Let . Form the evaluation map into a product space
The cube is compact by Tychonoff's theorem and Hausdorff, so its closed subspace is a compact Hausdorff space. A completely regular Hausdorff space has enough continuous functions to separate points and closed sets. Consequently is injective and continuous, and is a topological embedding: given open, choose with and off ; the coordinate neighborhood pulls back to a neighborhood of inside . Identify with this dense embedded subspace. This constructs the Stone-Čech compactification.
Each extends as its coordinate function. Rescaling therefore extends every bounded real continuous function; real and imaginary parts extend bounded complex continuous functions. The extension is unique because is dense and the target is Hausdorff. Its supremum norm is unchanged by density, so restriction is an isometric isomorphism of normed spaces of Banach spaces
These are the space of continuous functions on a compact space and the space of bounded continuous functions, respectively.
More generally, embed a compact Hausdorff space in its own evaluation cube . This is an embedding by complete regularity, and its image is closed by compactness. For a continuous map , extend all the bounded coordinates to . The resulting continuous map into the cube takes its values in the closed copy of : it does so on the dense subset , and the inverse image of that closed copy is closed. Thus
Uniqueness again follows from density. This is the universal property of the Stone-Čech compactification. It implies uniqueness of the compactification up to a homeomorphism fixing , by extending the identity in both directions. Every Hausdorff compactification of is the image of under a continuous surjection fixing , since that image is compact and contains the dense copy of . A continuous map between completely regular Hausdorff spaces likewise extends uniquely to after composing with the embedding of ; these extensions preserve identities and composition. If is already compact, its dense copy is closed, so .
For discrete , every indicator , , is continuous. Its extension to takes only values , since this closed condition holds on the dense subset . Its one-set is precisely and is clopen: it is closed, and every neighborhood of one of its points meets by density and continuity. This also gives the discrete Stone-Čech compactifications are extremally disconnected property: for open , density implies , which is clopen.
For the requested separation, a compact Hausdorff space is normal, so Urysohn's lemma supplies with on and on . Put and
The preceding indicator argument makes both sets clopen. At a point of , the neighborhood has its dense -part inside , so the point lies in . At a point of , the neighborhood misses , so the point lies outside . We have proved the stronger clopen separation in discrete Stone-Čech compactifications conclusion
Finally take , the Stone-Čech compactification of the natural numbers. It is a compact Hausdorff space and is separable, because its embedded copy of is countable and dense. For every , the indicator extends to an element . For distinct , evaluating at an integer in their symmetric difference gives . There are uncountably many such indicators. A separable metric space cannot contain an uncountable family at pairwise distance one: disjoint balls of radius would require distinct members of a countable dense set. Hence is not separable, despite separability of .
Here is a finite real convex function on the whole real vector space; no topology or continuity is required. We prove the convex domination form of the Hahn-Banach theorem by extending across one vector. Suppose a linear functional is already dominated on a vector subspace and choose . For and positive , the proposed extension must satisfy the bounds
To prove compatibility, take and . The convex combination of and with weights and lies in . Domination there and convexity give
Rearranging proves every lower candidate is at most every upper candidate. Also the candidates show
Thus both endpoints are finite, and some exists. For the upper bound gives domination of ; for the lower bound gives it; and for domination was assumed. This defines a dominated linear functional on extending .
Order all dominated extensions by inclusion of their domains and agreement of values. A chain has the union extension, still linear and dominated. By Zorn's lemma there is a maximal extension. The one-vector construction would enlarge any proper domain, so its domain is . Hence there is a full linear extension with . A convex function here need not be positively homogeneous; this is why the bounds included all positive .
Now fix . The translated convex function vanishes at zero. Apply the just-proved extension theorem to the zero linear functional on to obtain a linear functional with for every . Set
This is an affine function, satisfies for every , and has . Let . Every member is an affine minorant, while the member indexed by attains at . Thus the exact representation is
This is the finite convex function as supremum of affine minorants property. The linear functionals are algebraic; without a topology no assertion of continuous affine minorants is intended.
