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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Finite matrix exponential series and its identities
Statement
For a finite real square matrix , define
This series converges absolutely and uniformly for in every bounded interval. It is differentiable with and . If finite square matrices and commute, then .
Facts & Assumptions
Given: A finite real square matrix , and, for the product identity, finite real square matrices with .
For matrices of positive sizes, the induced matrix -norm is a norm (so is absolutely homogeneous and satisfies the triangle inequality) and is submultiplicative: . (The matrix norm induced by a published vector p-norm, Induced matrix norms are compatible with matrix-vector multiplication, submultiplicative, and satisfy ||I|| = 1)
The scalar series converges absolutely for every real and defines . (The real exponential function and the number by a power series, The exponential series converges absolutely for every real argument)
Matrix multiplication is associative and unital and distributes over matrix addition; it is compatible with scalar multiplication, including all zero-sized shapes. (Matrix multiplication is associative, unital, distributive, and compatible with scalar multiplication)
Since is a monoid by [F3], its natural powers satisfy and . (Powers : natural exponents in a monoid and integer exponents in a group, with )
For , the induced matrix -norm is the maximum absolute column sum. In particular, for each entry. (The induced 1-norm is the maximum column sum and the induced infinity-norm is the maximum row sum)
Factorials satisfy and and are nonzero; their canonical real images, used for the scalar denominators below, are nonzero. (The factorial and the falling factorial , defined by recursion in , The real exponential function and the number by a power series)
Proof
If has size , the matrix space has one element, which is both and ; every partial sum in the definition is this same matrix. Thus for every , its derivative is the zero matrix, and the derivative identities, initial value, and commuting-product identity all hold because every product and sum is the unique empty matrix. Hence assume below that has size with .
Put . By induction using [F1] and [F4], for every . For , [F5] then bounds each entry of by . The scalar majorant converges by [F2], so each entry series converges absolutely and uniformly on ; its entrywise sums define a matrix . Each column has only entries, so the maximum-column-sum formula [F5] bounds the norm of a matrix tail by times the scalar majorant tail. Therefore the matrix series converges in induced -norm uniformly on . Since was arbitrary, it converges locally uniformly and defines .
Suppose commute. For each set . Induction from and [F3] gives for every . Distributing and using this commutation, the coefficient of is for , which equals by [F6]; for and the single endpoint coefficient has that same value by . Thus . Since , induction, the recursive power law [F4], and the nonzero factorial images in [F6] give for every .
Fix and choose . If , then ; for , the factorization bounds the norm of the th matrix-series difference quotient by , and the same bound holds for the th proposed derivative term . The sum of these bounds is by [F2]. For each finite partial sum, the factorization also shows that its difference quotient tends to the finite sum of the proposed derivative terms, using from [F6]. For each fixed nonzero , the full difference quotient is the limit of those of the finite partial sums by [step 1.2]; the two uniform tail bounds let us first choose a large partial sum and then take . Consequently is differentiable at and .
Apply the power bound and convergence argument of step 1.2 to and as well. Put and ; both scalar series converge by [F2]. The finite product expands by [F3] as the square sum over of , whose term norms are at most by [F1] and [F4]. Its difference from the triangular sum over has norm at most , which tends to zero; every omitted pair has or . By step 1.2, (matrix multiplication is continuous by [F1]); by step 1.3 the triangular sum is , which converges to . Therefore .
From [F3] and the recursive power definition [F4], induction gives for every . Hence the derivative series in step 2.1 is the limit of both and as . Submultiplicativity [F1] makes multiplication by fixed continuous, so these limits are and . At , all positive-degree terms vanish and the constant term is . Thus and .
Step 1.1 covers the zero-sized matrix case. For , steps 1.2 and 2.1 establish convergence and differentiation, step 3.1 gives the derivative identities and initial value, and steps 1.3 and 2.2 prove the commuting-product identity. Thus every claim in the Statement holds.
Depends on
- The vector space $M_{m \times n}(F) := F^{\,m \times n}$ of $m$ by $n$ matrices over a field, with entrywise operations
- Rectangular matrix multiplication and the identity matrix $I_n$, including zero-sized shapes
- The matrix norm induced by a published vector p-norm
- Induced matrix norms are compatible with matrix-vector multiplication, submultiplicative, and satisfy ||I|| = 1
- The induced 1-norm is the maximum column sum and the induced infinity-norm is the maximum row sum
- Matrix multiplication is associative, unital, distributive, and compatible with scalar multiplication
- Powers $g^{n}$: natural exponents in a monoid and integer exponents in a group, with $g^{0} = e$
- The principle of mathematical induction
- The factorial $n!$ and the falling factorial $n^{\underline{k}}$, defined by recursion in $\mathbb{N}$
- The real exponential function and the number $e$ by a power series
- The exponential series converges absolutely for every real argument
Used by
- Consistent-mass P1 diffusion can leave the nonnegative cone Counterexample
- Finite master equation and uniformization formula Theorem
- Finite-volume and periodic diffusion preserve intervals and weighted mass Theorem
- Mesh-uniform first-order Lie error for a smooth periodic reaction-diffusion solution Theorem
- No-flux rectangular TPFA Lie error by even reflection Theorem
- Positive graph-diffusion semigroup and Euler maps Theorem
- Uniformization truncation, mass loss, and arithmetic-error ledger Theorem
Dependency tree · two levels
54 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Ward Whitt, Continuous-Time Markov Chains, full lecture notes (2006) (standard reference, not scraped)
- Brian Munsky and Mustafa Khammash, The Finite State Projection Algorithm for the Solution of the Chemical Master Equation (standard reference, not scraped)