DOC PREVIEW
MIT MATH 52 - Numerical Differentiation

This preview shows page 1-2-14-15-29-30 out of 30 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 30 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Numerical DifferentiationNumerical Differentiation and Integration/ad teg at o /Numerical Differentiation ByDr Ali JawarnehDr Ali Jawarneh1Dr Ali JawarnehCh23:Numerical Differentiation and Ch.23:Numerical Differentiation and Integration/ Numerical Differentiation23.1- High-Accuracy Differentiation Formulas232Ri h d E t l ti23.2-Richardson Extrapolation23.3- Derivatives of Unequally Spaced Data Spaced Data 2Dr Ali JawarnehNUMERICAL DIFFERENTIATIONHigh-Accuracy Differentiation FormulasIt provides a mean to predict a function value at one point in terms of the Taylor series function value and it’s derivative at another point. Taylor series expansions are used to derive finite-divided-difference approximations of derivatives.)(fTrue derivative)x(fforwardh=xi+1-xi=xi-xi-1backwardcenteredxx1ixxhhDr Ali Jawarneh 31ix+ix1ix−(old) (present) (new))x(f!n1awhere)xx(a)x(f0)n(nn00nm=−=∑∞=Taylor Series0n=...)x('"f!33)xx()x("f!22)xx()x('f!1)xx()x(f)x(fo0o0o00+−+−+−+=Series2)x("fForward Taylorn: is order of the seriesni1ii)n(3i1ii2i1iii1iii1i)xx()x(f...)xx()x('"f)xx(!2)x(f)xx)(x('f)x(f)x(f−++−+−+−+=+++i1ixxh−=+i1ii1i)xx(!n...)xx(!3++++)x("fBackward Taylorni)n(3i21iii1iiii1i)xx()x(f)xx()x('"f)xx(!2)x(f)xx)(x('f)x(f)x(f−−−−+−+−−−+−−=1iixxh−−=Dr Ali Jawarneh 41ii1ii)xx(!n......)xx(!3−−−+−+−)n()x(f)x('"f)x("fForward Taylor(1)ni3i2iii1ih!n)x(f...h!3)x(fh!2)x(fh)x('f)x(f)x(f+++++=+Zero order (n=0):)h(O)x(f)x(fi1i+=+O(h): Truncation(1)())()()(i1i+1storder (n=1):)h(Oh)x('f)x(f)x(f2ii1i++=+2ndorder (n=2):)x("f32iO(h): Truncation error of order h2ndorder (n=2):)h(Oh!2)x(fh)x('f)x(f)x(f32iii1i+++=+Backward TaylorBackward Taylorni)n(3i2iii1ih!n)x(f......h!3)x('"fh!2)x("fh)x('f)x(f)x(f +−+−+−=−(2)Dr Ali JawarnehZero order:)h(O)x(f)x(fi1i+=−)h(Oh)x('f)x(f)x(f2ii1i+−=−1storder:5)h(Oh!2)x("fh)x('f)x(f)x(f32iii1i++−=−2ndorder:Forward Approximation)x("f(3)...h!2)x("fh)x('f)x(f)x(f2iii1i+++=+from (1)h)x("f)x(f)x(f)('fii1i−+(3)...h!2)(h)()()x('fii1ii++=+)h(O)x(f)x(f)x('fi1i+−+Forward1stderivative of order h)h(Oh)x('fi+=Similar to (1)...)h2()x("f)h2)(x('f)x(f)x(f2iii2i+++=Forward , 1derivative of order h(4)Similar to (1)...)h2(!2)h2)(x(f)x(f)x(fii2i++++Multiplication Eq.(3) with 2, then subtract the result from (4), you will get:)x(f)x(f2)x(f+Fd2dd i ti())h(Oh)x(f)x(f2)x(f)x("f2i1i2ii++−=++Forward , 2nd derivative of order hDr Ali Jawarneh 6from (1)...h!3)x('"fh!2)x("fh)x('f)x(f)x(f3i2iii1i++++=+!3!2h)x("f)x(f)x(f22ii1i−Use also Forward , 2nd derivative of order h)h(Oh)]x(f)x(f2)x(f[)x(f)x(f)h(Ohh!2)x(fh)x(f)x(f)x('f22i1i2ii1i2ii1ii++−−=+−=++++)h(Oh!2hh2+−=Collecting terms:)h(Oh2)x(f3)x(f4)x(f)x('f2i1i2ii+−+−=++Forward , 1stderivative of order h2Dr Ali Jawarneh 7Backward Approximation)('"f)("fFrom (2)...h!3)x('"fh!2)x("fh)x('f)x(f)x(f3i2iii1i+−+−=−)x(f)x(f1ii−t(5))h(Oh)x(f)x(f)x('f1iii+=−Backward , 1stderivative of order hSimilar to (2)...)h2()x("f)h2)(x('f)x(f)x(f2iii2i−+−=(6)...)h2(!2)h2)(x(f)x(f)x(fii2i+−Multiplication Eq.