Start with the approximate surjectivity with geometric correction argument. For a fixed , put . Choose successively so that
Induction gives and . The series is absolutely convergent in the Banach space , because the sum of its norms is bounded by the convergent geometric series . Write its sum as . The bounded linear operator commutes with this limit, while . Consequently
The choices need not depend linearly on ; no bounded linear right inverse is being asserted.
For the bounded extension, use the restriction bounded linear operator on the Banach spaces of bounded continuous functions, each with its supremum norm. We will construct an approximation to any with . The two sets
are disjoint closed sets in , since is closed. If both are nonempty, the distance to a set construction defines a continuous function
The denominator is positive at every point: zero distance to a closed set means membership in that set. Thus , with on and on . On and , and also on the intervening range , this implies
If only is empty take , if only is empty take , and if both are empty take ; the same bounds hold. For take . This proves the approximation hypothesis with and . Applying the argument above to yields an element of the bounded continuous functions with and . Restriction gives the reverse inequality, so the extension preserves the exact supremum norm:
This proves the bounded metric-space case of the Tietze extension theorem rather than presupposing it.
For an arbitrary real continuous function on , let . Its values lie strictly between and , although its supremum norm can be . The bounded result supplies an extension with . Let , a closed set disjoint from . If is nonempty and is nonempty, put
Both factors are continuous functions; on and on . Off we have , and on we have , so everywhere. Therefore a real continuous extension is
On it equals . If is empty use ; if is empty simply take . This handles unbounded without requiring a uniform margin between and .
Vary the Maxwell Lagrangian with respect to . Since and is antisymmetric,
After integration by parts, the Euler-Lagrange field equation is
The gauge transformation leaves unchanged because partial derivatives commute. This gauge invariance means that potentials related in this way represent the same electromagnetic field; all four potential components are not independent physical photon degrees of freedom. In particular, the ungauged kinetic operator is singular and its temporal canonical momentum vanishes.
Choose the Lorenz gauge . The field equation then becomes . Residual gauge transformations with preserve this condition. The correctly named Lorenz gauge condition concerns the divergence of the potential; Lorentz covariance is the separate transformation property of the equations.
For the alternative quadratic Lagrangian density, direct differentiation gives
so its Euler-Lagrange field equation is also . This density is the Feynman-gauge Maxwell kinetic density after a boundary-term subtraction. Indeed, with ,
The divergence identity follows by expanding and commuting its second derivatives. The quadratic density alone describes four wave equations; the gauge constraint must still be incorporated in the physical states. It is not an unconstrained four-positive-polarization replacement for Maxwell equations.
Using this specified boundary-term convention, the canonical momentum conjugate to is
The canonical commutation relations are therefore
with field-field and momentum-momentum brackets zero. Equivalently . The temporal component has the opposite sign to the spatial components; replacing the metric by a positive Euclidean one would destroy these brackets.
The wave equation gives massless frequencies . Hermiticity pairs its positive- and negative-frequency solutions, so the Heisenberg picture field has the expansion
This is a real field expansion for each component, but its oscillator metric is indefinite. The assumed brackets, with the dagger's vector index restored, are
They verify the photon oscillator completeness and canonical brackets. Inserting gives at equal times
The other brackets cancel between the two frequency contributions. Thus both the normalization and the sign are needed.
The Gupta-Bleuler formalism imposes the Lorenz gauge condition weakly on states:
Here is the annihilation, positive-frequency part. Setting the full divergence to zero as an operator would conflict with the four-component canonical commutation relations. The subsidiary condition instead selects a physical pre-space inside the covariant photon Fock space, followed by a quotient of its null states.
Apply it to . Commuting through the creator gives a multiple of . Hence the allowed photon polarization vectors obey
Take . The complete solution is
with complex coefficients. The last two vectors are transverse linear photon polarizations, and gives the two circular photon polarizations. The first vector is , the allowed temporal-longitudinal combination. A purely spatial longitudinal vector by itself does not satisfy the subsidiary condition.
