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The number of consecutive true entries ending at a specified column of one row of a Boolean matrix. It is zero at a false entry and otherwise one plus the previous column's row run length, taking the value before column zero to be zero.

The row space of a matrix is the linear span of its rows. It is the column space of the matrix transpose.

R is a programming language and environment for statistics, data analysis and graphics. For matrix algorithms, drop = FALSE preserves dimensions when extracting submatrices. To choose an element uniformly from a possibly singleton numeric vector indices, use indices[sample.int(length(indices), 1L)]: sample(indices, 1L) has a special interpretation when its first argument is a single positive number. Official documentation: Extraction and the drop argument and Sampling, including the singleton numeric argument rule.

At one matrix entry $a$, let $\rho ,\mu ,\nu ,\lambda$ be the shapes of the northwest, northeast, southwest and southeast matrix prefixes under the RSK correspondence. The displayed row-length recurrence expresses merged insertion paths: the new entry supplies the first-row carry, and the overlap of the two incoming row extensions beyond the northwest shape is bumped into the next row. Zero boundary shapes determine the whole diagram. Transposing the matrix exchanges $\mu ,\nu$, leaving the rule invariant.

For differences $D_i$ between corresponding stages and differences $F_i$ between their vector fields, a Runge--Kutta step satisfiesTo derive the identity, expand the squared norm of $d_{n+1}=d_n+h{\sum }_i{b}_i{F}_i$ and substitute $d_n=D_i-h{\sum }_j{a}_{ij}{F}_j$ in the linear term. Symmetrizing the double sum gives the displayed coefficients $m_{ij}$. For a dissipative vector field, the first sum is nonpositive when $b_i\ge 0$, and the second sum is nonnegative when the matrix $(m_{ij})$ is a positive semidefinite matrix. Thus algebraic stability implies B-stability.

A Runge-Kutta method advances an initial value problem $y^{\prime }=f(t,y)$, $y(t_0)=y_0$, by sampling its vector field at several trial states within one time step. These samples are Runge-Kutta stages; a weighted combination of their slopes produces the next state. The construction improves on the single tangent line used by the Euler method while requiring evaluations of $f$, rather than explicit higher derivatives of the solution. It applies to scalar equations and systems of ordinary differential equations alike. The coefficient choices distinguish methods such as Heun's method, the classical fourth-order Runge-Kutta method and implicit Runge-Kutta methods.

For $s$ stages, write $A=(a_{ij})$ for the stage matrix, $c_i$ for relative time nodes and $b_i$ for output weights. A time step of length $h$ computesHere $k_i$ is a slope, with units of state per time; $h{k}_i$ is a state increment. Another common convention writes $K_i=h{k}_i$, so its stage states are $Y_i=y_n+{\sum }_j{a}_{ij}{K}_j$ and its output is $y_n+{\sum }_i{b}_i{K}_i$. These are equivalent conventions: including $h$ both inside $K_i$ and again in the output would count the time step twice. The textbook formulation uses the increment convention.^[ref]

Usually the Butcher tableau obeys $c_i={\sum }_j{a}_{ij}$, called internal consistency. Stage $i$ then combines slopes over an effective time $c_{i}h$. The second-order conditions with independent Runge-Kutta abscissae explain what changes when time nodes are specified independently. A Runge-Kutta stage is generally an auxiliary approximation, not a separate accepted solution value at its node. Several stages can even have the same time node but different state arguments, as in the classical fourth-order Runge-Kutta method.

The useful idea is to approximate the integral $y(t_n+h)-y(t_n)={\int }_{t_n}^{t_n+h}f(t,y(t))dt$ without knowing the intermediate exact trajectory. Trial states provide places to sample $f$, and Butcher order conditions make the resulting combination agree with the exact Taylor expansion. More stages alone do not guarantee a higher order of a numerical method: their coefficients must satisfy the appropriate conditions. Embedded Runge-Kutta pairs reuse these same samples for a second output and a practical local error estimator.

For a scalar constant-coefficient grid update, Fourier transformation turns $S$ into multiplication by its symbol $G$. Parseval identity proves the displayed norm equality; necessity is obtained by concentrating Fourier data near a maximizing frequency. Contractivity for every step is equivalent to $|G|\le 1$. Mesh-uniform fixed-time stability, on $nk\le T$, permits $\sup |G|\le 1+Ck$: powers are bounded by $e^{CT}$. Conversely a uniform bound at $n=\lfloor T/k\rfloor$ gives this $1+O(k)$ restriction as $k\to 0$. For matrix symbols, use the existing uniform power bound for matrix Fourier symbols rather than only eigenvalue moduli.

A linear transformation fixing every one-dimensional subspace is scalar. Fixing the coordinate lines makes it diagonal, and fixing the lines spanned by $e_i+e_j$ makes all diagonal entries equal. Within determinant-one matrices the scalar satisfies ${\lambda }^n=1$. Conversely scalars fix all projective points. Commutation with all elementary transvections shows these scalars are exactly the center of the special linear group, so its projective quotient acts faithfully.

A scalar matrix is a scalar multiple of the identity matrix, equivalently a diagonal matrix with all diagonal entries equal. It commutes with every square matrix of the same size. In $G{L}_n(F)$, the nonzero scalar matrices form the central subgroup removed to define the projective linear group.

A two-by-two matrix $M$ with zero trace satisfies $M^2=-(\det M)I$ by the Cayley-Hamilton theorem. Over the complex numbers, either $\det M\ne 0$ and its two distinct eigenvalues make it diagonalizable, or $M^2=0$. A matrix commutator has zero trace, so this alternative applies to it. Diagonalization over the real numbers need not follow when $-\det M<0$.

