993492
5 0.279562012937
6 0.279556330208
7 0.279556950986
8 0.279556883172
9 0.27955689058
edited Nov 26 '18 at 14:05
answered Nov 26 '18 at 13:53
LutzLLutzL
57.3k42054
57.3k42054
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
1
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
1
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
add a comment |
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
1
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
1
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
$begingroup$
So, I have 2 qn. 1) How do you get from $0<x<1$ to $x^2le 0.1le 2x^2$ ? 2) Why use $x_{k+1}=sqrt{frac{0.1}{1+x_k}}$ ? Is $x_{k+1}=(0.1-(x_k)^2)^{1/3}$ okay?
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:01
1
1
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
$begingroup$
1) Put $x^2+x^3$ in place of $0.1$
$endgroup$
– Empy2
Nov 26 '18 at 14:06
1
1
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
$0<x<1implies 0<x^3<x^2implies x^2<0.1<2x^2$. If computing with positive quantities it makes life more predictable if all intermediate terms stay positive. Some iteration formulas will be divergent. Computing using the secant or Dekker's method will be faster than such simple iterations.
$endgroup$
– LutzL
Nov 26 '18 at 14:09
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
$begingroup$
Thanks for the help and the tips!
$endgroup$
– Arc Neoepi
Nov 26 '18 at 14:17
add a comment |
$begingroup$
Just for your curiosity.
We can approximate functions using Padé approximants much better than with Taylor series (remember that Newton method is equivalent to an $O(x^2)$ Taylor expansion).
Since we know how to solve easily quadratic equations, let us consider the $[2,2]$ Padé approximant. It will be
$$x^3+x^2 simfrac{x^2}{x^2-x+1}$$ If you develop the rhs of the above as a Taylor series, you would get $x^2+x^3-x^5+Oleft(x^6right)$ (pretty close, isn't it ?).
So, for small values of $a$, the approximate solution of $x^3+x^2=a$ is given by the solution of $(1-a) x^2+a x-a=0$ that is to say
$$x_pm=frac{apmsqrt{4 a-3 a^2}}{2 (a-1)}$$ So, for $a=frac 1 {10}$, the estimate would be $xapprox 0.282376$ which is quite close to the "exact" solution $xapprox 0.279557$
$endgroup$
add a comment |
$begingroup$
Just for your curiosity.
We can approximate functions using Padé approximants much better than with Taylor series (remember that Newton method is equivalent to an $O(x^2)$ Taylor expansion).
Since we know how to solve easily quadratic equations, let us consider the $[2,2]$ Padé approximant. It will be
$$x^3+x^2 simfrac{x^2}{x^2-x+1}$$ If you develop the rhs of the above as a Taylor series, you would get $x^2+x^3-x^5+Oleft(x^6right)$ (pretty close, isn't it ?).
So, for small values of $a$, the approximate solution of $x^3+x^2=a$ is given by the solution of $(1-a) x^2+a x-a=0$ that is to say
$$x_pm=frac{apmsqrt{4 a-3 a^2}}{2 (a-1)}$$ So, for $a=frac 1 {10}$, the estimate would be $xapprox 0.282376$ which is quite close to the "exact" solution $xapprox 0.279557$
$endgroup$
add a comment |
$begingroup$
Just for your curiosity.
We can approximate functions using Padé approximants much better than with Taylor series (remember that Newton method is equivalent to an $O(x^2)$ Taylor expansion).
Since we know how to solve easily quadratic equations, let us consider the $[2,2]$ Padé approximant. It will be
$$x^3+x^2 simfrac{x^2}{x^2-x+1}$$ If you develop the rhs of the above as a Taylor series, you would get $x^2+x^3-x^5+Oleft(x^6right)$ (pretty close, isn't it ?).
So, for small values of $a$, the approximate solution of $x^3+x^2=a$ is given by the solution of $(1-a) x^2+a x-a=0$ that is to say
$$x_pm=frac{apmsqrt{4 a-3 a^2}}{2 (a-1)}$$ So, for $a=frac 1 {10}$, the estimate would be $xapprox 0.282376$ which is quite close to the "exact" solution $xapprox 0.279557$
$endgroup$
Just for your curiosity.
We can approximate functions using Padé approximants much better than with Taylor series (remember that Newton method is equivalent to an $O(x^2)$ Taylor expansion).