The oscillator brackets give, apart from the positive continuum normalization factor,
Momentum eigenstates are distributionally normalized; a box or a wave packet makes the norm statement literal. The allowed longitudinal combination has zero norm because , and it is orthogonal to every constrained state since . It also gives a pure-gauge field wave: the field strength of vanishes. Thus and represent the same physical photon polarization. The Gupta-Bleuler null-state quotient removes this null direction and leaves two positive-norm transverse photon photon polarizations. The negative-norm temporal oscillator of the unrestricted space is not an extra physical state. This is the transverse one-photon physical quotient.
Finally verify the two-photon fermion Ward identity directly for electron-positron annihilation into two photons. Define and the reduced Dirac propagator
The equality follows from the Clifford algebra, since . The incoming Dirac spinors obey and . On replacing one inserted photon polarization vector by , the spinor factor in the two diagrams is
At the electron end, , so
For the other diagram, four-momentum conservation gives . Hence
The two contributions therefore cancel exactly:
Individual diagrams need not vanish; their sum implements the Ward identity. Linearity then gives invariance under , as required by gauge invariance. In particular, the null longitudinal photon polarization proportional to has zero physical production amplitude. Physical scattering produces the transverse photon classes, not an independent longitudinal massless photon.
The real scalar field has canonical momentum . Its Legendre transform gives
The first three terms constitute , the free massive Hamiltonian operator. The remaining term is . This sign is a consequence of having no time derivatives in the interaction: the canonical momentum is unchanged, while the interaction enters the Hamiltonian density with the opposite sign to the Lagrangian density.
In the interaction picture, operators evolve with and states evolve with constructed from the free interaction-picture field. Their evolution operator satisfies
Integrating once gives . Repeated substitution orders the integrations as . Replacing the ordered simplex by the full integration cube introduces and a time-ordered product, which proves the Dyson series
Taking the asymptotic limits with the usual adiabatic or regulated scattering prescription yields
The field in this expression is the free field evolving in the interaction picture, not an independently prescribed classical field. For quantum fields the expansion is a regulated perturbation series; the time-ordering symbol cannot be dropped because the operators at different times need not commute.
Use relativistically normalized two-particle states , with . The connected first-order scattering contribution is
Here the connected S-matrix uses a normalized vacuum and physical external one-particle states. At this order, normal ordering the vertex separates the four-external-leg contribution from vacuum and tadpole pieces; those disconnected or external-mass contributions are not the two-to-two scattering amplitude.
Two fields annihilate the incoming particles and two create the outgoing ones. There are choices of the annihilating fields, two assignments of the incoming momenta and two assignments of the outgoing momenta. Hence the total factor is , the external-leg factorial cancellation at a quartic scalar vertex. Each attachment contributes its plane-wave factor, so
The spacetime integral is the four-momentum conservation delta function, giving
In particular, the tree scattering amplitude is independent of angle.
For completeness, derive the relativistic two-body phase space in the centre-of-mass frame. Write , . The spatial delta function sets the two final momenta opposite, and the energy delta function is . Its radial Jacobian is . Thus
The invariant flux factor is for equal initial masses. Using , the labeled angular density is . The outgoing quanta belong to one real scalar field, so they are identical. A full-sphere integration counts each event twice and requires the identical final-state symmetry factor . Consequently
Equivalently one may integrate the labeled density over a hemisphere representing each event once. The final-state factor is separate from the vertex factorial: applying it at the vertex instead would give the wrong elastic scattering from a quartic scalar contact interaction cross-section.
Treat as independent variables when taking the Legendre transform. Their canonical momenta are
The canonical quantization of a complex scalar field imposes, at equal times,
with , together with their adjoints, and . These are canonical commutation relations for two real scalar degrees of freedom, written in a complex basis. Local fields are operator-valued distributions, so these identities are understood after smearing or with a regulator.
