Linear Transformations: One-to-One and Onto Conditions

In summary: Saying that a linear transformation is "onto" means that for every "v" in the target space (in this case, R5), there is at least one "u" in the domain space (R3) that is mapped to it. In particular, that means that the range of the linear transformation is the entire target space. Since the range is the orthogonal complement of the nullspace, that means the nullspace is the "trivial" space consisting only of the 0 vector. In summary, if a linear transformation T : R3 -> R5 is one-to-one, then its rank is three and its nullity is zero. If a linear transformation T : R3 -> R5 is onto,
  • #1
phrygian
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Homework Statement




(124) If a linear transformation T : R3 -> R5 is one-to-one, then
(a) Its rank is five and its nullity is two.
(b) Its rank and nullity can be any pair of non-negative numbers that add
up to five.
(c) Its rank is three and its nullity is two.
(d) Its rank is two and its nullity is three.
(e) Its rank is three and its nullity is zero.
(f) Its rank and nullity can be any pair of non-negative numbers that add
up to three.
(g) The situation is impossible.

(125) If a linear transformation T : R3 -> R5 is onto, then
(a) Its rank is five and its nullity is two.
(b) Its rank is two and its nullity is three.
(c) Its rank is three and its nullity is zero.
(d) Its rank and nullity can be any pair of non-negative numbers that add
up to three.
(e) Its rank is three and its nullity is two.
(f) Its rank and nullity can be any pair of non-negative numbers that add
up to five.
(g) The situation is impossible.








Homework Equations





The Attempt at a Solution




These two problems on my practice test have me completely stumped, could someone help shed some light?

I understand the definitions of onto and one-to-one, but don't understnad how to connect this to the null space of a linear transformation from Rm to Rn?
 
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  • #2
Saying that a linear transformation is "one-to-one" means that only one "u" is mapped to a specific "v", right? In particular, that means only one vector is mapped to the 0 vector. Since any linear transformation maps the 0 vector to the 0 vector, a one to one mapping maps only the 0 vector to the 0 vector.

That is, a linear transformation is one to one if and only if its nullspace is the "trivial" space consisting only of the 0 vector.
 

Related to Linear Transformations: One-to-One and Onto Conditions

1. What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another vector space while preserving the linear structure and operations. In simpler terms, it is a transformation that preserves the lines and origin of a coordinate system.

2. How do you represent a linear transformation?

A linear transformation can be represented by a matrix. The columns of the matrix represent the image of the standard basis vectors of the domain in the range. The transformation can also be represented by a set of equations, known as the transformation equations, that describe how each coordinate in the domain is mapped to the range.

3. How does a linear transformation affect vectors?

A linear transformation affects vectors by scaling, rotating, shearing, or reflecting them. The transformation can also change the dimension of the vector space. In general, a linear transformation affects vectors by changing their direction and/or magnitude.

4. What is the difference between a linear transformation and a nonlinear transformation?

The main difference between a linear and nonlinear transformation is that a linear transformation preserves the linearity of the space, while a nonlinear transformation does not. This means that a linear transformation will always produce a straight line when mapping points, while a nonlinear transformation can produce curved or nonlinear shapes.

5. What are some real-life applications of linear transformations?

Linear transformations have various real-life applications in fields such as physics, engineering, computer graphics, and economics. They are used to model physical systems, such as the movement of objects in space, and to solve linear equations. Linear transformations are also used in computer graphics to create 3D animations and in economics to model supply and demand curves.

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