With query and key dimension $d_k$, form the score matrix $Q{K}^T/\sqrt{d_k}$, apply the softmax function row by row, and multiply by the values $V$. If query and key coordinates have independent unit variance, their dot product has variance $d_k$; division by $\sqrt{d_k}$ keeps that score scale of order one. Optional additive masks exclude forbidden keys before the softmax function.

For a block matrix $M=\left(\begin{array}{cc}A& B\\ C& D\end{array}\right)$ with $A$ an invertible matrix, the Schur complement of $A$ in $M$ is $S=D-C{A}^{-1}B$. It is the effective coefficient matrix for the remaining variables after block elimination. The definition requires invertibility of $A$, but not of $S$ or $M$. Its size is that of $D$, even when $B$ and $C$ are rectangular matrices.

In the original example under block matrix inverse, $D=5$ becomes $S=19/6$ because changing the remaining variable also changes the two variables solved through $A$. This is what makes a Schur complement more informative than simply deleting rows and columns. It retains their mediated effect, and its matrix inverse is the corresponding block of $M^{-1}$ when the block matrix inverse exists.

The same elimination appears when minimizing a quadratic form. The Schur complement as a minimized quadratic form gives a geometric example; the Schur complement determinant identity gives an exact test for an invertible matrix.

When $A$ is an invertible matrix, the factorization in block elimination has outer triangular factors with determinant one. Hence $\det M=\det A\det S$, without a symmetry assumption. For the original example under block matrix inverse, this gives $\det M=(2\cdot 3)(19/6)=19$. An invertible block $A$ therefore reduces the question of whether the full matrix is an invertible matrix to the smaller Schur complement; a singular $S$ makes $M$ a singular matrix.

The derivative of the determinant at an invertible matrix is $D{\det }_A(K)=\det A\mathrm{tr}(A^{-1}K)$. At $A=I+H$, use $\det (I+H)=1+\mathrm{tr}H+O(\Vert H{\Vert }^2)$ and $(I+H{)}^{-1}=I-H+O(\Vert H{\Vert }^2)$. Differentiating the first derivative gives the displayed symmetric bilinear map. More generally $D^2{\det }_A(H,K)=\det A[\mathrm{tr}(A^{-1}H)\mathrm{tr}(A^{-1}K)-\mathrm{tr}(A^{-1}H{A}^{-1}K)]$.

For a fixed mean-zero variance-one real entry law, centered truncation of a Wigner matrix satisfies $\mathbb{E}[N^{-1}\mathrm{Tr}(X-\hat{X}{)}^2]\le t(C)$, where $t(C)=\mathbb{E}[Y^2{1}_{|Y|\ge C}]\to 0$. The spectral Lipschitz bound from Frobenius distance and Markov inequality give probability at most $t(C)/{\epsilon }^2$ for a spectral test-function discrepancy exceeding $\epsilon$. Thus $t(C)<{\epsilon }^3$ works uniformly in matrix size. A varying entry-law family requires uniform second-moment tail decay instead.

Let $f$ be a twice continuously differentiable function on an open domain stable under positive scaling, with $f(\lambda x)={\lambda }^{n}f(x)$. Apply the chain rule twice to the scalar function $\lambda \mapsto f(\lambda x)$ and set $\lambda =1$. The result isIn two variables, this reads $x^2{f}_{xx}+2xy{f}_{xy}+y^2{f}_{yy}=n(n-1)f$. In matrix notation it is $x^T({\nabla }^{2}f)x=n(n-1)f$, where ${\nabla }^{2}f$ is the Hessian matrix. Equivalently, the radial differential operator $D={\sum }_i{x}_i{\partial }_i$ obeys $D^{2}f=Df+{\sum }_{i,j}{x}_i{x}_j{\partial }_i{\partial }_{j}f$; substituting $Df=nf$ explains the factor $n(n-1)$.

For the outward unit normal on a three-dimensional sphere, the off-diagonal integrals vanish by reflection symmetry. The diagonal integrals are equal by rotational symmetry, and their sum is ${\int }_{S^2}|\mathbf{n}{|}^{2}d\Omega =4\pi$. This proves the displayed matrix identity, where ${\delta }_{ij}$ is the Kronecker delta. Contracting it with a constant vector gives ${\int }_{S^2}(\mathbf{U}\cdot \mathbf{n})\mathbf{n}d\Omega =(4\pi /3)\mathbf{U}$, useful for integrating far-field stress forces.

At an unramified irregular singular point with distinct leading eigenvalues, a sectorial fundamental solution is a fundamental matrix whose entries are holomorphic functions asymptotic to a prescribed formal matrix on a sector of the logarithm cover. For competing cubic exponentials, the usual construction uses six overlapping sectors with basic angle $\pi /3$. A formal power series need not converge, and two sectorial realizations may differ by exponentially small terms across a sector boundary.

In the type-I regime $M_L\simeq 0$, $M_R\gg m_D$, the light mass is $m_D^2/M_R$. Specifying only the right-handed scale does not fix a numerical light mass. A choice of Dirac mass is an extra assumption, not a result of matrix diagonalization.

Cut a knot exterior along a Seifert surface and stack copies to form its infinite cyclic cover. Linking-dual generators for the homology of the cut-open piece give the positive and negative inclusion matrices $V$ and $V^T$ in row convention. The gluing relations therefore have matrix $tV-V^T$. The H0 gluing map is multiplication by $t-1$ and is injective, so these relations present the entire Alexander module of a knot. Its order is the determinant, up to a Laurent unit.