Polynomial Approximation - II
Legandre vs Chebyshev Polynomials
Worksheet by Mike May, S.J., maymk@slu.edu
restart;
Chebyshev polynomials of the first kind
We will begin with Chebyshev polynomials of the first kind.
?orthopoly,T
The help page shows us that weight function of Chebyshev polynomials of the first kind is (1-x^2)^(-1/2), The effect of the weight function is to make the endpoints count more when defining what it means for function to be small.
plot({(1-x^2)^(-1/2),1}, x=-1..1, y=0..3);
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
Once we have loaded the orthopoly package, we obtain the nth Chebychev polynomial with T(n,x);
T(3,x);
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
Exercise:
1) Find the 1st, second, and 5th Chebychev polynomials of the first kind. Verify that they are orthogonal under the oppropriate inner product.
An extended example, back to f(x) = sin(3*Pi*x)
Time to look at an example to see how this works out in a particular case. Once again we will use sin(3\317\200x) as our function.
func := x -> sin(3*Pi*x);
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
Computing coefficients and approximating polynomials
Next we start computing coefficients. We will find the coefficients for both the Legangre polynomials and for Chebyshev polynomials of the first kind. Recall that if P(n,x) is the nth Legandre polynomial, the projection coeffient of f(x) onto P(n,x) should be 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 divided by 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 . Similarly, if T(n,x) is the nth Chebyshev polynomial of the first kind, the projection coefficient of f(x) onto T(n,x) is 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 divided by 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, where wtCheb(x) = (1-x^2)^(-1/2).
wtchev := (1-x^2)^(-1/2):
for i from 0 to 15 do
normP[i] := evalf(Int((P(i,x))^2, x=-1..1));
coeffP[i] := evalf(Int(simplify(P(i,x)*func(x)*1.0), x=-1..1));
normT[i] := evalf(Int(wtchev*(T(i,x))^2, x=-1..1));
coeffT[i] := evalf(Int(wtchev*T(i,x)*func(x), x=-1..1)):
od;
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Now we define the approximating polynomials.
Lapprox := proc(m,x)
local count:
sum(P(count,x)*coeffP[count]/normP[count],count=0..m):
end;
Chebapprox := proc(m,x)
local count:
sum(T(count,x)*coeffT[count]/normT[count],count=0..m):
end;
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Comparing the approximations
Since our function has 6 bumps and looks like it could be a 7th degreee polynomial, we want to compare the 7th degree approximations. Note first that the polynomial approximations are significantly different.
n:= 7:
Lapprox(n,x);
Chebapprox(n,x);
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
Next compare the graphs of the approximations and of the errors.
lowy := -1.2: highy := 1.6: n:= 7:
pl1 := plot(func(x), x=-1..1,y=lowy..highy, color=red):
pl2 := plot(Lapprox(n,x), x=-1..1,y=lowy..highy, color=green):
pl3 := plot(Chebapprox(n,x), x=-1..1,y=lowy..highy, color=blue):
B := textplot({[-0.8,highy-(highy-lowy)/20,
`The function and the `||n||` term approximations`],
[-0.8,highy-(highy-lowy)/9, `Chebyshev (blue) vs Legandre (green)`]},
align=RIGHT, font = [TIMES, BOLD, 12] ):
display({pl1, pl2, pl3, B},view=[-1..1, lowy..highy]);
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As expected, the Legandre approximation tends to be better in the center of the graph, while the Chebyshev approximation tends to be better on the ends. This becomes even clearer when we look at the graphs of the errors.