The Hamiltonian density is , giving
The Heisenberg equation of motion is . For example, , while commuting with the gradient term and integrating its derivative of a Dirac delta distribution gives . Thus
Both fields satisfy the Klein-Gordon equation. Their spatial Fourier modes therefore have frequencies , . A complex scalar field has independent coefficients for these two frequency sectors, so, defining the invariant measure , its mode expansion is
One can recover the mode operators at any fixed time using complex scalar mode inversion:
Substitution of the expansion verifies these inverses: the spatial integral selects the appropriate momentum, while the two frequency sectors acquire factors . The unwanted sector vanishes when the delta function sets . The canonical commutation relations then give, for example,
Likewise has the same value. The cross commutators vanish; their coefficients contain on the corresponding momentum delta function. Conversely, these oscillator brackets reproduce the equal-time field brackets, confirming the normalization of the measure.
Insert the mode expansion into and integrate over space. The terms and enforce opposite spatial momenta; their coefficient is . For the diagonal terms the coefficient is . Combining this with the two invariant measures leaves
In a box, , one half-quantum for each of the two real oscillator species. It diverges as the cutoff is removed. Normal ordering removes this vacuum energy, yielding the normal-ordered Hamiltonian of a free complex scalar field:
In this nongravitational free theory an additive constant in changes only an overall state phase, not Heisenberg equations of motion, energy differences or scattering probabilities. It is therefore consistent to choose the Fock vacuum energy as zero. Both creation operators raise the energy: and . Repeated creation builds a bosonic Fock space of spin-zero particles and antiparticles, with the same positive mass and energy. The negative-frequency part of the field does not describe negative-energy physical particles.
The global phase symmetry of a complex scalar field, with orientation , has the indicated Noether current. Taking its divergence, the two first-derivative terms cancel, giving
The same calculation holds for its normal-ordered version. Integrating its temporal component gives a conserved charge when spatial boundary flux vanishes.
Here the ordering convention is important. Direct substitution into the literally ordered product yields
The pair terms again vanish, now because their coefficient is a difference of equal energies. Reordering the term produces a divergent vacuum constant: with a finite-mode regulator . Thus the requested finite charge formula uses zero vacuum charge, or equivalently the vacuum subtraction of a complex scalar charge. With that conventional subtraction,
The subtraction changes neither current conservation nor the action of on fields; without it, the unrenormalized product does not literally equal the displayed normal-ordered operator.
Finally the oscillator commutators give
or . Acting on a charge eigenstate, the first creator raises its charge by one and the second lowers it by one. Starting from the neutral Fock vacuum, their one-particle states therefore have charges and respectively. This is the complex scalar charge operator , whereas the energy counts the sum of the two occupations. A multiparticle state has additive charge equal to the number of particles minus the number of antiparticles.
Use natural units and the Minkowski metric . Multiplication of the block gamma matrices gives
The Pauli matrices obey , so the spatial anticommutators are , the mixed ones are zero, and the temporal one is . Thus
Because partial derivatives commute, only the symmetric part of survives in their contraction. Multiplying the Dirac equation on the left by its opposite-mass factor therefore gives the flat-space Dirac factorization
Hence every component satisfies , the Klein-Gordon equation. This implication does not remove the first-order spinor constraints imposed by the Dirac equation.
For Lorentz covariance, transform coordinates by and the Dirac spinor by . The chain rule gives . Consequently
The cancellation uses . Thus the Lorentz transformation maps every solution to another, establishing the Lorentz covariance of the Dirac operator.
The infinitesimal identity follows directly from the Clifford algebra. Moving past the two factors gives
Subtracting the same identity with interchanged proves the spinor Lorentz generator commutator
Contracting with the antisymmetric tensor yields
It also verifies for the infinitesimal Spinor representation of the Lorentz group.
For a positive-energy four-momentum, put , . The Dirac equation becomes , where the Feynman slash notation uses . Splitting gives
The second equation sets , and the first is then the mass shell, since . Choosing two orthonormal two-spinors gives the Dirac plane waves in the standard representation:
The negative-frequency solutions are independently
with orthonormal . They are the antiparticle modes after field quantization. The normalization gives and , , using the Dirac adjoint .
For a spatial rotation, . Hence the intrinsic rotation generators are
For a massive particle in its rest frame, the two independent spinors therefore carry the two-dimensional spin one-half representation, with eigenvalues and Casimir . A rotation acts as , the spinor sign under a full spatial rotation. Thus the particle has spin ; four spinor components encode particle and antiparticle sectors, not four spin states of one particle. In the massless limit the corresponding physical labels are helicities rather than a rest-frame spin basis.
