The Matrix Cookbook

Learn / The Matrix Cookbook

10 - Functions and Operators

Advanced formula selection, assumption checking, and exact application for Functions and Operators, book pages 58-63.

Learning path

0%

0 of 3 sections marked complete · about 25 minutes

Learning objectives

What you will be able to explain

  • Matrix functions and power series
  • Vec-Kronecker identities
  • Kronecker dimensions
  • Kronecker algebra audit
  • Vector norm formulas
  • Matrix norm meanings

Section 01

Matrix functions and power series to Kronecker dimensions

01

Matrix functions and power series

Noncommutativity blocks many scalar-looking identities.

Guided checkpoint

Let a scalar function have a convergent power series on the spectrum/domain of AA.

Source: Sec. 10.1, pp. 58-59

02

Vec-Kronecker identities

Vectorization stacks entries and turns two-sided multiplication into a Kronecker product.

Guided checkpoint

Match expression to its vectorized form.

Source: Sec. 10.2, pp. 59-61

03

Kronecker dimensions

Kronecker dimensions multiply: (24)×(35)(2\cdot4)\times(3\cdot5).

Guided checkpoint

If AA is 2×32\times3 and BB is 4×54\times5, how many rows does ABA\otimes B have?

Source: Sec. 10.2, pp. 59-61

Section 02

Kronecker algebra audit to Matrix norm meanings

01

Kronecker algebra audit

Kronecker products preserve many operations but are not literally commutative.

Guided checkpoint

Assume compatible sizes and inverses.

Source: Sec. 10.2, pp. 59-61

02

Vector norm formulas

These norms trade geometry, sparsity sensitivity, and maximum-coordinate control.

Guided checkpoint

Match norm to expression.

Source: Sec. 10.3, p. 61

03

Matrix norm meanings

Induced norms describe maximal vector amplification; Frobenius treats entries as one vector.

Guided checkpoint

Match norm to characterization.

Source: Sec. 10.4, pp. 61-62

Section 03

Rank inequalities to Operator-level discipline

01

Rank inequalities

Invertible transformations preserve linear dependence; sums and products obey subadditive/bottleneck bounds.

Guided checkpoint

Assume compatible matrices.

Source: Sec. 10.5, p. 62

02

Dirac delta sifting

The delta distribution evaluates the integrand at its support point.

Guided checkpoint

What does intf(x)delta(xa)dxint f(x)delta(x-a)dx equal when the integration domain contains aa?

Source: Sec. 10.6, pp. 62-63

03

Operator-level discipline

The cookbook's identities remain conditional on compatible dimensions and stated assumptions.

Guided checkpoint

Apply the final miscellaneous identities cautiously.

Source: Sec. 10.7, p. 63

Knowledge check

Turn understanding into recall.

The quiz now follows the same concepts in scored form. You can return to this lesson from the quiz whenever a gap appears.