lowy := -1.0: highy := 1.4: n:= 7:
pl1 := plot(func(x) - Chebapprox(n,x), x=-1..1,y=lowy..highy, color=green):
pl2 := plot(func(x) - Lapprox(n,x), x=-1..1,y=lowy..highy, color=blue):
B := textplot({[-0.8,highy-(highy-lowy)/20,
`The `||n||` term Legandre error (blue)`],
[-0.8,highy-(highy-lowy)/9, `and Chebychev error (green)`]},
align=RIGHT, font = [HELVETICA, BOLD, 12] ):
display({pl1, pl2, B});
6(-I'CURVESG6$%*protectedGI(_syslibG6"6$7gt7$$!""""!$!35]++!fxki$!#=7$$!3.MLe9r]X**F1$!3S>ddvOeTBF17$$!3%pmm"HU,"*)*F1$!3e*[;P;+>?"F17$$!3')***\PM@l$)*F1$!31$Hq!H(pE*>!#>7$$!3!RLL$e%G?y*F1$"3hqyzm_iUnFA7$$!3t****\(oUIn*F1$"3bEl5)*Htk?F17$$!3ommm;p0k&*F1$"3X!=xxuN)GIF17$$!3#HL3-)*G#p%*F1$"31+]3C3WkNF17$$!3E++vV5Su$*F1$"3Ajc[F,WaQF17$$!35<ajfSp]$*F1$"35_&3"\nX#*QF17$$!3%R$3_vq)pK*F1$"3/`%Q)p2o<RF17$$!3n\iS"4!G.$*F1$"3=N$\,\*fIRF17$$!3^m;H2Jdz#*F1$"31'QPqx#pJRF17$$!3M$3xJ7meD*F1$"36H:$e2L9#RF17$$!33*\i!R"f@B*F1$"3)\J\CU&G+RF17$$!3"f"z%\:_%3#*F1$"3"zz?hX2(oQF17$$!3vKL$3<XZ=*F1$"3#o_-EV\r#QF17$$!39***\iId9(*)F1$"3)=`A+WX]2$F17$$!3mmmmT%p"e()F1$"3wO`aKb*H%=F17$$!3qlm;/'=3l)F1$"3-uh$>*=p@6F17$$!3%emmmwnMa)F1$"3OVUSFu'))p$FA7$$!3+mm;Hp6O%)F1$!3c3#))*))[()zQFA7$$!3:mmm"4m(G$)F1$!3B@%e?$[JI6F17$$!3)****\i&[3:")F1$!3J;OU&zV&*[#F17$$!3"QLL3i.9!zF1$!3)\4"oHl3(f$F17$$!3'3++Dw$H.xF1$!3mb+ifugHVF17$$!3"ommT!R=0vF1$!3'3,e"\R(=u%F17$$!37MLL$3,RX(F1$!3K$HE3tXWz%F17$$!3K++]i#=ES(F1$!3%4#*=(RfnC[F17$$!3`mmmTaL^tF1$!39vZ)>y%oK[F17$$!3tKL$3i_+I(F1$!3CO*ffgy'=[F17$$!3Cmm;zp[(>(F1$!3ObR?*Rbes%F17$$!3u****\P8#\4(F1$!3aX=12.W\XF17$$!3#RLL3FuF)oF1$!3!QJpmHp#RRF17$$!3*pmmT?F1n'F1$!3Sn5:lf*H/$F17$$!3tLL$e\g"fkF1$!35hvtM"3+$>F17$$!3[++](y$pZiF1$!38y**>9=e=nFA7$$!3MnmT&Gu,.'F1$"3e\7HT#G,"pFA7$$!33LLL$yaE"eF1$"3E-n)p"olQ?F17$$!3%)****\([j5i&F1$"3kGbrr))3[JF17$$!3hmmm">s%HaF1$"3I&H@(4h">8%F17$$!3]LL$3x&y8_F1$"3%R"=e2x-N]F17$$!3Q+++]$*4)*\F1$"3j%4Zu_@Tn&F17$$!3K+++DL")*)[F1$"3IcHo"pWR)eF17$$!3E++++t_"y%F1$"3eo"e?1Ja,'F17$$!3]++](G%QFZF1$"3:eu=&p.70'F17$$!3>+++v7CtYF1$"3ALte;D"o1'F17$$!3))****\i#)4>YF1$"3$*=#)o5L?igF17$$!38+++]_&\c%F1$"3End2+#)QPgF17$$!33+++]zCcVF1$"3."Q0YXN\v&F17$$!30+++]1aZTF1$"3h\9^s*Hy=&F17$$!3RLL3FW,eRF1$"31KQxf6O[WF17$$!3umm;/#)[oPF1$"3jr$HI-CW_$F17$$!3=L$e*)p0el$F1$"3cQfWN3)=!HF17$$!3<++v$>BJa$F1$"3!