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Take $z$ off the real axis, or more generally require both $X-zI$ and $X^{(i)}-zI$ to be invertible. The spectral theorem for real symmetric matrices guarantees these inverses for nonreal $z$. Move coordinate $i$ to the first position by a simultaneous row and column permutation. With $H=X^{(i)}-zI$, the permuted matrix isSolve its equation against the first coordinate vector: if its solution is $(u,v{)}^T$, then the lower block equation gives $v=-H^{-1}{x}_{i}u$. Substitution into the first equation gives $(X_{ii}-z-x_i^T{H}^{-1}{x}_i)u=1$. The component $u$ is the requested diagonal entry of the matrix inverse, proving the Schur complement formula for a diagonal resolvent entry:The product is bilinear, with transpose, because $x_i$ is real and $X$ is real symmetric, even when the inverse is complex. In a complex Hermitian matrix version the row is $x_i^{\ast }$ instead. The final $x_i$ belongs inside the denominator, as in the original PDF; the converted TeX misplaces it outside the fraction.

The inverse hypotheses matter if $z$ is real: $X=\left(\begin{array}{cc}0& 1\\ 1& 0\end{array}\right)$ is invertible at $z=0$, while its one-by-one principal minor is not. Thus existence of the full inverse alone is not sufficient to use this formula.

Full column matrix rank makes $X^{T}X$ a positive-definite matrix, and its matrix inverse is symmetric. Thus the hat matrix satisfiesAlso $PX=X$ and the image of $P$ is contained in the column space of $X$. These identities show that $P$ is the orthogonal projection matrix onto the column space of $X$.

Write $\sigma =\mathrm{sgn}(x_S)$ and $G=A_S^{\ast }{A}_S$. The injectivity of $A_S$ makes this Gram matrix a positive-definite matrix, since $u^{\ast }Gu=\Vert A_{S}u{\Vert }_2^2>0$ for $u\ne 0$. Thus its matrix inverse exists. Construct the least-norm dual certificateOn the active coordinates, $A_S^{\ast }h=G{G}^{-1}\sigma =\sigma$. For $l\notin S$, symmetry of the real Gram matrix and its matrix inverse givesCondition (iii) makes the absolute value of this coordinate strictly less than one. Consequently $h$ is a strict dual certificate for basis pursuit, and part (c) applies. If $S$ is empty, take $h=0$; the zero vector uniquely minimizes the L1 norm on its feasible set. Condition (iii) supplies an explicit certificate and therefore unique recovery. There is no claim that this particular least-norm dual certificate is necessary: other valid certificates may exist when this one fails.

There are two closely related objects to distinguish. Inverting the quadratic Proca action gives the usual covariant Proca propagator. After integration by parts, its kernel isThe matrix inverse with the Feynman i-epsilon prescription givesMultiplication by $K^{ab}$ gives $i{\delta }_b^a$ in the distributional limit. The numerator agrees with the polarization sum for a massive vector boson at the poles, but is not a transverse linear projection at arbitrary four-momentum.

For the literal canonical time-ordered product of $A_a$ and $A_b$, the nondynamical component $A_0$ produces the Proca time-ordering contact term. In the chosen time coordinate the full answer isTo see the local term directly, the three physical polarization vectors give ${\sum }_{\lambda }{ϵ}_{\lambda 0}^2=|\mathbf{q}{|}^2/m^2$. The canonical $00$ two-point correlation function therefore has Fourier transformBy contrast, ${\eta }_{00}+q_0^2/m^2=|\mathbf{q}{|}^2/m^2-(q^2+m^2)/m^2$, so the covariant expression contains an additional $i/m^2$. The mixed and spatial components have no additional contact term. ThusIf $T$ is used to mean covariant time ordering, commonly denoted $T^{\ast }$, the conventional answer is instead just ${\Delta }^{\mathrm{cov}}$. Both conventions have the same propagating poles and agree away from coincidence; explicitly separating them respects the printed definition as an ordinary time-ordered product.

In Einstein notation, $(\mathbf{n}\cdot \mathbf{x})n_i=n_i{n}_j{x}_j$, so the matrix and, for $\alpha \ne 0$, its matrix inverse are

If $M_1^{T}J{M}_1=J$ and $M_2^{T}J{M}_2=J$, then matrix multiplication and the transpose rule giveThus the product also satisfies the equation.

The equation and part (i) imply that $M_1$ is invertible. Multiplying
$M_1^{T}J{M}_1=J$ on the left by $(M_1^{-1}{)}^T$ and on the right by $M_1^{-1}$ givesHence the set of solutions is closed under products and matrix inverses.

Let $\mathbf{e}$ be the downward vertical unit vector and let the rod's weight after buoyancy be $W\mathbf{e}$. Force balance givesBecauseits matrix inverse isUsing $\mathbf{e}\cdot \mathbf{t}=\cos \theta$ givesThereforeandThe required angle is consequentlyIf $c_1=c_2$, then $\cos \alpha =1$, so the rod falls vertically. For an anisotropic rod with $c_2>c_1$, the component parallel to its long axis encounters less drag, and the settling direction is generally deflected toward that axis.

For the active set $A_k$, maintainThe initial $n\times n$ matrix inverse costs $O(n^3)$. At step $k$, compute $v_k=M_k^{-1}Y$ in $O(n^2)$ operations and all active ridge coefficients $X_{A_k}^T{v}_k$ in $O(|A_k|n)$ operations; their smallest absolute value determines $j_k$.

After deleting $j_k$,Part b ensures that the denominator in the Sherman–Morrison formula is positive, and the rank-one downdatecosts $O(n^2)$. Summing over the $p$ steps givesSince $p\ge n$, both earlier terms are bounded by $O(p^{2}n)$, proving the claimed computational complexity within computational complexity theory.

Since $M^2=nM$, direct multiplication gives the matrix inverseprovided $1+np\ne 0$, as required by invertibility of the K-matrix. For $q=e_J$,The attached gauge-flux vector is thereforeRelative to the $K=I_n$ electron in part a, every one of the $n$ gauge fields consequently carries the additional flux