Fix a clamped nondecreasing spline knot sequence on , with and . For continuous splines assume interior multiplicities at most , and assume . An order- spline has degree at most on each nonempty knot interval; at a knot of multiplicity it has continuous derivatives. Its dimension is , and a normalized B-spline basis has local support, nonnegative values and sum one on .
These properties are visible in the Cox-de Boor recurrence. Starting with interval indicators at order one, set
with a term of zero denominator defined to be zero. On each contributing support the weights are nonnegative, and summing the recurrence makes the coefficients of each lower-order B-spline add to one. This gives partition of unity and , with endpoint values defined by limits.
At distinct ordered sites , interpolation is the linear system
It has a unique solution for every data vector exactly when the B-spline collocation matrix is invertible. The Schoenberg–Whitney theorem characterizes this by for every ; away from clamped endpoints this is . At the clamped endpoints the first and last B-splines have value one, so the endpoint version uses this positive-value formulation rather than the strict inequalities. Both ordering and the support conditions matter. A spline space with interior knots is generally not a Chebyshev system on the entire interval: a locally supported B-spline vanishes on an interval. Thus arbitrary distinct nodes need not permit interpolation. Merely specifying all data sites as knots also need not fix the remaining freedom; for example, cubic interpolation with simple interior knots at data sites has dimension , and two extra boundary conditions are needed to interpolate values uniquely.
The positivity governing this existence theorem is total nonnegativity of B-spline collocation matrices: every square minor with increasing row and column indices is nonnegative. In spline terminology this is often called total positivity, but it permits zero minors caused by disjoint supports. It is not the stronger assertion that every minor is strictly positive. A useful proof uses knot insertion. Each refinement step changes coefficients by
with the unchanged endpoint pieces. The refinement matrix is nonnegative bidiagonal, and its minors are nonnegative: any nonzero determinant has its allowed diagonal matching and is a product of nonnegative entries. Products preserve this property by the Cauchy–Binet formula. Insert each sampling site to full multiplicity, so evaluation at that site becomes selection of an ordered refined coefficient. The collocation matrix is then an ordered row submatrix of the refinement product, proving total nonnegativity. The strict support criterion is the strict part of this argument: an ordered path through the consecutive support intervals exists exactly when every diagonal B-spline value is positive, giving the Schoenberg–Whitney theorem determinant criterion.
If is invertible, its determinant is positive and the adjugate identity yields
This checkerboard inverse of a totally nonnegative matrix is the central stability fact. With alternating data , the absolute row sums satisfy
For general data, the cardinal splines are . The spline interpolation operator and its exact Lebesgue constant of interpolation are
The equality for the operator norm follows by extending the signs at distinct sites to a continuous unit-norm data function, as in Solution 2. That solution also shows the last inequality can be very strict. Stability concerns the range function as well as its coefficients.
An optimal interpolation set must be specified relative to an objective. For the normalized B-spline coefficients, every admissible sampling set satisfies
since and sampling cannot increase the supremum norm. If a unit-norm spline has ordered alternating extrema at admissible sites, the checkerboard identity gives
Taking the largest row shows , so equality holds. This proves the optimal spline coefficient interpolation sites property and the Chebyshev spline coefficient and dual norm equality. In particular, for the cubic space of Solution 1 the alternating extrema satisfy the support conditions. Its coefficients have maximum magnitude , so these sites are optimal for coefficient recovery and
The argument proves optimality whenever this admissible equioscillating spline is available; it does not identify extrema of an arbitrary spline as optimal sites, or transfer coefficient optimality automatically to the Lebesgue constant of interpolation.
