First, some
definitions:
The least squares approximation of a function f is a function

such as:

, for every

.
Also:
Now the
problem:
Let's consider

a class of approximations with the following properties:
- all functions

are defined on a symmetric interval
![[-a, a]](http://latex.codecogs.com/png.latex?[-a, a])
;
- if
\in\theta_n)
, then
\in\theta_n)
;
Consider
dt)
, where

is an even function.
Prove that if f is an even function on
![[-a, a]](http://latex.codecogs.com/png.latex?[-a, a])
, its least squares approximation

is even.
My attempt of a
solution started by trying to prove that the approximation is a linear combination of even functions. So we consider an orthogonal basis

for the given class of approximations. In this case, the least squares approximation is:

, where:
/(\pi_j,\pi_j))
(u,v) is the dot product of functions u and v. More precisely:
=\int_{-a}^a u(t)v(t)\omega(t)dt)
Using this formula and the hypothesis, it can be proven that if

is an odd function, then

, because
)
is the integral of an odd function on a symmetric interval.
This way, we eliminate from the approximation

all the odd basis functions. So now

is a linear combinations of even functions and functions that are neither even, nor odd. The even functions are good, because a linear combination of even function is also a even function. But what happens when the basis function

is neither even, nor odd? I tried decomposing

in combinations of even and odd functions, but this approach does not seem to lead anywhere...
Any help would be appreciated.