Understanding Linear Combinations of Vectors

In summary, when a vector can be written as a linear combination of vectors in a span, it can be geometrically represented as being in the same subspace as the span of those vectors. This applies to both two and higher dimensional objects. It is important to note that the vectors in the span must be independent for the span to represent a hyper-plane or subspace.
  • #1
Bashyboy
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5
Hello Everyone,

Pardon me if the following is incoherent. From what I understood of what my professor said, he was basically saying that when a vector can be written as a linear combination of some vectors in a span, this means, geometrically, that the vector is in the plane that the span of vectors defines. How true is this? And if it is so, does anyone know of a good example that illustrates this point?

Thank you.
 
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  • #2
To clarify, a vector written as a linear combination of two vectors is in the plane defined by the two vectors. If the span has more than two vectors, it could define a higher dimensional object.
 
  • #3
I'm not sure that "plane" is the right word since that tends to imply two dimensions. A more general term would be "hyper-plane" but even that tends to imply that we are working in Rn. The most correct word is "subspace".

Consider the vectors <3, 1, 1, 0>, <2, 2, 0, 4>, <5, 3, 1, 4>, and <1, 0, 1, 0> in R4.
By definition of 'span' any vector in the span of those four vectors can be written as a linear combination, a<3, 1, 1, 0>+ b<2, 2, 0, 4>+ c<5, 3, 1, 4>+ d<1, 0, 1, 0>.

But those four vectors are NOT "independent". In particular, <5, 3, 1, 4>= <3, 1, 1, 0>+ <2, 2, 0, 4> so that is the same as (a+ c)<3, 1, 1, 0>+ (b+ c)<2, 2, 0, 4>+ d<1, 0, 1, 0>. That is a 3 dimensional hyper-plane (subspace) of R4.

For another example, let [itex]f(x)= x^3+x^2+ 2x[/itex], [itex]g=x^2+ 3x- 4[/itex], and [itex]h(x)= x^3+ 2x^2+ 5x- 4[/itex] in the vector space of cubic polynomials in x (which is four dimensional). Any vector in their span can be written as [itex]a(x^3+x^2+ 2x)+ b(x^2+ 3x- 4)+ c(x^3+ 2x^2+ 5x- 4)[/itex]. But [itex]x^2+ 2x^2+ 5x- 4= (x^3+ x^2+ 2x)+ (x^2+ 3x- 4)[/itex] so that is [itex](a+c)(x^3+x^2+2x)+(b+c)(x^2+ + 3x- 4)[/itex]. That is a two dimensional subspace but I wouldn't call it either a "plane" or a "hyper-plane".
 

1. What is a linear combination of vectors?

A linear combination of vectors is a mathematical operation that involves multiplying each vector by a constant and then adding them together. It is represented by the formula c1v1 + c2v2 + ... + cnvn, where c is a constant and v is a vector.

2. Why is understanding linear combinations of vectors important?

Understanding linear combinations of vectors is important because it is a fundamental concept in linear algebra, which is used in many areas of science and engineering. It allows us to represent and manipulate complex systems and equations, and is the basis for many advanced mathematical concepts.

3. How do you determine if a vector is a linear combination of other vectors?

A vector is a linear combination of other vectors if it can be expressed as a linear combination of those vectors. This means that the vector can be written as a sum of the other vectors multiplied by constants. If there exists a set of constants that satisfies this condition, then the vector is a linear combination of the other vectors.

4. Can linear combinations of vectors be used to solve systems of equations?

Yes, linear combinations of vectors can be used to solve systems of equations. This is because systems of equations can be represented as matrices and vectors, and linear combinations allow us to manipulate and solve these matrices and vectors.

5. Are there any real-world applications of linear combinations of vectors?

Yes, there are many real-world applications of linear combinations of vectors. Some examples include computer graphics, where linear combinations are used to represent and manipulate images, and physics, where linear combinations are used to describe the motion and forces acting on objects. They are also used in machine learning, economics, and many other fields.

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