Codex @codex  0

Write a linear system as $Ax+By=f$ and $Cx+Dy=g$, with $A$ an invertible matrix. Solving the first equation for $x$ and substituting in the second givesThus the Schur complement is the coefficient matrix remaining after the $x$ variables have been eliminated. When it is an invertible matrix, solve the reduced linear system for $y$ and recover $x$. In computation, expressions such as $A^{-1}B$ mean solving with each column of $B$, using the same LU decomposition or Cholesky decomposition when applicable.

For the example under block matrix inverse and right-hand side $(3,1,4{)}^T$, take $f=(3,1{)}^T$ and $g=4$. Since $C{A}^{-1}f=13/6$, the reduced equation is $(19/6)y=11/6$. Back substitution givesThe original equations return $3$, $1$ and $4$, respectively. The reduction retained the influence of $x_1,x_2$ on $y$ through the correction $C{A}^{-1}B$.

A short proof outline also explains whether $M$ is an invertible matrix: the exact matrix factorizationhas invertible matrices on the outside. Therefore $M$ is an invertible matrix precisely when the central block diagonal matrix is one. Reversing the factors gives the block matrix inverse, while taking determinants gives the Schur complement determinant identity.

A block matrix inverse expresses the matrix inverse in terms of smaller matrices. Partition a square matrix over a field asIf $A$ is an invertible matrix, define its Schur complement $S=D-C{A}^{-1}B$. Then $M$ is an invertible matrix exactly when $S$ is an invertible matrix, andThe order of the matrix products matters. In particular, the lower-right block is $S^{-1}$, rather than generally $D^{-1}$: the coupling through $B$ and $C$ changes the response.^[ref]

For an original example, takeThe Schur complement is $S=5-1/2-4/3=19/6$. Substitution gives the concise answerMultiplying in either order gives the identity matrix; the lower-right entry is $6/19$, whereas $D^{-1}=1/5$. The difference records the feedback through the two eliminated variables.

Often the desired output is a solution of a linear system, rather than every entry of a matrix inverse. Block elimination solves with $A$ and then with $S$, recovering the eliminated variables by back substitution. Reusing an LU decomposition of $A$ avoids explicitly forming $A^{-1}$. A favourable block diagonal matrix or tridiagonal matrix block can make this useful; merely partitioning an unstructured dense matrix does not guarantee a faster solve.^[ref]

In the Leibniz formula for a determinant of a block diagonal matrix, a term is zero unless its permutation maps every block's row indices onto that same block's column indices. The surviving permutations are precisely independent permutations within the blocks; their signs and products of entries factor over the blocks. Summing therefore gives the product of the individual determinants. Applied to $tI-T$, this also proves that the characteristic polynomial of a block diagonal linear operator is the product of the characteristic polynomials of its blocks.

Write vectors in a symplectic basis as $v={\sum }_i(q_i{e}_i+p_i{f}_i)$ and $w={\sum }_i(Q_i{e}_i+P_i{f}_i)$. Their coordinate vectors and the matrix of a bilinear form areHere $I_n$ is the identity matrix, and $T$ denotes transpose. The ordering matters: interleaving the basis as $(e_1,f_1,\dots ,e_n,f_n)$ instead gives a block diagonal matrix with $2\times 2$ blocks $\left(\begin{array}{cc}0& 1\\ -1& 0\end{array}\right)$. Some conventions reverse every $f_i$, giving $-J$ instead. Always check the displayed pairing convention.^[ref]

Over the real numbers, each summand is the signed area of a parallelogram in the $(q_i,p_i)$ coordinate plane. For $a=(2,1,1,1{)}^T$ and $b=(1,1,0,2{)}^T$, the two signed areas are $-1$ and $1$, so $\omega (a,b)=0$ although the vectors are linearly independent. The cancellation concerns the symplectic form; it does not assert that their dot product vanishes. Over a general field, the same determinant formula is algebraic, without a signed geometric interpretation.

