Proving Vector Addition with Linear Independence

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In summary: I apologize for not pointing it out - V^2(O) is the set of all radius vectors in the Euclidean plane, where your story is, of course, set up in a Cartesian coordinate system.
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b2386
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Hi all,

While working on my differential equations homework, I encountered a proof dealing with linear independence and vector addition. I sort of know how to proceed but, not having dealt with formal proofs much, I am afraid that I may not be addressing all necessary apects of the proof. Anyway, here is the question: Prove that if the vectors x = (x_1)i + (x_2)j and y = (y_1)i + (y_2)j
are linearly independent, then any vector z = (z_1)i + (z_2)j can be expressed as a linear combination of x and y.

The linear combination of x and y gives us (x_1)i + (x_2)j + (y_1)i + (y_2)j. Rearranging terms, [(x_1)+(y_1)]i + [(x_2)+(y_2)]j = x+y. We can now define x+y = z. Therefore, z = (z_1)i + (z_2)j

Where do I bring in the necessity of linear independence?
 
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b2386 said:
Prove that if the vectors x = (x_1)i + (x_2)j and y = (y_1)i + (y_2)j are linearly independent, then any vector z = (z_1)i + (z_2)j can be expressed as a linear combination of x and y.

Actually, this question is kind of 'definition-like'. You have two vectors in V^2(O). Any set of two vectors in V^2(O) which are linearly independent (i.e. non collinear) form a basis for V^2(O), and hence every vector from V^2(O) can be represented uniquely as a linear combination of these two independent vectors.
 
  • #3
What exactly is V^2(O)? I haven't had linear algebra so I am probably unfamiliar with some terminology.

EDIT: Is that just a 2-D vector space?
 
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  • #4
b2386 said:
What exactly is V^2(O)? I haven't had linear algebra so I am probably unfamiliar with some terminology.

My apologies for not pointing it out - V^2(O) (or call it whatever you like) is the set of all radius vectors in the Euclidean plane, where your story is, of course, set up in a Cartesian coordinate system.

EDIT: it could be, but be careful when using that terminology; formally, a 2-D vector space is any 2-dimensional vector space - its elements don't need to be radius vectors!
 

FAQ: Proving Vector Addition with Linear Independence

1. What is the concept of "Proving Vector Addition with Linear Independence"?

"Proving Vector Addition with Linear Independence" is a mathematical concept that involves determining whether a set of vectors can be added together to create a linear combination of those vectors. This is often used in linear algebra to test the independence of a set of vectors and to determine if they span a particular vector space.

2. How is linear independence used in proving vector addition?

Linear independence is used in proving vector addition by showing that a set of vectors can be added together in a unique way to create any other vector in the same vector space. This is done by showing that the coefficients in the linear combination of the vectors are unique and cannot be changed without changing the resulting vector.

3. What is the difference between linear independence and linear dependence?

Linear independence refers to a set of vectors that cannot be combined in a non-trivial way to create the zero vector. On the other hand, linear dependence refers to a set of vectors that can be combined in a non-trivial way to create the zero vector. In other words, linear independence means that the vectors are not redundant, while linear dependence means that there is redundancy among the vectors.

4. Why is proving vector addition with linear independence important?

Proving vector addition with linear independence is important because it allows us to determine whether a set of vectors can be used as a basis for a vector space. It also helps us to understand the structure and properties of vector spaces, and is a fundamental concept in linear algebra that is used in various applications in science and engineering.

5. What are some real-world applications of proving vector addition with linear independence?

The concept of proving vector addition with linear independence has various real-world applications, such as in engineering, physics, and computer graphics. For example, in computer graphics, linear independence is used to determine the 3D position of an object based on its projection in a 2D image. In physics, linear independence is used to analyze forces and motion in vector spaces. In engineering, it is used in signal processing, control systems, and circuit analysis.

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