For the intrinsic interpolation objective, minimize itself. There is a minimizer for a fixed finite-dimensional continuous spline space. Indeed the sites lie in a compact ordered simplex. On admissible sets, inverse entries and hence vary continuously. As a collocation matrix tends to a singular one, its inverse norm diverges. The uniform-norm stability of a B-spline basis gives the complementary bound
choose sample signs attaining a largest inverse row, extend them to a continuous unit-norm function, and use . Thus a minimizing sequence with bounded Lebesgue constants of interpolation cannot approach a singular site set. A convergent subsequence has an admissible limit attaining the minimum.
Fekete interpolation sites for a continuous function space offer another useful, constructive criterion: maximize the absolute evaluation determinant. A nonzero maximum exists by compactness and linear independence. Replacing one row by evaluation at multiplies the determinant by the corresponding cardinal function, so maximality gives , and therefore . These sites are determinant-optimal, with a proven interpolation bound; one should not claim without further argument that they minimize . Greville abscissae provide inexpensive admissible sites for the usual continuous clamped spline spaces of order at least two. Admissibility alone does not give the best stability bound.
Finally, the coefficient condition number of a normalized B-spline basis is bounded by a constant depending only on order. To see the mechanism, for each coefficient choose a longest nonempty knot cell in that B-spline's support. If its length is and the support width is , then . On that cell the spline is a degree-at-most- polynomial; repeated Markov polynomial inequalities bound its th derivative by . In the De Boor–Fix spline coefficient functional, the accompanying derivative of the knot polynomial is bounded by . Each term is consequently at most , yielding independently of the number and spacing of knots. This is basis stability; it does not bound at arbitrarily poor sampling sites.
For approximation, reproduction gives the Lebesgue inequality for approximation projectors
As the maximum knot gap tends to zero with fixed order, spline quasi-interpolation gives an approximant with error at most , so the infimum tends to zero. A sequence of uniformly bounded interpolation linear operators therefore converges to every continuous target. Local support and a stable choice of sites make spline interpolation effective; mesh refinement without control of the interpolation operator norm is not by itself a convergence proof.
The Weierstrass approximation theorem asserts that every real continuous function on a compact interval is a uniform limit of polynomials. An affine change of variable reduces the interval to . There are two complementary routes: construct positive approximation operators, or construct an algebra dense enough to approximate absolute values and hence pointwise maxima and minima.
For the first route, the Korovkin theorem reduces convergence of positive linear operators on continuous functions to the three tests . Here is its proof. Given and , uniform continuity supplies a such that for . For all one then has
Apply positivity to the two real inequalities obtained from this bound. At this gives
The expression in brackets is nonnegative and tends uniformly to zero by the three test assumptions. Also uniformly. Taking the upper limit of the supremum norm and then letting proves convergence for every . This is the quadratic barrier estimate for positive approximation operators.
The Bernstein polynomial supplies the constructive corollary
The binomial theorem makes its nonnegative weights sum to one. Differentiating the binomial theorem, or using the first two binomial distribution moments, gives
The Korovkin theorem proves uniformly, and each is a polynomial, proving the Weierstrass approximation theorem. The same criterion gives the Schoenberg spline operator convergence in Solution 3. For periodic functions its counterpart uses : the nonnegative barrier is bounded away from zero outside any fixed small arc. This periodic Korovkin test set gives convergence of the normalized Fejér sums and density of trigonometric polynomials.
The Lebesgue convergence criterion for approximation operators addresses stability. Suppose bounded linear operators converge to the identity on every polynomial. Then they converge on every continuous function if and only if . Sufficiency is the extension of approximation operators from a dense subspace: for a polynomial with and a common bound ,
First let , then . Necessity follows from the Uniform boundedness principle, since a convergent is bounded for every . Applied to approximation projectors, the Lebesgue inequality for approximation projectors is
Indeed makes ; take the infimum. Thus density of the ranges and a uniform bound on the Lebesgue constants of interpolation imply convergence. Density alone does not control a sequence of interpolation projectors.
There is also a kernel version of this stability criterion, useful for trigonometric polynomial approximation. If integrable periodic kernels have uniformly bounded norms, their integrals tend to one, and for every , then convolution with converges uniformly to every continuous periodic function. Subtract and split the error into a small arc, bounded by , and its complement, bounded by . The remaining mass error tends to zero. This approximate identity argument shows directly why nonnegative unit-mass Fejér kernels work even though ordinary Fourier partial sums need not converge uniformly for every continuous target.
