in the problem (3.5). The purpose of such a process is to create a controlled sub-space
inside 
, which can be designed to approximate the solution of (3.6) with arbitrary
precision.
The basics of Galerkin’s method consist of a clever discretization of the infinite linear space
in the problem (3.5). The purpose of such a process is to create a controlled sub-space
inside , which can be designed to approximate the solution of (3.6) with arbitrary
precision.
Consider the finite sub-space 
of 
with dimension N. Consider also the functions
as an orthogonal basis of 
. Hence, every function 
in 
can be written as
linear combination of the basis as in
 | (3.9) |
The discrete formulation of the variational problem (3.5) can be written as
 | (3.10) |
Actually, the formulation (3.10) can be simplified. It can be proven that if 
satisfies
(3.10) only for the basis function of 
, it satisfies it for every element in 
. To show
this, replace 
in (3.10) by the basis projection (3.9) as in
On the assumption 
for j = 1..N, the equality (3.11) holds and 
satisfies
(3.10) for every element of 
. Consequently, the simplified version of (3.10) can be
summarized by:
 | (3.12) |
The formulation (3.12) is well suitable for the construction of a numerical scheme, because it
restricts the search for the solution 
to the computation of a finite number of equations
(N). Hence, the problem shifts to a more concrete perspective, and a method
is required to compute 
from those N equations. For that purpose, consider
the expansion of 
in the basis of 
, in the same way as was done before
for 
(3.9). By substituting 
appropriately in (3.10) the obtained function
is
In fact, the substitution of 
expansion in (3.12) leads to the transformation of the
discrete problem in a linear system 
with N equations, where A, x and b are given
by
 | (3.14) |
