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Choose zero energy in the unstrained state. The required strain energy density isIts variation is $\delta W=(\lambda \mathrm{tr}(e)I+2\mu e):\delta e$, giving the prescribed stress as the tensor gradient. This variational notation handles the symmetric off-diagonal components without a spurious factor of two.

Decompose $e=e^{\mathrm{dev}}+(\mathrm{tr}e)I/3$ into orthogonal deviatoric and spherical parts. ThenThe two squares vanish together only for $e=0$. Thus $\mu >0$ and $3\lambda +2\mu >0$ are sufficient for positive definiteness. They are necessary by testing a nonzero trace-free strain and a pure dilation separately. Equivalently both shear modulus $\mu$ and bulk modulus $K=\lambda +2\mu /3$ must be positive.

Apply energy uniqueness for a linear elastic traction problem. Subtract two solutions with the same loading, writing their displacement difference as $\mathbf{v}$. Linearity makes its body force and boundary traction zero. The preceding energy identity therefore yields ${\int }_{B}2W(e(\mathbf{v}))dV=0$. Positive definiteness and continuity imply $e(\mathbf{v})=0$ everywhere. By the permitted zero-strain characterization,Thus the solution is unique modulo infinitesimal rigid translations and rotations, on each connected body. Conversely adding such a displacement changes neither strain nor stress, so these are exactly the unconstrained modes. This is conditional uniqueness; existence of a pure-traction solution additionally requires balanced resultant force and torque.

If $R$ and $S$ both satisfy the defining identity of an adjoint operator, thenfor every $u,w\in V$. Choosing $u=(R-S)w$ gives $\Vert (R-S)w{\Vert }^2=0$. The positive definiteness of the inner product forces $(R-S)w=0$ for every $w$, proving

Let $w=Tv$. If $T^{2}v=0$, then $Tw=0$, and part (i) gives $\Vert T^{\ast }w\Vert =\Vert Tw\Vert =0$. Thus $T^{\ast }w=0$ andThe positive definiteness of the inner product gives $w=0$. Since the reverse kernel inclusion always holds, we obtainThis is kernel stabilization for a normal operator.

With conjugation in its first slot, a complex inner product has conjugate symmetry, linearity in its second slot, and positive definiteness. For the proposed Hermitian inner product, direct expansion givesThe last expression vanishes exactly for the zero vector. Conjugate symmetry and linearity also give antilinearity in the first slot: $⟨\alpha z+\beta w,y⟩=\bar{\alpha }⟨z,y⟩+\bar{\beta }⟨w,y⟩$. These verify all three axioms and the associated first-slot consequence.

The risk-free weight is $1-1^{t}w$, so a zero risk-free weight requires $1^{t}w=1$. Define $H=1^t{\Sigma }^{-1}d$. If $H\ne 0$, this condition gives $\lambda =1/H$ andThese are the requested tangent-portfolio mean and variance under the usual condition $H>0$. Then $\lambda >0$, the normalized portfolio lies on the upper efficient ray, and its Sharpe ratio is $\sqrt{A}$.

There is a real qualification if the data are allowed to be arbitrary. If $H=0$, the efficient risky direction has zero total risky weight, and no finite multiple makes its risky weights sum to one. If $H<0$, the formal normalized portfolio above has negative excess mean and lies on the lower, dominated branch; it is not an efficient tangent portfolio as described in the question. The sign of the normalized tangency portfolio must therefore be checked, rather than inferred from positive definiteness of $\Sigma$ alone.

Associate to $Q$ its symmetric bilinear form by the polarization identity,We first construct a diagonal basis by induction on the dimension. A zero quadratic form is already diagonal. Otherwise choose $v$ with $Q(v)\ne 0$. Every $x$ has the decompositionwhose second term lies in $W=\{w:B(w,v)=0\}$. Also $\mathbb{R}v\cap W=\{0\}$, so $V=\mathbb{R}v\oplus W$ and $\dim W=n-1$. Apply the induction hypothesis to the restricted quadratic form on $W$. The cross terms with $v$ vanish, giving a diagonal basis on $V$. Rescale each vector with diagonal coefficient $a\ne 0$ by $1/\sqrt{|a|}$, and reorder positive, negative and zero coefficients. This yieldsNo nondegeneracy assumption was used.

