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This module will give a very brief tutorial on some of the basic terms and ideas of linear algebra. These will include linear independence, span, and basis.

This brief tutorial on some key terms in linear algebra is not meant to replace or be very helpful to those of you trying togain a deep insight into linear algebra. Rather, this brief introduction to some of the terms and ideas of linear algebra ismeant to provide a little background to those trying to get a better understanding or learn about eigenvectors andeigenfunctions, which play a big role in deriving a few important ideas on Signals and Systems. The goal of theseconcepts will be to provide a background for signal decomposition and to lead up to the derivation of the Fourier Series .

Linear independence

A set of vectors x x i n x 1 x 2 x k are linearly independent if none of them can be written as a linear combination of the others.

Linearly Independent
For a given set of vectors, x 1 x 2 x n , they are linearly independent if c 1 x 1 c 2 x 2 c n x n 0 only when c 1 c 2 c n 0
Graphical representation of two vectors that are not linearly independent.

We are given the following two vectors: x 1 3 2 x 2 1 2 These are linearly independent since c 1 x 1 c 2 x 2 only if c 1 c 2 0 . Based on the definition, this proof shows that these vectors are indeed linearly independent. Again, wecould also graph these two vectors (see [link] ) to check for linear independence.

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Graphical representation of two vectors that are linearly independent.

Are x 1 x 2 x 3 linearly independent? x 1 3 2 x 2 1 2 x 3 -1 0

By playing around with the vectors and doing a little trial and error, we will discover the followingrelationship: x 1 x 2 2 x 3 0 Thus we have found a linear combination of these threevectors that equals zero without setting the coefficients equal to zero. Therefore, these vectors are not linearly independent !

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As we have seen in the two above examples, often times the independence of vectors can be easily seen through a graph.However this may not be as easy when we are given three or more vectors. Can you easily tell whether or not thesevectors are independent from [link] . Probably not, which is why the method used in the above solution becomesimportant.

Plot of the three vectors. Can be shown that a linear combination exists among the three, and therefore they are not linear independent.

A set of m vectors in n cannot be linearly independent if m n .


The span of a set of vectors x 1 x 2 x k is the set of vectors that can be written as a linear combination of x 1 x 2 x k span x 1 x k α α i n α 1 x 1 α 2 x 2 α k x k


A basis for n is a set of vectors that: (1) spans n and (2) is linearly independent.
Clearly, any set of n linearly independent vectors is a basis for n .

We are given the following vector e i 0 0 1 0 0 where the 1 is always in the i th place and the remaining values are zero. Then the basis for n is i i

    1 2 n
e i
i i
    1 2 n
e i is called the standard basis .

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h 1 1 1 h 2 1 -1 h 1 h 2 is a basis for 2 .

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Plot of basis for 2

If b 1 b 2 is a basis for n , then we can express any x n as a linear combination of the b i 's: α α i x α 1 b 1 α 2 b 2 α n b n

Given the following vector, x 1 2 writing x in terms of e 1 e 2 gives us x e 1 2 e 2

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Try and write x in terms of h 1 h 2 (defined in the previous example).

x 3 2 h 1 -1 2 h 2

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In the two basis examples above, x is the same vector in both cases, but we can express it in many different ways (we give only two out of many, manypossibilities). You can take this even further by extending this idea of a basis to function spaces .

As mentioned in the introduction, these concepts of linear algebra will help prepare you to understand the Fourier Series , which tells us that we can express periodic functions, f t , in terms of their basis functions, ω 0 n t .

Khan lecture on basis of a subspace

video from Khan Academy , Basis of a Subspace - 20 min.

Questions & Answers

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The answer is neither. The function, 2 = 0 cannot exist. Hence, the function is undefined.
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At high concentrations (>0.01 M), the relation between absorptivity coefficient and absorbance is no longer linear. This is due to the electrostatic interactions between the quantum dots in close proximity. If the concentration of the solution is high, another effect that is seen is the scattering of light from the large number of quantum dots. This assumption only works at low concentrations of the analyte. Presence of stray light.
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Source:  OpenStax, Signals and systems. OpenStax CNX. Aug 14, 2014 Download for free at http://legacy.cnx.org/content/col10064/1.15
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