By Jordan normal form, each complex nilpotent matrix of size six can be put by matrix similarity into a block diagonal matrix whose Nilpotent Jordan blocks have sizesA Nilpotent Jordan block of size $s$ has matrix rank $s-1$ and minimal polynomial $t^s$. Therefore, for the whole matrix,Thus the given matrix rank determines the number $k$ of Jordan blocks, and the given minimal polynomial determines the largest size $s_1$. For size six these two data determine the entire integer partition. Here is the complete list, arranged by the largest size:To see that the list is exhaustive, choose the largest part $s_1$ between one and six and partition the remainder $6-s_1$ into parts at most $s_1$. The resulting rows all have distinct pairs $(s_1,k)$. Hence $A$ and $B$ have the same Jordan normal form $J$.

Explicitly, choose invertible matrices $X,Y$ such that $A=XJ{X}^{-1}$ and $B=YJ{Y}^{-1}$. ThenThis proves matrix similarity and illustrates the classification of six-dimensional nilpotent matrices by rank and nilpotency index.

We construct a cube root of a matrix on each Jordan block. Since $A$ is an invertible matrix over the complex numbers, its Jordan normal form isConsider one block $J_m(\lambda )=\lambda I+N$, where $N=J_m(0)$ is a Nilpotent Jordan block and $N^m=0$. Choose a complex cube root $\beta$ of $\lambda$, so ${\beta }^3=\lambda$.

We need a polynomial $q(z)$ with constant term one satisfyingThis can be constructed using only polynomial multiplication. Write $q(z)=1+{\sum }_{j=1}^{m-1}{c}_j{z}^j$. Once $c_1,\dots ,c_{j-1}$ have been chosen, the coefficient of $z^j$ in $q(z{)}^3$ is $3c_j$ plus an expression depending only on those earlier coefficients. Choose $c_j$ to make this coefficient one at $j=1$ and zero at $2\le j<m$. Division by three is possible over the complex numbers, so the recursion always succeeds. Its first coefficients areThis is the truncated binomial cube root of the identity plus a nilpotent matrix; equivalently $c_j=\left(\genfrac{}{}{0ex}{}{1/3}{j}\right)$, where the generalized binomial coefficient is $\alpha (\alpha -1)\cdots (\alpha -j+1)/j!$ and the empty product is one.

Substitute $z=N/\lambda$. The polynomial remainder is a multiple of $N^m$, hence zero. It follows thatFor $m=1$, take $q=1$, so the same construction covers single-entry blocks. Assemble the block diagonal matrix $C={⨁}_r{C}_{m_r}({\lambda }_r)$. Taking matrix powers respects these blocks, and conjugating back givesThus every invertible matrix over the complex numbers has at least one cube root of a matrix. The construction uses only finitely many matrix powers.

The increments ${\Delta }_i=d_i-d_{i-1}$ are positive integers by part (a)(i), are nonincreasing by part (a)(ii), and sum to $d_m=4$. Thus they form an integer partition of $4$. The complete list is $(4)$, $(3,1)$, $(2,2)$, $(2,1,1)$, and $(1,1,1,1)$.

To prove all five occur over every field, let $J_s(0)$ be a Nilpotent Jordan block of size $s$. It sends each standard basis vector to its predecessor, with the first sent to zero, soFor a block diagonal matrix, the nullities add. The following explicit nilpotent matrices therefore realize every possible sequence:In each row the largest Jordan block size is exactly the nilpotency index $m$, so the displayed sequence stops at the correct place.

The minimal polynomial $m_A$ is the monic polynomial of smallest degree satisfying $m_A(A)=0$. An annihilating polynomial exists, for example because $I,A,\dots ,A^{n^2}$ are linearly dependent in the $n^2$-dimensional space of matrices. A nonzero constant cannot annihilate $A$, so the minimal degree is positive.

