How to Understand a Solution to a Linear Algebra Question?

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In summary, the conversation discusses a linear transformation and its matrix representation. The transformation is defined as T(f) = [f(0) - f(1), f(0), f(1)] and is applied to quadratic polynomials in the variable x. The basis for the polynomial is {1, x, x^2} and the basis for the ordered triple is {(1, 0, 0), (0, 1, 0), (0, 0, 1)}. The conversation also explains how to find the matrix representation of the transformation by applying it to each basis vector and writing the result in terms of the basis for the ordered triple. The link provided shows the logic of the solution.
  • #1
transgalactic
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i added a link in which i show a question and a solution to it

http://img163.imageshack.us/my.php?image=img8227nu6.jpg

i can't understand the solution that was given

i can't understand how they take a vector of a polinomial (1,0,0) for example and transform it into
another vector
?
 
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  • #2
You are given a linear transformation T:R3[x]->R3 defined by
[tex]T(f)= \left[\begin{array}{c}f(0)- f(1) \\ f(0) \\ f(1)\end{array}\right][/tex]

R3[x] is, of course, the space of all quadratic polynomials in the variable x, a+ bx+ cx2. Using basis {1, x, x2} such a polynomial can be written a(1)+ b(x)+ c(x2= [a, b, c]. R3 is the space of all ordered triples of real numbers, (a, b, c). Using basis {(1, 0, 0), (0, 1, 0), (0, 0, 1)} such a triple can be written a(1, 0, 0)+ b(0, 1, 0)+ c(0, 0, 1)= [a, b, c].

That is,T applied to the polynomial corresponding to f= (1, 0, 0)= 1 +0x+ 0x2= 1, is given by, since f(0)= f(1)= 1, T(f)= [f(1)- f(0), f(0), f(1)]= [1-1, 1, 1]= [0, 1, 1].

T applied to the polynomial corresponding to f= (0, 1, 0)= 0+ 1x+ 0x2= x, is given by, since f(0)= 0, f(1)= 1, T(f)= [f(1)- f(0), f(0), f(1)]= [1- 0, 0, 1]= [1, 0, 1].

T applied to the polynomial corresponding to f= (0, 0, 1)= 0+ 0x+ 1x2= x2 is given by, since f(0)= 0, f(1)= 1, T(f)= [f(1)- f(0), f(0), f(1)]= [1- 0, 0, 1]= [1, 0, 1].

The matrix of T, in those bases is the matrix having those as columns:
[tex]\left[\begin{array}{ccc} 0 & 1 & 1 \\ 1 & 0 & 0 \\ 1 & 1 & 1 \end{array}\right][/tex]
 
  • #3
why is f(0)= f(1)= 1 in the first

why is f(0)= 0, f(1)= 1 in the second

why is f(0)= 0, f(1)= 1 in the 3rd

?

it was not given
it was only my conclusion for this solution that the values of these variable are needed
to be like that in order to get to their result
 
  • #4
You are applying T to a function of the form f(x)= a+ bx+ cx2. In other words, "f" is just the general quadratic, not a specific function. Since we were asked to find a matrix representation of that function in the "standard" basis: e1= x2, e2= x, e3= 1, we apply the transformation to each of those functions in turn.

Te1 is T applied to the function f(x)= x2. For that function, f(0)= 02= 0 and f(1)= 12= 1.

Te2 is T applied to the function e2= x. For that function f(0)= 0 and f(1)= 1.

Te3 is T applied to the function e3= 1. For that function, f(0)= 1 and f(1)= 1.
 
  • #5
the bases are the temporary functions??

and we put 0 and one in them each time??

for the base e1=x^2
f(x)=x^2 ==> f(0)=0 f(1)=1

thanks
 
Last edited:
  • #6
Are you clear on what a "basis" (not "base") is? It is a collection of vectors so that every vector can be written as a linear combination of them in one way only.

"ex= x^2" is member of the given basis, not a "base" itself. Yes, in this problem, you "put 0 and one in them" because that is what the problem told you to do. The linear transformation was originally defined as:
[tex]T(f)= \left[\begin{array}{c}f(0)- f(1) \\ f(0) \\ f(1)\end{array}\right][/tex]

In general, if you are given a linear transformation, T, from a vector space U to a vector space V, you can represent T as a matrix for given bases of U and V (important: the matrix depends on the particular choice of bases, not only on T). A good way to do that is to apply the linear transformation to each of the basis vectors of U in turn, writing the result in terms of basis V. The numbers involved in that linear combination form a column of the matrix.

Take a very abstract, general, example. T goes from vector space U to vector space V. [itex]\{u_1, u_2, u_3\}[/itex] is a basis for U, [itex]\{v_1, v_2, v_4\}[/itex] is a basis for V (so U is 3 dimensional and V is 4 dimensional). [itex]Tu_1[/itex] is, of course, some vector in V and so can be written in terms of [itex]\{v_1, v_2, v_4\}[/itex]. Suppose [itex]Tu_1= av_1+ bv_2+ cv_3+ dv_4[/itex]. Then the first column of the matrix representing T (in those bases) is a, b, c, d.

The point is that once we have chosen a basis, we represent each vector by the numbers multiplying each basis vector, not writing the basis vectors themselves (which are "understood"). That is, [itex]u_1[/itex] itself is written as just (1, 0, 0, 0) because it is [itex]1*u_1+ 0*u_2+ 0*u_3[/itex] while [itex]Tu_1= av_1+ bv_2+ cv_3+ dv_4[/itex] would be written as (a, b, c, d). Applying T to [itex]u_1[/itex] would be the same as multiplying the matrix representing it by the column vector (1, 0, 0) and the result must be the column (a, b, c, d). Of course
[tex]\left[\begin{array}{ccc} a & * & * \\ b & * & *\\ c & * & * \\ d & * & * \end{array}\right]\left[\begin{array}{c} 1 \\ 0 \\ 0 \\\end{array}\right]= \left[\begin{array}{c} a \\ b \\ c \\ d \end{array}\right][/tex]
where the "*" are other numbers determined by applying T to the other basis vectors.
 
  • #7

1. What is linear algebra?

Linear algebra is a branch of mathematics that deals with the study of linear equations and their representations in vector spaces. It involves the manipulation and analysis of vectors, matrices, and linear transformations.

2. What are the applications of linear algebra?

Linear algebra has various applications in fields such as physics, engineering, computer graphics, economics, and statistics. It is used to solve systems of equations, analyze networks, and model complex systems.

3. What are the basic concepts in linear algebra?

The basic concepts in linear algebra include vectors, matrices, linear transformations, determinants, eigenvalues and eigenvectors, and systems of linear equations. These concepts form the foundation for the study of more advanced topics in linear algebra.

4. How is linear algebra used in machine learning?

Linear algebra is essential in machine learning as it is used to represent and manipulate data, calculate similarities between data points, and optimize mathematical models. It is also used in dimensionality reduction techniques and deep learning algorithms.

5. What are some resources for learning linear algebra?

There are various resources available for learning linear algebra, including textbooks, online courses, video tutorials, and practice problems. Some popular textbooks include "Linear Algebra and Its Applications" by David C. Lay and "Introduction to Linear Algebra" by Gilbert Strang. Online platforms like Khan Academy and Coursera also offer free courses on linear algebra.

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