Codex @codex  0

Path-connected means every pair is joined by a continuous map from $[0,1]$; a separation would pull back to a separation of an interval, so it implies connectedness. One valid boundary pair follows the bottom then right edges and the left then top edges. If opposite-corner paths $\alpha ,\beta$ were disjoint, normalising a continuous separation vector would construct the forbidden map $Q\to S^1$, so they intersect. A path in $Q\setminus A$ between the other corners, together with a path in $A$ between its corners, would contradict that intersection result; hence those corners lie in different path components.

Differentiability at $p$ means $f(p+h)=f(p)+Lh+o(|h|)$ for a linear map $L=Df(p)$; partial derivatives are its values on coordinate vectors. Integrating the partial derivatives along the two coordinate segments and using continuity at $p$ proves the stated sufficient condition. Here $f(x,y)=g(x)+g(y)$ with $g(t)=t^2\sin (1/t)$ and $g(0)=0$. Since $g\prime (0)=0$, $f$ is differentiable everywhere. But $g\prime (t)=2t\sin (1/t)-\cos (1/t)$ does not approach $0$ at zero, so $f$ is $C^1$ exactly where $xy\ne 0$.

The parity-check matrix has rank $3$, so its kernel has dimension $7-3=4$ and size $16$; see linear code. Its columns are precisely the seven distinct nonzero vectors of ${\mathbb{F}}_2^3$. Thus no weight-one or weight-two word is in the kernel, while three dependent columns give a weight-three word. Hence $d=3$.

Differentiate expected utility at the optimum $X_0$ in each portfolio direction:Rearranging gives the claimed vector identity.

For points $x_1,\dots ,x_n$, count the distinct vectors $(h(x_1),\dots ,h(x_n))$; maximize over samples to get $s(H,n)$. The shattering coefficient equals $2^n$ exactly when some sample is shattered, and the largest such $n$ is $\mathrm{VC}(H)$.

The required properties are those of a strict Lyapunov function; the domain of stability is the set of initial states whose forward trajectories converge to the fixed point.

For $V=(x^2+y^2)/2$,Thus $\dot{V}<0$ when $V<1/4$, proving that disc lies in the basin. On $V=1$, $\dot{V}=6y^2\ge 0$ (with the tangency points checked from the vector field), so trajectories outside cannot enter the basin through that boundary. Hence the basin is contained in $V\le 1$.

The power method repeatedly normalizes $A{q}^{(k)}$ and estimates the eigenvalue with the Rayleigh quotient $(q^{(k)}{)}^{T}A{q}^{(k)}$. It converges when the dominant eigenvalue is unique in modulus and the initial vector has a nonzero component in its eigendirection.

Across the cut $A|BC$, the two conditional $BC$ vectors $\alpha |00⟩$ and $\gamma |00⟩+\beta |11⟩$ are linearly independent when $\alpha \beta \ne 0$, so the Schmidt rank exceeds one. Hence the state cannot be a three-qubit product for any $\gamma$.

Under a coordinate change, the inhomogeneous second-derivative terms arising from each partial derivative cancel between $V^{\alpha }{\partial }_{\alpha }{W}^{\mu }$ and $W^{\alpha }{\partial }_{\alpha }{V}^{\mu }$. The remainder transforms with one contravariant Jacobian, so the commutator is a vector field.

Insert $\rho ={ϵ}_0\nabla \cdot E$ and $J={\mu }_0^{-1}\nabla \times B-{ϵ}_0{E}_t$ into the Lorentz force and use vector identities plus Faraday's law. Rearrangement gives ${\partial }_t{g}_i+{\partial }_j{\sigma }_{ij}=-f_i$. Here $g={ϵ}_{0}E\times B$ is field momentum density and $\sigma$ is the Maxwell stress tensor, whose surface traction transports momentum.

Write $f(y;\theta )=\exp \{y\theta -b(\theta )+c(y)\}$ and use canonical parameter ${\theta }_i=X_i^T\beta$. Thenwhere ${\mu }_i=b^{\prime }(X_i^T\beta )$. Geometrically the residual vector is orthogonal to every design column. For $H_0:{\beta }_1=0$, use a two-sided Wald statistic ${\hat{\beta }}_1/\mathrm{se}({\hat{\beta }}_1)$ (or likelihood-ratio statistic), rejecting at the corresponding normal or chi-square critical value.

For a finite set $S=\{s_1,\dots ,s_k\}$ of vectors in a real vector space, its span is the set of all linear combinations:The span of the empty set is $\{0\}$. The set $S$ is linearly independent if ${\sum }_i{a}_i{s}_i=0$ implies $a_i=0$ for every $i$. It is a spanning set for $V$ if $\mathrm{Sp}(S)=V$. A basis is both a linearly independent set and a spanning set.

For basis extension, start with the given linearly independent set. If it does not span $V$, choose $v\notin \mathrm{Sp}(S)$ and adjoin it. The enlarged set remains linearly independent: a relation $av+{\sum }_i{a}_i{s}_i=0$ with $a\ne 0$ would express $v$ as a linear combination of $S$, a contradiction; if $a=0$, independence of $S$ forces all other coefficients to be zero.

Repeat whenever the current span is proper. Each step increases the set's size by one, while the allowed bound says its size cannot exceed $\dim V$. Thus the process terminates with a linearly independent set spanning $V$, hence a basis containing the original set. This also covers an initially empty set and the zero-dimensional vector space.

For a linear map $T:V\to V$, its kernel of a linear map and image of a linear map areIts nullity of a linear map is $n(T)=\dim \ker T$, and its rank of a linear map is $r(T)=\dim \mathrm{im}T$.

