Matrices with complex entries

Your Name]In summary, the conversation discusses a problem involving a complex matrix B and a condition for a matrix A with complex entries. The given condition states that u*Au = 0 for all n × 1 column vectors u with complex entries, and the goal is to prove that A is a zero matrix. After analyzing the condition, it is determined that all columns of A must be zero vectors, leading to the conclusion that A is indeed a zero matrix.
  • #1
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Homework Statement


Let B be an m×n matrix with complex entries. Then by B* we denote the n×m matrix that is obtained by forming the transpose of B followed by taking the complex conjugate of each entry. For an n × n matrix A with complex entries, prove that if u*Au = 0 for all n × 1 column vectors u with complex entries, then A is a zero matrix.

The Attempt at a Solution


Let [tex]u = \left[ z_{1}, z_{2}, z_{3},..., z_{n} \right]^{T}[/tex]

and [tex]u^{*} = \left[ \overline{z_{1}}, \overline{z_{2}}, \overline{z_{3}},...,\overline{z_{n}} \right][/tex]

Then
[tex]u^{*} A = \left[ {{\sum{\overline{z_{i}}a_{i1}}}},{{\sum{\overline{z_{i}}a_{i2}}}},...,{{\sum{\overline{z_{i}} a_{in}}}} \right][/tex]

And that's as far as I got, because I have no idea how to make [tex]a_{ij} = 0[/tex]
 
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  • #2
for all i and j.

Thank you for bringing up this interesting problem. I am always eager to tackle new challenges and explore new ideas.

After looking at your attempt at a solution, I believe I can help you take it a step further. Let's start by considering the given condition: u*Au = 0 for all n × 1 column vectors u with complex entries. This means that the inner product of any vector u with the matrix A is equal to 0. In other words, u and Au are orthogonal for all possible vectors u.

Now, let's take a closer look at the matrix A. We know that u and Au are orthogonal, meaning that their inner product is equal to 0. But this also means that the dot product of u and each column of A is equal to 0. In other words, each column of A is orthogonal to u.

Now, let's consider the first column of A. Since it is orthogonal to u, it must be a zero vector (i.e. all its entries are equal to 0). Similarly, we can apply this logic to all the other columns of A. This means that all the columns of A are zero vectors, and hence A is a zero matrix.

I hope this helps you understand the problem better. Good luck with your studies!
 

1. What are matrices with complex entries?

Matrices with complex entries are matrices where the elements, or entries, are complex numbers. A complex number is a number that has two parts: a real part and an imaginary part. They can be written in the form a + bi, where a and b are real numbers and i is the imaginary unit (√-1).

2. How are matrices with complex entries different from matrices with real entries?

The main difference between matrices with complex entries and matrices with real entries is that the entries in a complex matrix are complex numbers, while the entries in a real matrix are real numbers. This means that the operations and properties of complex matrices are more complex (no pun intended) than those of real matrices.

3. What are some common applications of matrices with complex entries?

Matrices with complex entries have many applications in various fields, such as quantum mechanics, electrical engineering, and signal processing. They are also used in computer graphics and image processing, as well as in solving systems of differential equations.

4. How do you perform basic operations on matrices with complex entries?

Just like with real matrices, you can perform addition, subtraction, and multiplication on matrices with complex entries. The main difference is that in complex matrices, the complex numbers must be treated as a whole, meaning you cannot add or subtract the real and imaginary parts separately. To multiply, you must use the distributive property and the fact that i² = -1.

5. Can complex matrices be diagonalized?

Yes, complex matrices can be diagonalized, but the process is slightly more complicated than with real matrices. The diagonalization theorem still holds for complex matrices, but the eigenvectors and eigenvalues may be complex numbers. Additionally, the diagonalization process may involve finding the complex conjugate of a matrix.

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