What is the optimal polynomial of degree 2 to minimize the given expression?

In summary, the problem is to find the polynomial of degree 2 that minimizes the expression \int \left|cos(\frac{\pi t}{2}) - p(t)\right|^{2}dt with the given conditions. The solution involves using the method of least squares and linear algebra.
  • #1
sunrah
199
22

Homework Statement



[itex]P_{2} \subset L_{2} [/itex] is the set of all polynomials of degree [itex]n \leq 2[/itex]. Complete the following approximation. In other words find the polynomial of degree 2 that minimises the following expression:

[itex]\int \left|cos(\frac{\pi t}{2}) - p(t)\right|^{2}dt = min[/itex] with -1 <= t >= 1

Homework Equations



[itex]x(t) = cos(\frac{\pi t}{2}) [/itex] this is real so [itex]\overline{x} = x [/itex]

[itex]p(t) = \sum a_{n}t^{n}[/itex] because polynomial max 2nd degree: 0 <= n >= 2 (we do not know whether it has imaginary terms or not)

The Attempt at a Solution



[itex]\left|cos(\frac{\pi t}{2}) - p(t) \right|^{2} = \left|x(t) - p(t) \right|^{2} [/itex]

[itex]= \left\langle x(t) - p(t),x(t) - p(t)\right\rangle [/itex]

[itex]= \int (x(t) - p(t))( \overline{x(t) - p(t)})dt [/itex] scalar product defined in set of polynomial functions

[itex]= \int (x\overline{x} - x\overline{p} - \overline{x}p + p\overline{p}) dt [/itex]

[itex]= \int (x^{2} - x\overline{p} - xp + p\overline{p}) dt [/itex]

[itex]= \int (x^{2} - x\sum\overline{a_{n}}t^{n} - x\sum a_{n}t^{n} + \sum a_{n}\overline{a_{n}}t^{2n}) dt[/itex]

[itex]= \int (x^{2} - x\sum(\overline{a_{n}} + a_{n})t^{n} + \sum \left|a_{n} \right|^{2}t^{2n}) dt[/itex]

[itex]= \int (x^{2} - 2x\sum Real(a_{n})t^{n} + \sum \left|a_{n} \right|^{2}t^{2n}) dt[/itex]now I don't know what to do. what conditions make this minimal?
 
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  • #2
No need to reply, I have now a solution using the method of least squares and some linear algebra.
 

1. What is minimisation in Hilbert space?

Minimisation in Hilbert space refers to the process of finding the smallest value of a function in a specific Hilbert space. This is done by finding the point in the space where the gradient of the function is equal to zero, also known as the critical point.

2. Why is minimisation in Hilbert space important?

Minimisation in Hilbert space is important because it allows us to find optimal solutions to various problems in mathematics, physics, and engineering. It is also used in many machine learning algorithms to find the best parameters for a given model.

3. How is minimisation in Hilbert space different from other minimisation methods?

Minimisation in Hilbert space is different from other minimisation methods because it takes into account the specific properties of a Hilbert space, such as its inner product and norm, to find the optimal solution. It also allows for the minimisation of functions defined on infinite-dimensional spaces.

4. What are some applications of minimisation in Hilbert space?

Minimisation in Hilbert space has many applications in various fields such as signal processing, control systems, and optimization. It is also used in quantum mechanics to find the ground state of a system and in image processing to denoise images and reconstruct missing information.

5. What are the key concepts involved in minimisation in Hilbert space?

The key concepts involved in minimisation in Hilbert space include the inner product, norm, gradient, and critical points. Other important concepts include convexity, duality, and the projection theorem. Knowledge of linear algebra and functional analysis is also necessary to understand minimisation in Hilbert space.

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