For uniqueness, let $V_{+}$ be the positive coordinate subspace and $V_{\le 0}$ the span of all negative and zero coordinate vectors. Their dimensions are $p$ and $n-p$. Any subspace $W$ on which $Q$ has positive definiteness intersects $V_{\le 0}$ only in zero. The dimension formula for a sum of subspaces therefore gives $\dim W\le p$. Conversely $V_{+}$ itself has dimension $p$ and positive restriction. Thus $p$ is the largest possible dimension of a positive subspace, an intrinsic description independent of the basis. Applying the same argument to $-Q$ shows that $q$ is the largest dimension of a negative subspace. This proves Sylvester's law of inertia, including uniqueness of the zero count $n-p-q$.

The extremal subspaces themselves need not be unique. For $Q(x,y)=x^2-y^2$, the coordinate axes give one positive and one negative line. For any $t\ne 0$, putThen $Q(u)=1$, $Q(v)=-1$, $B(u,v)=0$, and the determinant of the change of basis is one. The distinct lines $\mathbb{R}u$ and $\mathbb{R}v$ give another positive/negative decomposition, with the same intrinsic counts $p=q=1$.

In weight one, the Hodge numbers are $h^{1,0}=h^{0,1}=r$, and the Hodge filtration is determined by the $r$-plane $H=F^1$. The bilinear orthogonality condition is $\psi (H,H)=0$. An isotropic $r$-plane in a symplectic $2r$-space is a Lagrangian subspace, soThe ordinary Grassmannian has dimension $r^2$. Restriction of the alternating form to an $r$-plane imposes $r(r-1)/2$ independent conditions. Equivalently, near a fixed Lagrangian plane, transverse Lagrangians are graphs of symmetric matrices, with $r(r+1)/2$ independent entries. Therefore

Write ${\mathbb{C}}^{2r}={\mathbb{C}}^r\oplus {\mathbb{C}}^r$ in the block order of the given matrix. On $D$, positivity is $i\psi (v,\bar{v})>0$ for every nonzero $v\in H$. The plane $H$ cannot contain $(x,0)\ne 0$, because that vector has $\psi (v,\bar{v})=0$. Projection onto the second coordinate factor is therefore injective on $H$ and, by equal dimensions, an isomorphism. There is a unique matrix $Z$ such thatFor $v=(Zu,u)$ and $v^{\prime }=(Zw,w)$, direct evaluation givesThus isotropy is exactly $Z=Z^{\mathsf{T}}$. For such $Z$,Hence positivity is precisely positive definiteness of $\mathrm{Im}Z$. Conversely, symmetry and this inequality define a positive Lagrangian plane; it is opposed to its conjugate because $(Z-\bar{Z})u=0$ forces $u=0$. We have explicitly identifiedthe Siegel upper half-space. The choice of the graph $(Zu,u)$, rather than $(u,Zu)$, is what matches the sign of the given symplectic form.

Let an integral element of the symplectic group have block matrixActing on graph vectors givesThe image remains a positive Lagrangian plane, so its second projection is invertible. Thus $CZ+D_0$ is invertible and the induced action isThe symplectic identities give symmetry of this matrix, and preservation of $i\psi (v,\bar{v})$ givesThis is a Hermitian congruence by an invertible matrix, so it proves preservation of the positive domain as well as describing the action. These are coordinates on marked Hodge structures; passing to the integral-group quotient forgets the symplectic marking.

Put $D_1=X_1-Y_1$, $D_2=X_2-Y_2$ andThe difference vector is $D=BZ$. Its mean is zero because the two sons have the same mean vector. Its covariance matrix isFor example, $\mathrm{Cov}(D_1,D_2)=b-c-c+b=2(b-c)$. ThusThe positive definiteness assumption supplies $a-b>0$ and $a+b-2c>0$: these are eigenvalues of the original matrix on within-son contrast and between-son difference directions. In particular the resulting difference covariance is positive definite.

