Linear Algebra Theorems for Graduate Level Course

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SUMMARY

The discussion focuses on essential theorems in graduate-level Linear Algebra, highlighting 14 key concepts including the Rank-Nullity Theorem, the Unique Dual Basis Theorem, and the Spectral Theorem. Participants emphasize the importance of the Rational Canonical Form Theorem and the Jordan Canonical Form in representing linear operators on finite-dimensional vector spaces. The conversation also touches on the significance of change-of-basis matrices and similarity transformations in relation to canonical forms.

PREREQUISITES
  • Understanding of finite-dimensional vector spaces
  • Familiarity with linear transformations and their properties
  • Knowledge of characteristic polynomials and eigenvalues
  • Concepts of inner product spaces and adjoint operators
NEXT STEPS
  • Study the Rank-Nullity Theorem in detail
  • Explore the Rational Canonical Form Theorem and its applications
  • Learn about the Jordan Canonical Form and its significance
  • Investigate the properties of self-adjoint operators and their eigenvalues
USEFUL FOR

Graduate students in mathematics, educators teaching Linear Algebra, and researchers focusing on linear transformations and vector space theory.

CoachZ
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If you were in Linear Algebra at a graduate level, or have been at some point in the past, what are the main theorems you would think of as being the most important for that course. In the next few days I have an exam and this is very good for my studying. Here's my top 14 list (nice round number) =) , but it might change depending on what other people say:

1. Rank Nullity Theorem

2. Let V and W be finite dimensional vector spaces over F, s.t. dim W = dim V. If T is a linear transformation from V into W, the following are equivalent:
First, T is invertible
Second, T is non-singular
Third, T is onto, i.e. the range of T is W

3. Unique dual basis theorem

4.If f is a polynomial over F with derivative f', then f is a product of distinct irreducible polynomials over F iff f and f' are relatively prime.

5. Let T be a linear operator on a finite dimensional space V and let c be a scalar. TFAE:
First, c is a characteristic/eigenvalue of T.
Second, The operateor (c-TI) is singular, hence not invertible
Third, det(c-TI) = 0

6. Let T be a linear operator on a finite dimensional space V. Let, c1,c2,...,ck be the distinct characteristic/eigenvalues of T and let Wi be the nullspace of (c-TI), TFAE:
First, T is diagonalizable
Second, The characteristic polynomial for T is f = (x-c1)^d1 ... (x-ck)^dk and dim Wi = di, where i = 1,...,k.
Third, dimW1 + dimW2 + ... + dimWk = dimV

7. (Generalized) Cayley-Hamilton Theorem

8. Primary Decomposition Theorem

9.Cyclic Decomposition Theorem

10. Not so much a Theorem, but Gram-Schmidt

11.Inner Product Space Theorem (with properties)

12. For any linear operator T on a finite dimensional inner product space V, there exists a unique linear operator T* on V such that <Ta,b> = <a,T*b>. (T* is called the adjoint of T).

13. Not so much a Theorem, but self-adjoint operators, i.e. Hermitian properties, specifically the fact that there exists an orthonormal basis of eigenvectors and furthermore each eigenvalue of the operator is real.

14. Spectral Theorem
 
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I would also put Rank + Nullity Theorem at the top, or the RCF theorem (see below).

Perhaps we have been following a bit different set of topics than in your course, but for us one of the most important theorems was the Rational Canonical Form Theorem, and subsequently Jordan Canonical Form - so many things after derive from these. (Maybe this is what you refer to as primary and cyclic decomposition theorems). This gave us way to represent any linear operator on a finite dimensional vector space using its minimal polynomial by a block-diagonal matrix with each block corresponding to a term of the minimal polynomial, and blocks of companion matrices along the diagonal of each primary block.

Otherwise I would agree with your list, I can't think of much else. One other thing may be change-of-basis matrix and similarity, we say A is similar to B if A = PBP^-1 for some invertible P, a change of basis matrix, though this is related to the discussion of canonical form theorems.
 

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