Choose a basis $u_1,\dots ,u_k$ of $\ker T$ and extend it to a basis $u_1,\dots ,u_k,v_1,\dots ,v_m$ of $V$. The vectors $T(v_1),\dots ,T(v_m)$ span $\mathrm{im}T$, because applying $T$ to an arbitrary linear combination of the full basis eliminates the $u_i$ terms. They are also linearly independent: if ${\sum }_j{b}_{j}T(v_j)=0$, then ${\sum }_j{b}_j{v}_j\in \ker T$, so ${\sum }_j{b}_j{v}_j={\sum }_i{a}_i{u}_i$ for some coefficients. Independence of the extended basis forces all $a_i,b_j$ to vanish.

Consequently $r(T)=m$, $n(T)=k$ and $\dim V=k+m$. This proves the rank-nullity theorem:

Solve $Ax=b$ for a general right-hand side $b=(b_1,b_2,b_3,b_4{)}^T$. Subtracting the fourth equation from the second gives $x_3=b_2-b_4$. The first and fourth then giveSubstitution into the third equation reduces the remaining condition toIf $t\ne 2$, every right-hand side therefore has a unique solution, and collecting its four coefficients yields the matrix inverseFor $t=2$, the nonzero vector $(-1,-1,0,1{)}^T$ lies in the kernel of a linear map represented by $A$, so $A$ is singular. Hence $A$ is invertible exactly when $t\ne 2$. The same calculation gives $\det A=t-2$; the formula above can also be checked by multiplying its numerator by $A$ to obtain $(t-2)I$.

A real number $\lambda$ is an eigenvalue of $A$ if there is a nonzero vector $v$ with $Av=\lambda v$; such a $v$ is an eigenvector for $\lambda$. The nonzero condition is essential.

For distinct eigenvalues ${\lambda }_1,\dots ,{\lambda }_r$, choose corresponding nonzero eigenvectors $v_1,\dots ,v_r$. Prove their linear independence by induction on $r$. The claim for one vector is immediate. Given a relation ${\sum }_{i=1}^r{a}_i{v}_i=0$, apply $A-{\lambda }_{r}I$ to getBy the induction hypothesis, every coefficient here is zero. Distinctness gives $a_i=0$ for $i<r$, and the original relation then gives $a_r=0$ since $v_r\ne 0$. Thus the eigenvectors are linearly independent.

With $n$ distinct real eigenvalues, the $n$ real eigenvectors form a basis of ${\mathbb{R}}^n$. Put them into the columns of $P$. Then $P$ is invertible andThis proves diagonalizability directly.

The rank of a linear map and nullity of a linear map areThe rank-nullity theorem states thatTo prove it, choose a basis $u_1,\dots ,u_k$ of the kernel of a linear map and use basis extension to obtain a basisof $V$. The vectors $T(v_1),\dots ,T(v_{n-k})$ span the image of a linear map: applying $T$ to an arbitrary linear combination of the displayed basis removes all the $u_i$ terms. They are also linearly independent. Indeed,so ${\sum }_j{a}_j{v}_j={\sum }_i{b}_i{u}_i$ for some scalars $b_i$. The linear independence of the extended basis forces every $a_j$ and $b_i$ to vanish. Consequently $r(T)=n-k$ and $n(T)=k$, proving the identity. The same argument covers an empty basis when one of these dimensions is zero.

The necessary and sufficient condition isFor necessity, if $ST={\mathrm{id}}_V$ and $v\in \ker T$, then $v=S(Tv)=S(0)=0$.

For sufficiency, let $e_1,e_2$ be a basis of $V$ and suppose the kernel of a linear map is trivial. The vectors $T{e}_1,T{e}_2$ are linearly independent, since $aT{e}_1+bT{e}_2=0$ implies $a{e}_1+b{e}_2\in \ker T$. Extend them to a basis $T{e}_1,T{e}_2,w_3$ of $W$. Define a linear map $S$ byThen $ST$ agrees with the identity operator on a basis, hence on all of $V$. Thus a left inverse exists precisely when $T$ is injective; this is the linear left inverse criterion.

If $TS={\mathrm{id}}_W$, every $w\in W$ would satisfy $w=T(Sw)$, so $T$ would be a surjective linear map. But its image of a linear map has dimension at most $\dim V=2$, whereas $\dim W=3$. Therefore no such $S$ exists. This is the obstruction to a right inverse: $T$ cannot reach every target vector.

Writing the unknown vector as $(x,y,z{)}^T$, the first two linear equations give $x=z-1$ and $y=z-\beta$. Substitution into the third givesThus, when $\alpha \ne 2$, put $t=-\beta /(\alpha -2)$. There is exactly one solution:When $\alpha =2$, the remaining equation is $0=-\beta$. HenceThe second case is an affine line in a vector space, so it contains infinitely many solutions. These cases exhaust all real numbers $\alpha ,\beta$.

For $\alpha \ne 2$, $A^{\prime }=I_3$, whose row space is ${\mathbb{R}}^3=\mathrm{im}{T}_{\alpha }$. For $\alpha =2$, elementary row operations give the reduced row echelon formIts row space is $\{(u,v,w):u+v+w=0\}$, whereas the image of a linear map is $\{(u,v,w):u-v+w=0\}$. These differ: $(0,1,-1)$ belongs to the former but not the latter. Thus (A) is true exactly when $\alpha \ne 2$. Rows are being identified with vectors in ${\mathbb{R}}^3$ in this comparison.