*\9dpz([B#F17$$!3<n;a)oS/V$F1$"3#f/n;ccB`"F17$$!3hLLL$=exJ$F1$"3!=*QD!o+q.)FA7$$!3!QLL$eW%o7$F1$!3)\D![.BFJYFA7$$!3)RLLLtIf$HF1$!3EKm>`K$*G<F17$$!3M+]P4<*[#GF1$!3KK;d1CnXCF17$$!3CnmT&o_Qr#F1$!3wA_m$exm8$F17$$!3:M$e9m8Gg#F1$!3S))*fX0MJz$F17$$!3]++]PYx"\#F1$!3&fgdk()zmS%F17$$!3QnmTNz>&H#F1$!3YZWAY*eRO&F17$$!3EMLLL7i)4#F1$!3=i(HYCmX7'F17$$!3#pm;aVXH)=F1$!3)yk&Q(Qnqp'F17$$!3c****\P'psm"F1$!3a3Szz(o)opF17$$!3u**\(o/Efh"F1$!3am-lWAY()pF17$$!3k***\iX#ek:F1$!3*e\%\Mr6))pF17$$!3a**\il)QK^"F1$!3SSRKQ">3(pF17$$!3r*****\F&*=Y"F1$!3=z8#GM,c$pF17$$!3z***\P43#f8F1$!3L8qT\Zx6oF17$$!3')****\74_c7F1$!3i/YD'yTyh'F17$$!3ZmmT5VBU5F1$!3!eeH#f0s'*fF17$$!3)3LLL3x%z#)FA$!3cl6Q=332^F17$$!3gKL$e9d;J'FA$!3kB?C8rQ(3%F17$$!3KMLL3s$QM%FA$!310fh"[hI"HF17$$!3'ym;aQdDG$FA$!3%==2=tY4B#F17$$!3T,+]ivF@AFA$!3!=%HLou@C:F17$$!3'\L$eRx**f6FA$!3ugWO4'*p1!)FA7$$!3\^omm;zr)*!#@$!3o7%R-CY(Go!#?7$$"3&yK$3_Nl.5FA$"3%>&HM_FRJpFA7$$"3@CL$3-Dg5#FA$"3Y#)[z$R9jW"F17$$"3e?Le*['R3KFA$"3<p&G"yAF#=#F17$$"3%pJL$ezw5VFA$"33=ZpAQD#*GF17$$"3K`mmmJ+IiFA$"3L'[[3CX9/%F17$$"3s*)***\PQ#\")FA$"3Y*e*3HW<X]F17$$"3ilm"z\1A-"F1$"3b#e*o<0UCfF17$$"3GKLLe"*[H7F1$"3ez=:M.OblF17$$"3.***\PzglL"F1$"3*e>4:!G(\x'F17$$"3ylm;HCjV9F1$"3O[q`^vw=pF17$$"3;**\(oCor\"F1$"3>?fCL(><'pF17$$"3aKLekSq]:F1$"3M(*4K)R6_)pF17$$"3#fm"H#))RUg"F1$"3$>PcCR*=*)pF17$$"3H*******pvxl"F1$"3Jw%pfbcO(pF17$$"3g)***\7JFn=F1$"3#p(3-Sq>FnF17$$"3#z****\_qn2#F1$"3eui'zHnb>'F17$$"3=)**\P/q%zAF1$"3g0E4Kp]KaF17$$"3U)***\i&p@[#F1$"3$\kmEZwuX%F17$$"3Q)*\i!>,Zf#F1$"3%yG\)=0^RQF17$$"3L)**\(=GB2FF1$"3pVvF$\no<$F17$$"3F)*\(oWk(>GF1$"3V-(*\]c@yCF17$$"3A)****\2'HKHF1$"3\!\UJW2Fv"F17$$"3uJL$3UDX8$F1$"3j4([Uia&=TFA7$$"3ElmmmZvOLF1$!3)4ay.*Hgz#*FA7$$"3VKLLexn_NF1$!3YG&Q-MTHH#F17$$"3h******\2goPF1$!3SvL,S2-DNF17$$"3,mm"H2fU'RF1$!3pv:CE0%eZ%F17$$"3UKL$eR<*fTF1$!3CH`c%[z*G_F17$$"3.mm"HiBQP%F1$!36DXNGb(**y&F17$$"3l******\)Hxe%F1$!3XT34t\G]gF17$$"30LeRAr/QYF1$!3OOw/-o6mgF17$$"3*em"z%Rk$)o%F1$!3&>?3<`'[kgF17$$"3s)\(=n;oQZF1$!3j<olT6SXgF17$$"36KLeR*)**)y%F1$!3qtsE"=<*3gF17$$"3M)*\P%[L'*)[F1$!3j"yRFkGU)eF17$$"3ckm;H!o-*\F1$!3]+4?&**o=p&F17$$"3nJL$3A_1?&F1$!34n$z9o=>3&F17$$"3y)***\7k.6aF1$!39!=%>8]G=UF17$$"3IKLe9as;cF1$!32o\Q%\L><$F17$$"3#emmmT9C#eF1$!3#QXjg>z'z>F17$$"3WKLeR<vPgF1$!3UO%Qy;BIV'FA7$$"32****\i!