Linearly Dependent? Clarifying Wronskian Results

  • Thread starter kdinser
  • Start date
  • Tags
    Linearly
In summary, if the Wronskian of a set of functions comes out as either 0 or never 0, then the set of functions is linearly dependent. If the Wronskian of a set of functions comes out as anything other than 0 or never 0, then the set of functions is linearly independent.
  • #1
kdinser
337
2
I just want to make sure I'm clear on the whole linearly dependent thing.

If I find the Wronskian of a set of functions and it comes out:

12x^2 + 12x

This would indicate that my set of functions is linearly dependent if the interval included x=0 and would be linearly independent if x never equaled 0.

if I find the wronskian of a set of functions and it comes out:

6 + 12x

This would show that my set of functions is linearly independent for all x.
 
Physics news on Phys.org
  • #2
Would it? what if x= -1/2?

Of course, on can show that if the functions involved all satisfy the same linear, homogeneous differential equation, THEN their Wronskian is either always 0 or never 0.
 
  • #3
Thanks for pointing that out. That's exactly the kind of thing my professor would put on a test.
 
  • #4
kdinser said:
I just want to make sure I'm clear on the whole linearly dependent thing.

If I find the Wronskian of a set of functions and it comes out:

12x^2 + 12x

This would indicate that my set of functions is linearly dependent if the interval included x=0 and would be linearly independent if x never equaled 0.

if I find the wronskian of a set of functions and it comes out:

6 + 12x

This would show that my set of functions is linearly independent for all x.

There is an important distinction to be made here regarding the definition of Linear Dependence. Normally, Linear Dependence for an arbitrary differentiable set of Functions is defined on an OPEN INTERVAL "I" and requires the Wronskian to be zero (0) everywhere on "I". Being (0) at 1 point in "I" (or a finite number of points in "I") does not usually indicate Linear Dependence if there exists at least 1 other point in "I" for which the Wronskian is NON-zero.

Most definitions of Linear Dependence would hold that the 2 above Wronskians indicate Linear INdependence on all OPEN INTERVALS, regardless if the interval contained x=(0) in the first case or x=(-1/2) in the second. Again, this results because all such Open Intervals contain at least 1 point for which the Wronskian is NON-zero.


~~
 
Last edited:

1. What does it mean for a set of vectors to be linearly dependent?

When a set of vectors is linearly dependent, it means that at least one of the vectors in the set can be expressed as a linear combination of the other vectors. In other words, one or more vectors in the set are redundant and can be removed without changing the span of the set.

2. How can I determine if a set of vectors is linearly dependent?

One way to determine if a set of vectors is linearly dependent is by calculating the determinant of the matrix formed by the vectors. If the determinant is equal to 0, then the vectors are linearly dependent. Another method is to check if any of the vectors can be written as a linear combination of the others.

3. What is the significance of the Wronskian when studying linearly dependent vectors?

The Wronskian is a mathematical tool used to determine the linear independence of a set of functions or vectors. If the Wronskian is equal to 0, then the functions or vectors are linearly dependent. If the Wronskian is non-zero, then the functions or vectors are linearly independent.

4. Can a set of vectors be both linearly dependent and linearly independent?

No, a set of vectors cannot be both linearly dependent and linearly independent. These two concepts are mutually exclusive. If a set of vectors is linearly dependent, then it is not linearly independent and vice versa.

5. How can I use the concept of linearly dependent vectors in real-world applications?

The concept of linearly dependent vectors is commonly used in fields such as physics, engineering, and computer science. In physics, it can be used to analyze forces acting on an object in equilibrium. In engineering, it can be used to solve systems of linear equations. In computer science, it can be used in machine learning algorithms to reduce the dimensionality of data and improve efficiency.

Similar threads

  • Calculus and Beyond Homework Help
Replies
1
Views
252
  • Calculus and Beyond Homework Help
Replies
2
Views
247
  • Introductory Physics Homework Help
Replies
6
Views
615
  • Introductory Physics Homework Help
Replies
7
Views
344
Replies
11
Views
473
  • Calculus and Beyond Homework Help
Replies
14
Views
363
  • Linear and Abstract Algebra
Replies
3
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
22
Views
2K
  • Differential Equations
Replies
8
Views
2K
  • Introductory Physics Homework Help
Replies
28
Views
1K
Back
Top