At fixed particle numbers, the perturbations satisfy $\int \delta n_i{d}^{3}x=0$. Expanding the uniform interaction energy, its linear term is $\int ({\mu }_1\delta n_1+{\mu }_2\delta n_2)d^{3}x=0$, and its exact quadratic change in this contact-interaction approximation isCompleting the square givesHence the miscibility criterion for a two-component condensate isThese are precisely strict positive definiteness of the interaction matrix, and make every nonzero admissible long-wavelength density perturbation cost energy. With both equilibrium densities positive, a negative eigenvalue would instead allow a small sinusoidal perturbation along its eigenvector, with zero mean and negative energy change. The omitted positive gradient term cannot cure arbitrarily long wavelengths in an unbounded system.

If cross-repulsion exceeds the geometric mean of the self-repulsions, the unstable direction favors separation of species; sufficiently strong cross-attraction favors a joint density increase and collapse. Equality gives a zero-cost direction at this order, so it is marginal rather than strictly stable. Constraints near an absent component or finite-system wavelength restrictions require a separate boundary analysis.

The prescribed form is a sum of products of real polynomial evaluations. It is a symmetric bilinear form, since each evaluation is a linear map. AlsoEquality forces $f(-1)=f(0)=f(1)=0$. A nonzero polynomial of degree of a polynomial at most two cannot have three distinct roots of a polynomial, by the root bound for a polynomial. Thus equality holds only for the zero polynomial, establishing positive definiteness. Therefore the form is a real inner product, and $V$ is an inner product space. This is a discrete sampling inner product on polynomial spaces.

The gradient of the given polynomial isIts second component vanishes when $y=0$ or $x=-1$. The first component then gives exactly three critical points:The Hessian matrix and its determinant areAt $(-1/2,0)$ the Hessian matrix is $\mathrm{diag}(2,1)$, whose positive definiteness proves a strict local minimum by the second-derivative test. At $(-1,\pm 1)$ its determinant is $-4$, so both points are saddle points of a scalar function. Their values and classifications areThere are no local maxima.

The hypothesis implies $v_1,v_2\ne 0$, since the zero vector is a scalar multiple of every vector. SetSince $v_1$ is not a scalar multiple of $v_2$, $w\ne 0$. Positive definiteness and bilinearity of the real inner product giveMultiplying by $\Vert v_2{\Vert }^2>0$ and taking square roots proves the strict Cauchy-Schwarz inequalitySince every real number $s$ satisfies $s\le |s|$, this proves, in particular,The argument derives the required inequality directly from the inner product axioms.

The spectral theorem for real symmetric matrices gives $A=Q\Lambda Q^T$ with an orthogonal matrix $Q$. Positive definiteness makes every eigenvalue ${\lambda }_i$ positive. Define the matrix square rootIt is symmetric, its square is $A$, and for any nonzero vector $x$,Thus it is also positive-definite, and its matrix inverse is $A^{-1/2}=Q\mathrm{diag}({\lambda }_i^{-1/2})Q^T$.

Write $A=D+L+R$, where $D$ is the diagonal matrix of diagonal entries of $A$, and $L,R$ are its strictly lower- and upper-triangular parts. Since $A$ is symmetric, $R=L^T$. Subtract the two factors in the SOR iteration:Therefore, for $\omega \ne 0$,Positive definiteness makes each $D_{ii}>0$, so the triangular matrix $B(\omega )$ is an invertible matrix for every $\omega \ne 0$. The original formula gives $C(0)=I$; the expression for $B(\omega )$ itself is undefined there. The affine term ensuring that the desired solution is a fixed point is $c=B(\omega {)}^{-1}b$.

For the quadratic function, write $r_j=b-A{x}_j$ and restrict to the full line $x_j+\alpha p_j$. Symmetry of $A$ givesThe coefficient $p_j^{T}A{p}_j$ is positive by positive definiteness and $p_j\ne 0$. The unique minimizer is thereforeQuadratic minimization along a line also gives the exact changeThus strict decrease holds exactly when $p_j^T{r}_j\ne 0$. The search is over the whole line, so ${\alpha }_j$ can be negative; a positive step size would require the additional descent direction condition $p_j^T{r}_j>0$.

Nonzero vectors $p_1,\dots ,p_k$ are conjugate directions with respect to $A$ when $p_i^{T}A{p}_j=0$ for $i\ne j$. They are linearly independent: if ${\sum }_i{c}_i{p}_i=0$, multiplication by $p_j^{T}A$ gives $c_j{p}_j^{T}A{p}_j=0$, hence $c_j=0$ by positive definiteness. There are consequently at most $n$ such directions.

