1) Approximation of Functions Flashcards

(42 cards)

1
Q

What does interpolation guarantee about the existence and uniqueness of a polynomial passing through given points

A

There is a unique polynomial of degree at most n that exactly interpolates given values at n + 1 distinct points

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2
Q

How can an interpolating polynomial be written explicitly

A
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3
Q

What is the error formula for an interpolating polynomial of degree at most n at n + 1 distinct points, assuming f has n + 1 continuous derivatives

A
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4
Q

What do the notations C[a,b] and C(a,b) represent

A
  • C[a, b] denotes the set of real-valued functions f defined and continuous on the closed interval [a, b]
  • C(a, b) denotes the set of real-valued functions f defined and continuous on the open interval (a, b)
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5
Q

What does the Weierstrass Approximation Theorem state

A
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6
Q

What is a limitation of the Weierstrass Approximation Theorem in practice

A

The polynomial p(x) may need to be of very high degree to achieve the desired accuracy, so the theorem is mainly of theoretical interest rather than practical use

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7
Q

What are Chebyshev polynomials

A
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8
Q

What recurrence relation do the Chebyshev polynomials satisfy, and what does it imply about their degree

A
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9
Q

What are the key properties of Chebyshev polynomials

A
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10
Q

What are the properties that define a norm on a vector space V over R

A
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11
Q

What is a normed linear space

A

The pair (V, ||·||)

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12
Q

What are some examples of norms on the space C[a,b]

A
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13
Q

What is the Lp-norm

A
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14
Q

What does it mean for a function g∈S to be a best approximation to f∈V from a subspace S⊂V

A
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15
Q

What does the existence theorem for best approximation state in a finite-dimensional subspace

A

If (V, ||·||) is a normed linear space and S is a finite dimensional subspace of V , then for every f ∈ V there exists at least one best approximation g ∈ S

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16
Q

What is the space Pn, and how is it used in relation to C[a,b]

A

Pn is the vector space consisting of all polynomials of degree at most n. For any interval [a,b], we consider Pn as a subspace of C[a,b]

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17
Q

What does it mean for pn ∈ Pn to be a best L∞ (minimax) approximation to f ∈ C[a,b]

18
Q

What is the best L∞ approximation to a function f∈C[a,b] from P0 and how is it determined

19
Q

What is the Chebyshev equioscillation theorem

A

The error 𝑓(𝑥) − 𝑝𝑛(𝑥) reaches its maximum absolute value at n+2
distinct points{𝑥𝑘}𝑘=0 𝑛+1 ⊂ [𝑎, 𝑏], and the sign of 𝑓(𝑥𝑘) − 𝑝𝑛(𝑥𝑘) alternates between successive points.

20
Q

Why does the best L∞ approximation pn to a function f∈C[a,b] interpolate f at at least n+1 points

21
Q

What can be said about the existence and uniqueness of the best L∞ approximation from Pn to f∈C[a,b]

A

The best L∞ approximation to f∈C[a,b] from Pn exists and is unique

22
Q

What is the best L∞ approximation to x ^(n+1) on [−1,1] from polynomials of degree at most n

23
Q

Which monic polynomial of degree n+1 has the smallest L∞ norm on [−1,1], and what is its norm

24
Q

When are a set of functions {Φj}j=0, n considered linearly independent, and what is such a set called

25
What can be said about polynomials Φ𝑗 (x) of degree j, and their role in expressing polynomials of degree at most n
If Φj (x) is a polynomial of degree j, then {Φj}j=0 ,n are linearly independent, and any polynomial pn ∈ Pn can be uniquely expressed as a linear combination of Φ0, Φ1,..., Φn
26
What is true about finding the best weighted L2 approximation to f(x)∈C[a,b] using a set of linearly independent functions {Φj} j=0, n
27
What is a weight function, and how is the weighted inner product defined
28
When are two functions f,g∈C[a,b] said to be orthogonal with respect to a weight function w(x)
29
What defines an orthogonal and an orthonormal basis {Φk}, and how do these properties simplify the normal equations for approximation
30
With respect to which weight function are the Chebyshev polynomials orthogonal on (−1,1)
31
How can you construct an orthogonal polynomial basis {Φk} given a weight function w(x)
32
What are the Legendre polynomials, their weight function, and their recurrence relation
33
What is an example of an orthogonal basis that does not consist of polynomials, and what is its weight function
34
What happens to the error in the best L2 approximation of f∈C[a,b] from Pn as n increase
35
What is a [n/m] Padé approximant of a function f(x)
36
Show that if an [n/m] Pade approximant exists then it is unique
37
What is the difference between the naive method and Horner’s method for evaluating a polynomial, and how do their operations compare
38
What are the advantages and drawbacks of polynomial approximants
Advantages - * Guaranteed approximation by Weierstrass’ theorem * Easy to evaluate, differentiate, and integrate Disadvantages - * Always diverge as x→±∞; no finite asymptotes * Cannot model vertical/horizontal asymptotes * May oscillate wildly (Runge phenomenon)
39
How can a rational function be efficiently evaluated, and what is the cost when using Horner’s method
40
How can we generate a sequence of Padé approximants for a function using a continued fraction
41
How does the bottom-up algorithm evaluate a continued fraction, and what is its computational cost
42
What is the top-down algorithm for evaluating a continued fraction, and what are its advantages and computational cost
p-1 = 1 p0 = b0 q-1 = 0 q0 = 1