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Numerical Methods I Orthogonal Polynomials

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Numerical Methods I Orthogonal Polynomials Numerical Methods I Orthogonal Polynomials Aleksandar Donev Courant Institute, NYU1 [email protected] 1Course G63.2010.001 / G22.2420-001, Fall 2010 Nov. 4th and 11th, 2010 A. Donev (Courant Institute) Lecture VIII 11/04/2010 1 / 40 Outline 1 Function spaces 2 Orthogonal Polynomials on [−1; 1] 3 Spectral Approximation 4 Fourier Orthogonal Trigonometric Polynomials 5 Conclusions A. Donev (Courant Institute) Lecture VIII 11/04/2010 2 / 40 Final Project Presentations The final presentations will take place on the following three dates (tentative list!): 1 Thursday Dec. 16th 5-7pm (there will be no class on Legislative Day, Tuesday Dec. 14th) 2 Tuesday Dec. 21st 5-7pm 3 Thursday Dec. 23rd 4-7pm (note the earlier start time!) There will be no homework this week: Start thinking about the project until next week's homework. A. Donev (Courant Institute) Lecture VIII 11/04/2010 3 / 40 Lagrange basis on 10 nodes A few Lagrange basis functions for 10 nodes 2 1 0 −1 −2 −3 −4 −5 φ 5 φ −6 1 φ 3 −7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 A. Donev (Courant Institute) Lecture VIII 11/04/2010 4 / 40 Runge's phenomenon f (x) = (1 + x2)−1 Runges phenomenon for 10 nodes 1 0 −1 y −2 −3 −4 −5 −5 −4 −3 −2 −1 0 1 2 3 4 5 x A. Donev (Courant Institute) Lecture VIII 11/04/2010 5 / 40 Function spaces Function Spaces Function spaces are the equivalent of finite vector spaces for functions (space of polynomial functions P, space of smoothly twice-differentiable functions C2, etc.). Consider a one-dimensional interval I = [a; b]. Standard norms for functions similar to the usual vector norms: Maximum norm: kf (x)k1 = maxx2I jf (x)j R b L1 norm: kf (x)k1 = a jf (x)j dx 1=2 hR b 2 i Euclidian L2 norm: kf (x)k2 = a jf (x)j dx 1=2 hR b 2 i Weighted norm: kf (x)kw = a jf (x)j w(x)dx An inner or scalar product (equivalent of dot product for vectors): Z b (f ; g) = f (x)g ?(x)dx a A. Donev (Courant Institute) Lecture VIII 11/04/2010 6 / 40 Function spaces Finite-Dimensional Function Spaces Formally, function spaces are infinite-dimensional linear spaces. Numerically we always truncate and use a finite basis. Consider a set of m + 1 nodes xi 2 X ⊂ I , i = 0;:::; m, and define: " m #1=2 X X 2 kf (x)k2 = jf (xi )j ; i=0 which is equivalent to thinking of the function as being the vector fX = y = ff (x0); f (x1); ··· ; f (xm)g. Finite representations lead to semi-norms, but this is not that important. A discrete dot product can be just the vector product: m X X ? (f ; g) = fX · gX = f (xi )g (xi ) i=0 A. Donev (Courant Institute) Lecture VIII 11/04/2010 7 / 40 Function spaces Function Space Basis Think of a function as a vector of coefficients in terms of a set of n basis functions: fφ0(x); φ1(x); : : : ; φn(x)g ; k for example, the monomial basis φk (x) = x for polynomials. A finite-dimensional approximation to a given function f (x): n X f~(x) = ci φi (x) i=1 Least-squares approximation for m > n (usually m n): c? = arg min f (x) − f~(x) ; c 2 which gives the orthogonal projection of f (x) onto the finite-dimensional basis. A. Donev (Courant Institute) Lecture VIII 11/04/2010 8 / 40 Function spaces Least-Squares Approximation Discrete case: Think of fitting a straight line or quadratic through experimental data points. The function becomes the vector y = fX , and the approximation is n X yi = cj φj (xi ) ) y = Φc; j=1 Φij = φj (xi ): This means that finding the approximation consists of solving an overdetermined linear system Φc = y Note that for m = n this is equivalent to interpolation. MATLAB's polyfit works for m ≥ n. A. Donev (Courant Institute) Lecture VIII 11/04/2010 9 / 40 Function spaces Normal Equations Recall that one way to solve this is via the normal equations: (Φ?Φ) c? = Φ?y A basis set is an orthonormal basis if m ( X 1 if i = j (φi ; φj ) = φi (xk )φj (xk ) = δij = 0 if i 6= j k=0 Φ?