Since we know how to solve easily quadratic equations, let us consider the $[2,2]$ Padé approximant. It will be
$$x^3+x^2 simfrac{x^2}{x^2-x+1}$$ If you develop the rhs of the above as a Taylor series, you would get $x^2+x^3-x^5+Oleft(x^6right)$ (pretty close, isn't it ?).
So, for small values of $a$, the approximate solution of $x^3+x^2=a$ is given by the solution of $(1-a) x^2+a x-a=0$ that is to say
$$x_pm=frac{apmsqrt{4 a-3 a^2}}{2 (a-1)}$$ So, for $a=frac 1 {10}$, the estimate would be $xapprox 0.282376$ which is quite close to the "exact" solution $xapprox 0.279557$
answered Nov 27 '18 at 10:07
Claude LeiboviciClaude Leibovici
120k1157132
120k1157132
add a comment |
add a comment |
$begingroup$
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newcommand{verts}[1]{leftvert,{#1},rightvert}$
- Start with the iteration
$$
x_{n} = root{0.1 - x_{n}^{3}},,qquad
x_{0} = root{0.1} approx 0.3162
$$
It seems to be that $ds{10}$ iterations are enough. Namely, $$
x_{10} approx 0.279564quadmbox{and}quadmrm{f}pars{x_{10}} approx 5.43129 times 10^{-6}
$$
where $bbox[10px,#ffd,border: 1px groove navy]{ds{mrm{f}pars{x} equiv x^{3} + x^{2} - 0.1}}$. - In addition, you can refine the above result by means of a Newton-Rapson Iteration:
$$
y_{n} = y_{n - 1} - {y_{n - 1}^{3} + y_{n - 1}^{2} - 0.1 over
3y_{n - 1}^{2} + 2y_{n - 1}},,qquad y_{0} = x_{10} approx 0.279564
$$
With about three iterations, I'll find
$$
y_{3} approx bbx{large 0.279957} implies mrm{f}pars{y_{3}} approx
2.77556 times10^{-17}
$$
begin{align}
end{align}
$endgroup$
add a comment |
$begingroup$
$newcommand{bbx}[1]{,bbox[15px,border:1px groove navy]{displaystyle{#1}},}
newcommand{braces}[1]{leftlbrace,{#1},rightrbrace}
newcommand{bracks}[1]{leftlbrack,{#1},rightrbrack}
newcommand{dd}{mathrm{d}}
newcommand{ds}[1]{displaystyle{#1}}
newcommand{expo}[1]{,mathrm{e}^{#1},}
newcommand{ic}{mathrm{i}}
newcommand{mc}[1]{mathcal{#1}}
newcommand{mrm}[1]{mathrm{#1}}
newcommand{pars}[1]{left(,{#1},right)}
newcommand{partiald}[3]{frac{partial^{#1} #2}{partial #3^{#1}}}
newcommand{root}[2]{,sqrt[#1]{,{#2},},}
newcommand{totald}[3]{frac{mathrm{d}^{#1} #2}{mathrm{d} #3^{#1}}}
newcommand{verts}[1]{leftvert,{#1},rightvert}$
- Start with the iteration
$$
x_{n} = root{0.1 - x_{n}^{3}},,qquad
x_{0} = root{0.1} approx 0.3162
$$
It seems to be that $ds{10}$ iterations are enough. Namely, $$
x_{10} approx 0.279564quadmbox{and}quadmrm{f}pars{x_{10}} approx 5.43129 times 10^{-6}
$$
where $bbox[10px,#ffd,border: 1px groove navy]{ds{mrm{f}pars{x} equiv x^{3} + x^{2} - 0.1}}$. - In addition, you can refine the above result by means of a Newton-Rapson Iteration:
$$
y_{n} = y_{n - 1} - {y_{n - 1}^{3} + y_{n - 1}^{2} - 0.1 over
3y_{n - 1}^{2} + 2y_{n - 1}},,qquad y_{0} = x_{10} approx 0.279564
$$
With about three iterations, I'll find
$$
y_{3} approx bbx{large 0.279957} implies mrm{f}pars{y_{3}} approx
2.77556 times10^{-17}
$$
begin{align}
end{align}