The finite termination of conjugate directions follows from the residual orthogonality maintained by the exact line minimizations. At step $j$,For $i<j$, conjugacy gives $p_i^T{r}_{j+1}=p_i^T{r}_j$. By mathematical induction, $r_{j+1}$ is orthogonal to every $p_1,\dots ,p_j$. After $n$ nonzero conjugate directions they form a basis of ${\mathbb{R}}^n$, forcing $r_{n+1}=0$. Thus the method reaches $Ax=b$ in at most $n$ steps, and positive definiteness makes that solution unique. The claim is in exact arithmetic; rounding errors need not preserve exact conjugacy.

Positive definiteness gives a positive smallest eigenvalue $c$ with $x^{t}Ax\ge c|x{|}^2$. Hence the set $x^{t}Ax=y^{t}Ay=1$ is compact, and $h$ attains a maximum there. Its two constraint gradients are $(2Ax,0)$ and $(0,2Ay)$; they are linearly independent because $x,y$ are nonzero and $A$ is invertible.

Put $s=x^{t}Ay$. At any constrained extremum the multiplier equations areMultiplication by $x^t$ and $y^t$ gives $\lambda =\mu =s^2$. If $s=0$ the objective is zero. Otherwise $Ay=sAx$, so $y=sx$ and normalization gives $s^2=1$. Thus every maximum is at most one, and the value one is attained at $x=y$. This proves $\boxed{(x^{t}Ay{)}^2\le 1}$ on the normalized constraints.

For nonzero arbitrary vectors apply that result to $x/\sqrt{x^{t}Ax}$ and $y/\sqrt{y^{t}Ay}$. If either vector is zero the conclusion is immediate. ThereforeEquality for nonzero vectors holds precisely when they are linearly dependent. This is the Cauchy-Schwarz inequality for the inner product induced by $A$.

The eigenvector identity $Ab=\frac{1}{2}b$ gives $\nabla f(-b)=-2Ab+b=0$. Since $H_f=2A$ is a positive-definite matrix, this is a strict local minimum. A global certificate follows by completing the quadratic form:Positive definiteness makes the first term nonnegative, with equality only at $x=-b$. Thus $-b$ is the unique global minimum, with value $-1/2$.

The strict positivity of a quadratic form on every nonzero vector. A real symmetric matrix has positive definiteness exactly when its associated quadratic form does, making it a positive-definite matrix. A Hermitian form is positive definite when its value on $(v,v)$ is real and strictly positive for every nonzero vector.

Think of a positive-definite quadratic form as an energy cost for a displacement: every direction costs something, and zero cost identifies the origin. For example, the real symmetric matrixis a positive-definite matrix because the two squares can vanish together only at $(0,0)$. The mixed term couples the coordinates, but cannot overwhelm their positive contributions: $q(1,1)=8$ and $q(1,-1)=4$. The graph is a bowl, and its positive level sets are ellipses with axes along the eigenvectors, rather than along the original coordinates.

The real and complex settings require different symmetry assumptions. For a real symmetric matrix use $q(x)=x^{T}Ax$ with $x\in {\mathbb{R}}^n$; for a complex Hermitian matrix use $q(z)=z^{\ast }Az$ with $z\in {\mathbb{C}}^n$, where $z^{\ast }$ is the conjugate transpose. The latter expression is real, so strict positivity is meaningful. A complex quadratic form such as $z_1^2+z_2^2$ has no such positivity: it vanishes at $(1,i)$ and takes negative real values at $(i,0)$. A Hermitian form uses complex conjugation, which changes this example to $|z_1{|}^2+|z_2{|}^2$.

For a real matrix $M$ without a symmetry hypothesis, its quadratic form depends only on its symmetric part of a matrix:Positive eigenvalues of $M$ alone do not establish positive definiteness of this quadratic form. For example, $M=\left(\begin{array}{cc}1& 4\\ 0& 1\end{array}\right)$ has both eigenvalues equal to $1$, yet $q(1,-1)=1-4+1=-2$. The relevant test is positivity of the symmetric part of a matrix, not the spectrum of an arbitrary matrix.^[ref]