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LemmaStatement: Literature-sourcedProof: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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Finite matrix exponential series and its identities

Statement

For a finite real square matrix A, define

etA:=∑k=0∞tkAkk!.

This series converges absolutely and uniformly for t in every bounded interval. It is differentiable with ddtetA=AetA=etAA and e0A=I. If finite square matrices A and B commute, then eAeB=eA+B.

Facts & Assumptions

Given: A finite real square matrix A, and, for the product identity, finite real square matrices A,B with AB=BA.

[F1]

For matrices of positive sizes, the induced matrix 1-norm is a norm (so is absolutely homogeneous and satisfies the triangle inequality) and is submultiplicative: ∥CD∥1≤∥C∥1∥D∥1. (The matrix norm induced by a published vector p-norm, Induced matrix norms are compatible with matrix-vector multiplication, submultiplicative, and satisfy ||I|| = 1)

[F2]

The scalar series ∑k≥0xk/k! converges absolutely for every real x and defines ex. (The real exponential function and the number e by a power series, The exponential series converges absolutely for every real argument)

[F3]

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)

[F4]

Since Mn(R) is a monoid by [F3], its natural powers satisfy A0=In and Ak+1=AkA. (Powers gn: natural exponents in a monoid and integer exponents in a group, with g0=e)

[F5]

For n≥1, the induced matrix 1-norm is the maximum absolute column sum. In particular, ∣Cij∣≤∥C∥1 for each entry. (The induced 1-norm is the maximum column sum and the induced infinity-norm is the maximum row sum)

[F6]

Factorials satisfy 0!=1 and (k+1)!=(k+1)k! and are nonzero; their canonical real images, used for the scalar denominators below, are nonzero. (The factorial n! and the falling factorial nk‾, defined by recursion in N, The real exponential function and the number e by a power series)

Proof

technique · direct
1.1givenF3F4algebra

If A has size 0×0, the matrix space has one element, which is both 0 and I0; every partial sum in the definition is this same matrix. Thus etA=I0 for every t, 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 A has size n×n with n≥1.

1.2givenF1F2F4F5inductionalgebra

Put M=∥A∥1. By induction using [F1] and [F4], ∥Ak∥1≤Mk for every k≥0. For ∣t∣≤T, [F5] then bounds each entry of tkAk/k! by (TM)k/k!. The scalar majorant converges by [F2], so each entry series converges absolutely and uniformly on [−T,T]; its entrywise sums define a matrix E(t). Each column has only n entries, so the maximum-column-sum formula [F5] bounds the norm of a matrix tail by n times the scalar majorant tail. Therefore the matrix series converges in induced 1-norm uniformly on [−T,T]. Since T was arbitrary, it converges locally uniformly and defines etA=E(t).

1.3givenF3F4F6inductionalgebra

Suppose A,B∈Mn(R) commute. For each r≥0 set Dr:=∑m=0rAmBr−m/(m!(r−m)!). Induction from AB=BA and [F3] gives BsA=ABs for every s≥0. Distributing Dr(A+B) and using this commutation, the coefficient of AjBr+1−j is 1/(j!(r−j)!)+1/((j−1)!(r+1−j)!) for 1≤j≤r, which equals (r+1)/(j!(r+1−j)!) by [F6]; for j=0 and j=r+1 the single endpoint coefficient has that same value by (r+1)!=(r+1)r!. Thus Dr(A+B)=(r+1)Dr+1. Since D0=In, induction, the recursive power law [F4], and the nonzero factorial images in [F6] give Dr=(A+B)r/r! for every r.

2.1givenF1F2F4F6step 1.2algebra

Fix c∈R and choose R>∣c∣. If ∣h∣<R−∣c∣, then ∣c∣,∣c+h∣<R; for k≥1, the factorization (c+h)k−ck=h∑j=0k−1(c+h)k−1−jcj bounds the norm of the kth matrix-series difference quotient by MkRk−1/(k−1)!, and the same bound holds for the kth proposed derivative term ck−1Ak/(k−1)!. The sum of these bounds is M∑j≥0(MR)j/j!<∞ 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 (k+1)!=(k+1)k! from [F6]. For each fixed nonzero h, 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 h→0. Consequently E is differentiable at c and E′(c)=∑k≥1ck−1Ak/(k−1)!.

2.2givenstep 1.2step 1.3F1F2F3F4algebra

Apply the power bound and convergence argument of step 1.2 to B and A+B as well. Put am=∥A∥1m/m! and bn=∥B∥1n/n!; both scalar series converge by [F2]. The finite product EN(A)EN(B) expands by [F3] as the square sum over 0≤m,n≤N of AmBn/(m!n!), whose term norms are at most ambn by [F1] and [F4]. Its difference from the triangular sum over m+n≤N has norm at most (∑m>N/2am)(∑nbn)+(∑mam)(∑n>N/2bn), which tends to zero; every omitted pair has m>N/2 or n>N/2. By step 1.2, EN(A)EN(B)→eAeB (matrix multiplication is continuous by [F1]); by step 1.3 the triangular sum is ∑r=0N(A+B)r/r!, which converges to eA+B. Therefore eAeB=eA+B.

3.1step 1.2step 2.1F1F3F4inductionalgebra

From [F3] and the recursive power definition [F4], induction gives Ak=AAk−1=Ak−1A for every k≥1. Hence the derivative series in step 2.1 is the limit of both A∑j=0NcjAj/j! and (∑j=0NcjAj/j!)A as N→∞. Submultiplicativity [F1] makes multiplication by fixed A continuous, so these limits are AE(c) and E(c)A. At c=0, all positive-degree terms vanish and the constant term is A0=In. Thus E′(c)=AE(c)=E(c)A and E(0)=In.

4.1step 1.1step 1.2step 2.1step 3.1step 1.3step 2.2∎

Step 1.1 covers the zero-sized matrix case. For n≥1, 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.

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Sources