*3`iF1$"3ES0F7.C^qFA7$$"3Xmm"z\%[gkF1$"3qD:"op3v$>F17$$"3%QLLL$*zym'F1$"3_^pLv#*yHIF17$$"3ILL$3sr*zoF1$"3LB0k@G;HRF17$$"3wKLL3N1#4(F1$"3P/cX&enLa%F17$$"3gl;a)GJs>(F1$"3)pZ)=]z^DZF17$$"3c***\(o!*R-tF1$"3)eP+[D'z>[F17$$"3)f;a)eH)\N(F1$"3BZ\3!HXG$[F17$$"3]L$e*[oc2uF1$"3!p%[@0,tA[F17$$"3#**\i!R2:guF1$"336)3I!fB*y%F17$$"3Mmm;HYt7vF1$"3kncY$\VAt%F17$$"3"HL$ek6,1xF1$"3?@$)RsEn@VF17$$"3Y*******p(G**yF1$"3#zhogz*\1OF17$$"3ymmTgg/5!)F1$"3TT")=HE@rIF17$$"3)HLL3U/37)F1$"3<Ud4RC+cCF17$$"3=***\7yi:B)F1$"3XkQ-A1Ct<F17$$"3]mmmT6KU$)F1$"3g3C@/h6Q5F17$$"3a****\P$[/a)F1$!3*RTpzqaa[$FA7$$"3fKLLLbdQ()F1$!38\J-wE"[r"F17$$"3`mm"zW?)\*)F1$!3&f%pY]0SnHF17$$"3Z++]i`1h"*F1$!3$ok]D5=hx$F17$$"3Z+](oW7;@*F1$!3y,'4xF.N(QF17$$"3Y++DJ&f@E*F1$!35Xz"y@P_#RF17$$"3Y+]i:mq7$*F1$!3'o!=G%[0p#RF17$$"3X++++PDj$*F1$!3`TCo02%R(QF17$$"3X++voyMk%*F1$!3==?BRY2&e$F17$$"3W++]P?Wl&*F1$!3)z+1rp@!>IF17$$"3K+]7G:3u'*F1$!3(z8a)=Me`?F17$$"3A++v=5s#y*F1$!3.*)f:ZGQRmFA7$$"3;+D1k2/P)*F1$"3x,Z2M2-#3#FA7$$"35+]P4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It is clear that the Chebyshev approximation is better at the endpoints, for degree 7.
Exercises:
2) For the example above, approximately what is the maximum error for over the domain [-1, 1] for the 11th degree Legandre and Chebyshev approximations?
3) For the example above, approximately what is the maximum error for over the domain [-.5, .5] for the 11th degree Legandre and Chebyshev approximations?
Viewing the pattern of approximations
To illustrate the pattern we can do an animation of the errors produced by the polynomials.
framererrCheb := proc(n,x, lowy, highy)
local pl1, pl2, B:
pl1 := plot(func(x) - Chebapprox(n,x),
x = -1..1, y = lowy..highy, color = green):
pl2 := plot(func(x) - Lapprox(n,x),
x = -1..1, y = lowy..highy, color = blue):
B := textplot({[-0.8,highy-(highy-lowy)/20,
`The `||n||` term Legandre error (blue)`],
[-0.8,highy-(highy-lowy)/9, `and Chebyshev error (green)`]},
align=RIGHT, font = [HELVETICA, BOLD, 12] ):
display({pl1, pl2, B});
end:
display([seq(framererrCheb(counta,x, -1.2, 1.6), counta=1..15)], insequence = true);
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Exercise:
4) Explain why, with our example, the error only changes with odd degree approximations.