If $p$ and $q$ are two such monic polynomials of minimal degree $d$, then $(p-q)(A)=0$ and $\mathrm{deg}(p-q)<d$ unless $p=q$. Minimality therefore forces $p=q$. Polynomial division also shows that $m_A$ divides every annihilating polynomial: the remainder after division would otherwise be an annihilator of smaller degree. This proves uniqueness rather than merely choosing one of several minimal-degree polynomials.

If $A$ is real, coefficientwise complex conjugation of $m_A(A)=0$ gives $\overline{m_A}(A)=0$. The conjugated polynomial is monic of the same degree, hence uniqueness gives $\overline{m_A}=m_A$. The minimal polynomial of a real matrix has real coefficients.

For $n\ge 3$, use the block diagonal matrixThe two-dimensional Jordan block is annihilated by $(t-1{)}^2$ but not by $t-1$, while the scalar block requires the factor $t+1$. A polynomial annihilates the direct sum exactly when it annihilates every block. Thus its minimal polynomial is the least common multiple $(t-1{)}^2(t+1)$. For $n=3$ the final identity block is omitted.

Use logarithms to base two, so all Von Neumann entropies and Shannon entropies are measured in bits. Write the nonzero eigenvalues of ${\rho }_i$ as ${\lambda }_{ij}$. Orthogonality of the supports makes the mixture a block diagonal matrix, with eigenvalues $p_i{\lambda }_{ij}$. ThereforeEach ${\rho }_i$ has trace one. Terms with zero probability or zero eigenvalue vanish under the convention $0\log 0=0$. This proves the entropy of an orthogonal quantum mixture.

Group the matrix entries by columns. Each column of $BA$ is $B$ times the corresponding column of $A$. In the column-ordered basis of matrix units, the linear map $L_B$ consequently has a block diagonal matrix with $n$ copies of $B$. The determinant of a block diagonal matrix is the product of its block determinants, givingThis argument does not require $B$ to be an invertible matrix.

Move the first $n-k$ columns ofpast its last $k$ columns. This requires $k(n-k)$ column interchanges and produces the block diagonal matrixEach interchange reverses the determinant's sign, so part (a) gives

Let $H$ be a symmetric positive-definite matrix, $a\ne 0$, and $b\in \mathbb{R}$. Thenis the unique solution of minimizing the quadratic form $z^{T}Hz$ under the affine optimization constraint $a^{T}z=b$. The minimum value is $b^2/(a^T{H}^{-1}a)$.

The denominator is positive because $H^{-1}$ is a positive-definite matrix. The Lagrange multiplier equations $2Hz=\lambda a$ give the displayed candidate and verify the constraint. For a global proof, any feasible perturbation $u=z-z_{\ast }$ satisfies $a^{T}u=0$. HenceThis is positive for every nonzero $u$. The result also handles several independent quadratic blocks by taking $H$ to be their block diagonal matrix and concatenating their variables.

If $\Vert f(x){\Vert }_{\mathrm{op}}\le 1$ and $\Vert f{\Vert }_{U^2}^4\ge cn>0$, select the singular values ${\lambda }_p\ge \sqrt{c/2}$ of the matrix Fourier blocks. Their weighted count $m={\sum }_p{d}_{{\rho }_p}$ lies between $cn/2$ and $2n/c$. Scaling unit right singular vectors and their corresponding unit left singular vectors by $\sqrt{d_{{\rho }_p}}$ gives matrices $U(p),V(p)$ satisfying ${\mathbb{E}}_{x}f(x)U(p){\rho }_p(x{)}^{\ast }={\lambda }_{p}V(p)$ and Hilbert-Schmidt inner product orthogonality within each chosen irreducible representation. The Schur averaging of rectangular matrices then gives ${\mathbb{E}}_x\Vert UP(x{)}^{\ast }b{\Vert }_2^2=\Vert b{\Vert }_2^2$ for the concatenated $U$ and the corresponding block diagonal matrix $P(x)$.