Linear and tensor methods repair
Component calculations become reliable only when every array has an invariant meaning. This focused review rebuilds the distinction between a vector and its coordinates, between a covector and a row of numbers, and between a linear map and the matrix used to represent it. The payoff in QFT is immediate: index placement, adjoints, kinetic terms, and changes of field basis stop being rules to memorize and become consequences of types.
Required background. You should be able to multiply small complex matrices, solve a two-by-two linear system, and work with complex conjugation. No abstract tensor notation is assumed. If you arrived from the mathematics diagnostic, return there after the final check and retry with different data.
Vectors, covectors, and basis changes
Section titled “Vectors, covectors, and basis changes”Let be a finite-dimensional complex vector space with basis . A vector and a covector have expansions
where is the dual basis, defined by . The positions of the indices record different types: are coordinates of a vector, while are coordinates of a linear functional.
Choose a new basis
with invertible. Because the geometric objects do not change, their components must obey
The opposite transformations make the pairing basis independent:
This cancellation—not the visual appearance of one upper and one lower index—is what licenses the contraction. A systematic treatment of bases, duals, and maps appears in Axler 2015, chs. 1–3.
Linear maps and tensor type
Section titled “Linear maps and tensor type”A linear map belongs to . In bases of and , its components carry one index for each space:
The repeated index pairs with ; the free index says that the result lies in . If the same space is used on both sides and its basis changes by , the matrix changes by similarity,
That formula is not a universal rule for every two-index array. A bilinear form belongs to and transforms instead as . A Hermitian form transforms as . The types decide the transformation law.
A quick index check prevents many errors:
- Name the space or dual space associated with each index.
- Pair only a space with its dual, unless an explicit form supplies a map between them.
- Confirm that the free indices have the type of the claimed result.
- Change basis once. A purported scalar should not change.
Forms, raising indices, and adjoints
Section titled “Forms, raising indices, and adjoints”A nondegenerate Hermitian form maps a vector to a covector. In components,
Lowering an index therefore uses additional structure; it is not typography. If is positive definite it defines an inner product. In Lorentzian QFT, the spacetime metric is nondegenerate but indefinite, so Euclidean geometric intuition must be used with care.
The -adjoint of is defined by
With the convention that the form is conjugate-linear in its first argument, this gives
Under the non-unitary basis change above,
Thus self-adjointness is an invariant statement even though the familiar matrix condition holds only in a -orthonormal basis. For the role of adjoints and domains in quantum theory, see Hall 2013, ch. 2.
A QFT bridge: why a kinetic term is a scalar
Section titled “A QFT bridge: why a kinetic term is a scalar”For a scalar field , the derivative is a covector. The inverse metric supplies the map from covectors to vectors,
The kinetic density
is a scalar because every tensor index is paired according to type. Without the metric, two covectors cannot be contracted canonically. Under a linear field redefinition , a multiplet kinetic matrix changes with the basis; the quadratic form does not:
This is the same invariant-content/component-array distinction used above. It later controls flavor mixing, gauge representations, propagator matrices, and the normalization of fields.
Exercises
Section titled “Exercises”1. Pairing under a non-orthogonal basis change
Section titled “1. Pairing under a non-orthogonal basis change”Let
where and are the component columns of a vector and a covector in the original basis. Find and , then verify .
Solution
Here
The vector and covector laws give
The two pairings agree:
Using on both objects would fail because a covector is not a second copy of a vector.
2. An adjoint in a non-orthonormal basis
Section titled “2. An adjoint in a non-orthonormal basis”Take
Compute and check the defining relation with and .
Solution
Because ,
Now , so
Also , and therefore
The ordinary matrix is Hermitian but not self-adjoint with respect to this ; the form is part of the statement.
3. Diagnose an illegal contraction
Section titled “3. Diagnose an illegal contraction”Let and . Explain what is missing from , and write a scalar after a nondegenerate metric is supplied.
Solution
Both displayed indices label covectors, so there is no natural pairing between them. Once an inverse metric is given, the scalar is
Writing two lower indices with the same label does not create the required map. In an orthonormal Euclidean basis the metric components happen to be a Kronecker delta, which can hide this structure.
Re-check and return
Section titled “Re-check and return”Choose a new invertible, non-unitary matrix , a positive Hermitian , a map , a vector , and a covector . Without copying the formulas above, produce:
- the transformed components of all five objects;
- one invariant vector–covector pairing;
- one invariant quadratic form;
- the -adjoint of before and after the basis change; and
- one sentence naming the type of every index in the calculation.
The repair is complete when the types determine the transformations and both scalar checks agree in the two bases. If the arithmetic works but the map supplied by remains implicit, review the forms section and repeat with a nondiagonal . If a purported scalar changes, return to the vector–covector pairing and add one object at a time.
Then retry the mathematics diagnostic with fresh matrices, or continue to Readiness. Core QFT uses this capability immediately in Classical fields and the action principle.
References
Section titled “References”- Sheldon Axler, Linear Algebra Done Right, third edition, Springer, 2015, doi:10.1007/978-3-319-11080-6.
- Brian C. Hall, Quantum Theory for Mathematicians, Springer, 2013, doi:10.1007/978-1-4614-7116-5.
- Steven Roman, Advanced Linear Algebra, third edition, Springer, 2008, doi:10.1007/978-0-387-72831-5.