(5) with 2, then subtract the result from (6), you will get:(6))x(f)x(f2)x(f+bk d2dd i ti f)h(Oh)x(f)x(f2)x(f)x("f22i1iii=+−=−−backward , 2nd derivative of order hDr Ali Jawarneh 8from (2)+−+−=−3i2iii1ih!3)x('"fh!2)x("fh)x('f)x(f)x(fUse also Backward , 2nd derivative of order h1h])x(f)x(f2)x(f[)x(f)x(f)x('f22i1ii1ii−−−+−+−=h!2]h[h)x(f2i+=Collecting terms:)h(Oh)x(f)x(f4)x(f32)x('f22i1iii++−=−−Dr Ali Jawarneh 9Centered ApproximationSubtract (1) from (2))h(Oh2)x(f)x(f)x('f21i1ii+−=−+Dr Ali Jawarneh 10Summary of Finite-Divided-Difference Formulas(1) Forward FiniteDividedDifference Formulas(1) Forward Finite-Divided-Difference Formulas1stderivative:)h(Oh)x(f)x(f)x('fi1ii+−=+h)h(Oh2)x(f3)x(f4)x(f)x('f2i1i2ii+−+−=++More accurate2ndderivative:h2)x(f)x(f2)x(f+)h(Oh)x(f)x(f2)x(f)x("f2i1i2ii++−=++)(f2)(f5)(f4)(f)h(Oh)x(f2)x(f5)x(f4)x(f)x("f22i1i2i3ii++−+−=+++Dr Ali Jawarneh 11More accurate(2) Backward FiniteDividedDifference Formulas(2) Backward Finite-Divided-Difference Formulas1stderivative:)h(Oh)x(f)x(f)x('f1iii+−=−h)h(Oh2)x(f)x(f4)x(f3)x('f22i1iii++−=−−More accurate2ndderivative:h2)x(f)x(f2)x(f+)h(Oh)x(f)x(f2)x(f)x("f22i1iii++−=−−)(f)(f4)(f5)(f2)h(Oh)x(f)x(f4)x(f5)x(f2)x("f223i2i1iii+−+−=−−−Dr Ali Jawarneh 12More accurate(3) Centered FiniteDividedDifference Formulas(3) Centered Finite-Divided-Difference Formulas1stderivative:)h(Oh2)x(f)x(f)x('f21i1ii+−=−+h2)h(Oh12)x(f)x(f8)x(f8)x(f)x('f42i1i1i2ii++−+−=−−++More accurate2ndderivative:h12)x(f)x(f2)x(f+)h(Oh)x(f)x(f2)x(f)x("f221ii1ii++−=−+)(f)(f16)(f30)(f16)(f)h(Oh12)x(f)x(f16)x(f30)x(f16)x(f)x("f422i1ii1i2ii+−+−+−=−−++Dr Ali Jawarneh 13More accurateTwo ways to improve derivative estimates when employing finite divided differences(1)Decrease the step size(1)Decrease the step size(2) Use a higher-order formula that employs more pointsDr Ali Jawarneh 14Example: Find the first derivative of y= cos(x) at x=π/4 using a step size of h=π/12Use forward and backwardusing a step size of h= π/12.Use forward and backward approximations of O(h) & O(h2) , and central difference approximation of O(h2) and O(h4). Also, estimate the true tltifh itipercent relative error εtfor each approximation.SolutionForward )h(O)x(f)x(f)('fi1i−+)x(fForward )h(Oh)()()x('fi1ii+=+)h(Oh2)x(f3)x(f4)x(f)x('f2i1i2ii+−+−=++xhh2Backward)h(Oh)x(f)x(f)x('f1iii+−=−x2ix− 1ix− ix1ix+ 2ix+)h(Oh2)x(f)x(f4)x(f3)x('f22i1iii++−=−−Centered)h(O)x(f)x(f)x('f21i1ii+−=−+12π6π4π3π125πDr Ali Jawarneh15)h(Oh2)x(fi+)h(Oh12)x(f)x(f8)x(f8)x(f)x('f42i1i1i2ii++−+−=−−++xf(x) (radian)Xπ/120965925826(radian70710678.0)4sin(|)4('ftrue−=π−=πXi‐2π/120.965925826Xi‐1π/60.866025404Xiπ/40.707106781(radianForward )h(Oh)x(f)x(f)x('fi1ii+−=+)4/()3/(Xiπ/40.707106781Xi+1π/30.5Xi+25π/120.25881904579108963.012/)4/cos()3/cos()4('f −=ππ−π=π)h(O)x(f3)x(f4)x(f)x('f2i1i2ii+−+−=++i+2/)(h2)(i72601275.012/2)4/cos(3)3/cos(4)12/5cos()4/('f −=ππ−π+π−=πBackward)h(Oh)x(f)x(f)x('f1iii+−=−60702442.012/)6/cos()4/cos()4/('f −=ππ−π=π)x(f)x(f4)x(f32+−)h(Oh2)x(f)x(f4)x(f3)x('f22i1iii++=−−71974088.012/2)12/cos()6/cos(4)4/cos(3)x('fi−=ππ+π−π=Dr Ali Jawarneh


View Full Document

MIT MATH 52 - Numerical Differentiation

Download Numerical Differentiation
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Numerical Differentiation and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Numerical Differentiation 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?