$endgroup$
add a comment |
$begingroup$
$newcommand{bbx}[1]{,bbox[15px,border:1px groove navy]{displaystyle{#1}},}
newcommand{braces}[1]{leftlbrace,{#1},rightrbrace}
newcommand{bracks}[1]{leftlbrack,{#1},rightrbrack}
newcommand{dd}{mathrm{d}}
newcommand{ds}[1]{displaystyle{#1}}
newcommand{expo}[1]{,mathrm{e}^{#1},}
newcommand{ic}{mathrm{i}}
newcommand{mc}[1]{mathcal{#1}}
newcommand{mrm}[1]{mathrm{#1}}
newcommand{pars}[1]{left(,{#1},right)}
newcommand{partiald}[3]{frac{partial^{#1} #2}{partial #3^{#1}}}
newcommand{root}[2]{,sqrt[#1]{,{#2},},}
newcommand{totald}[3]{frac{mathrm{d}^{#1} #2}{mathrm{d} #3^{#1}}}
newcommand{verts}[1]{leftvert,{#1},rightvert}$
- Start with the iteration
$$
x_{n} = root{0.1 - x_{n}^{3}},,qquad
x_{0} = root{0.1} approx 0.3162
$$
It seems to be that $ds{10}$ iterations are enough. Namely, $$
x_{10} approx 0.279564quadmbox{and}quadmrm{f}pars{x_{10}} approx 5.43129 times 10^{-6}
$$
where $bbox[10px,#ffd,border: 1px groove navy]{ds{mrm{f}pars{x} equiv x^{3} + x^{2} - 0.1}}$. - In addition, you can refine the above result by means of a Newton-Rapson Iteration:
$$
y_{n} = y_{n - 1} - {y_{n - 1}^{3} + y_{n - 1}^{2} - 0.1 over
3y_{n - 1}^{2} + 2y_{n - 1}},,qquad y_{0} = x_{10} approx 0.279564
$$
With about three iterations, I'll find
$$
y_{3} approx bbx{large 0.279957} implies mrm{f}pars{y_{3}} approx
2.77556 times10^{-17}
$$
begin{align}
end{align}
$endgroup$
$newcommand{bbx}[1]{,bbox[15px,border:1px groove navy]{displaystyle{#1}},}
newcommand{braces}[1]{leftlbrace,{#1},rightrbrace}
newcommand{bracks}[1]{leftlbrack,{#1},rightrbrack}
newcommand{dd}{mathrm{d}}
newcommand{ds}[1]{displaystyle{#1}}
newcommand{expo}[1]{,mathrm{e}^{#1},}
newcommand{ic}{mathrm{i}}
newcommand{mc}[1]{mathcal{#1}}
newcommand{mrm}[1]{mathrm{#1}}
newcommand{pars}[1]{left(,{#1},right)}
newcommand{partiald}[3]{frac{partial^{#1} #2}{partial #3^{#1}}}
newcommand{root}[2]{,sqrt[#1]{,{#2},},}
newcommand{totald}[3]{frac{mathrm{d}^{#1} #2}{mathrm{d} #3^{#1}}}
newcommand{verts}[1]{leftvert,{#1},rightvert}$
- Start with the iteration
$$
x_{n} = root{0.1 - x_{n}^{3}},,qquad
x_{0} = root{0.1} approx 0.3162
$$
It seems to be that $ds{10}$ iterations are enough. Namely, $$
x_{10} approx 0.279564quadmbox{and}quadmrm{f}pars{x_{10}} approx 5.43129 times 10^{-6}
$$
where $bbox[10px,#ffd,border: 1px groove navy]{ds{mrm{f}pars{x} equiv x^{3} + x^{2} - 0.1}}$. - In addition, you can refine the above result by means of a Newton-Rapson Iteration:
$$
y_{n} = y_{n - 1} - {y_{n - 1}^{3} + y_{n - 1}^{2} - 0.1 over
3y_{n - 1}^{2} + 2y_{n - 1}},,qquad y_{0} = x_{10} approx 0.279564
$$
With about three iterations, I'll find
$$
y_{3} approx bbx{large 0.279957} implies mrm{f}pars{y_{3}} approx
2.77556 times10^{-17}
$$
begin{align}
end{align}
edited Nov 29 '18 at 1:16
answered Nov 27 '18 at 21:12
Felix MarinFelix Marin
67.5k7107141
67.5k7107141
add a comment |
add a comment |
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