Φ = I (unitary or orthogonal matrix) ) m ? ? X X c = Φ y ) ci = φi · fX = f (xk )φi (xk ) k=0 A. Donev (Courant Institute) Lecture VIII 11/04/2010 10 / 40 Orthogonal Polynomials on [−1; 1] Orthogonal Polynomials Consider a function on the interval I = [a; b]. Any finite interval can be transformed to I = [−1; 1] by a simple transformation. Using a weight function w(x), define a function dot product as: Z b (f ; g) = w(x)[f (x)g(x)] dx a For different choices of the weight w(x), one can explicitly construct basis of orthogonal polynomials where φk (x) is a polynomial of degree k (triangular basis): Z b 2 (φi ; φj ) = w(x)[φi (x)φj (x)] dx = δij kφi k : a A. Donev (Courant Institute) Lecture VIII 11/04/2010 11 / 40 Orthogonal Polynomials on [−1; 1] Legendre Polynomials For equal weighting w(x) = 1, the resulting triangular family of of polynomials are called Legendre polynomials: φ0(x) =1 φ1(x) =x 1 φ (x) = (3x2 − 1) 2 2 1 φ (x) = (5x3 − 3x) 3 2 n 2k + 1 k 1 d h ni φ (x) = xφ (x) − φ (x) = x2 − 1 k+1 k + 1 k k + 1 k−1 2nn! dxn These are orthogonal on I = [−1; 1]: Z −1 2 φi (x)φj (x)dx = δij · : −1 2i + 1 A. Donev (Courant Institute) Lecture VIII 11/04/2010 12 / 40 Orthogonal Polynomials on [−1; 1] Interpolation using Orthogonal Polynomials Let's look at the interpolating polynomial φ(x) of a function f (x) on a set of m + 1 nodes fx0;:::; xmg 2 I , expressed in an orthogonal basis: m X φ(x) = ai φi (x) i=0 Due to orthogonality, taking a dot product with φj (weak formulation): m m X X 2 2 (φ, φj ) = ai (φi ; φj ) = ai δij kφi k = aj kφj k i=0 i=0 This is equivalent to normal equations if we use the right dot product: ? 2 ? (Φ Φ)ij = (φi ; φj ) = δij kφi k and Φ y = (φ, φj ) A. Donev (Courant Institute) Lecture VIII 11/04/2010 13 / 40 Orthogonal Polynomials on [−1; 1] Gauss Integration −1 2 2 aj kφj k = (φ, φj ) ) aj = kφj k (φ, φj ) Question: Can we easily compute Z b Z b 2 aj kφj k = (φ, φj ) = w(x)[φ(x)φj (x)] dx = w(x)p2m(x)dx a a for a polynomial p2m(x) = φ(x)φj (x) of degree at most 2m? Let's first consider polynomials of degree at most m Z b w(x)pm(x)dx =? a A. Donev (Courant Institute) Lecture VIII 11/04/2010 14 / 40 Orthogonal Polynomials on [−1; 1] Gauss Weights Now consider the Lagrange basis f'0(x);'1(x);:::;'m(x)g, where you recall that 'i (xj ) = δij : Any polynomial pm(x) of degree at most m can be expressed in the Lagrange basis: m X pm(x) = pm(xi )'i (x); i=0 m m Z b X Z b X w(x)pm(x)dx = pm(xi ) w(x)'i (x)dx = wi pm(xi ); a i=0 a i=0 where the Gauss weights w are given by Z b wi = w(x)'i (x)dx: a A. Donev (Courant Institute) Lecture VIII 11/04/2010 15 / 40 Orthogonal Polynomials on [−1; 1] Back to Interpolation For any polynomial p2m(x) there exists a polynomial quotient qm−1 and a remainder rm such that: p2m(x) = φm+1(x)qm−1(x) + rm(x) Z b Z b w(x)p2m(x)dx = [w(x)φm+1(x)qm−1(x) + w(x)rm(x)] dx a a Z b = (φm+1; qm−1) + w(x)rm(x)dx a But, since φm+1(x) is orthogonal to any polynomial of degree at most m,(φm+1; qm−1) = 0 and we thus get: m Z b X w(x)p2m(x)dx = wi rm(xi ) a i=0 A. Donev (Courant Institute) Lecture VIII 11/04/2010 16 / 40 Orthogonal Polynomials on [−1; 1] Gauss nodes Finally, if we choose the nodes to be zeros of φm+1(x), then rm(xi ) = p2m(xi ) − φm+1(xi )qm−1(xi ) = p2m(xi ) m Z b X w(x)p2m(x)dx = wi p2m(xi ) a i=0 and thus we have found a way to quickly project any polynomial onto the basis of orthogonal polynomials: m X (pm; φj ) = wi pm(xi )φj (xi ) i=0 m m X X (φ, φj ) = wi φ(xi )φj (xi ) = wi f (xi )φj (xi ) i=0 i=0 A. Donev (Courant Institute) Lecture VIII 11/04/2010 17 / 40 Orthogonal Polynomials on [−1; 1] Gauss-Legendre polynomials For any weighting function the polynomial φk (x) has k simple zeros all of which are in (−1; 1), called the (order k) Gauss nodes, φm+1(xi ) = 0. The interpolating polynomial φ(xi ) = f (xi ) on the Gauss nodes is the Gauss-Legendre interpolant φGL(x). The orthogonality relation can be expressed as a sum instead of integral: m X 2 (φi ; φj ) = wi φi (xi )φj (xi ) = δij kφi k i=0 We can thus define a new weighted discrete dot product m X f · g = wi fi gi i=0 A.
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