Lebesgue's constructive approach to the Weierstrass approximation theorem starts instead with the absolute value. On define the polynomial recurrence for the absolute value
Induction gives , monotonicity, and
The pointwise limit satisfies and is nonnegative, hence equals . Dini's theorem makes the convergence uniform. More explicitly, the continuous decreasing errors have, for any positive tolerance, open sublevel sets covering the compact interval; a finite subcover supplies one index valid everywhere. Every continuous piecewise linear function has the form
The are its changes in slope. Scaling and translating the absolute-value approximation shows that is uniformly approximable by polynomials. Solution 4, or polygonal interpolation and uniform continuity, makes these piecewise linear functions dense in . Approximating first by and then by a polynomial is another complete proof of the Weierstrass approximation theorem.
The Stone-Weierstrass theorem generalizes from intervals to a compact Hausdorff space : a real subalgebra containing the constants and separating points is uniformly dense. Its proof uses the same absolute-value mechanism. The uniform closure of a function algebra is an algebra. If belongs to this closure, approximation of by polynomials on a bounded interval containing its range proves that also belongs to the closure. Therefore the closure contains
Point separation and constants allow exact interpolation of any two real values: choose with and solve for in . The lattice approximation from two-point approximation proved in Solution 4 now makes the closure all of .
The complex version requires a unital, point-separating subalgebra closed under complex conjugation. Its real-valued members separate points using real or imaginary parts, so the real result approximates both parts of every complex target. Conjugation closure is essential: polynomials in alone on the closed unit disk cannot approximate uniformly, because whereas .
Among the corollaries are density of multivariate polynomials on compact subsets of real Euclidean space, since coordinates separate points, and density of trigonometric polynomials on the circle, since sine and cosine separate points there. On a compact interval, polynomials with rational coefficients are a countable dense family, giving separability of . The real and complex approximation results above concern uniform approximation of continuous targets; neither asserts convergence of arbitrary interpolation schemes or arbitrary Fourier series.
The original PDF's kernel has an incorrect normalization. With ordinary , its integral is , not one. Already at the printed kernel is the constant . For , the printed operator therefore returns , while for every . Thus the literal inequality with the printed kernel is false. The Fejér kernel representing the stated average of Fourier partial sums is
The ratio at zero is interpreted by its limit. To verify the normalization and the averaging property, use the finite geometric sum:
Fourier orthogonality gives unit integral; the displayed Fourier coefficients are exactly the multipliers of the Fejér sum. This also proves nonnegativity.
For the correctly normalized Fejér sum, subtraction of gives
Take and split at . The inner part is at most , because the kernel is nonnegative and has mass one. The strict inequality in the definition of the modulus of continuity causes no endpoint issue: continuity gives the same bound at distance exactly .
On , , so
Divide an arc of length into at most shorter arcs. The triangle inequality then bounds by . Consequently
Choosing proves the requested fractional-scale Fejér approximation bound for the intended normalization, for example with the absolute constant :
The same proof applies to complex-valued continuous functions, since the estimates use absolute values.
Fix and put . For each pair , choose whose errors at these two points are smaller than . By continuity, there is a neighborhood of on which . For a fixed , these neighborhoods cover the compact space . Choose a finite subcover and form
Then everywhere, while , because every chosen function has error smaller than at . Since is continuous, there is a neighborhood of on which .
A second use of compactness supplies covering . Set
Every is above , so their minimum is also above it. At each point at least one is below , and hence so is their minimum. This proves . The two finite operations are the essential mechanism in lattice approximation from two-point approximation; no closure under addition or multiplication has been assumed.
For the corollary take to consist of all continuous piecewise linear functions on . On a common finite partition, the maximum or minimum of two affine pieces can change which piece it selects only at their crossing. Adding those finitely many crossings to the partition shows that is closed under both operations. For two distinct points, the affine polynomial through the two prescribed values belongs to and matches exactly there; for equal points use a constant function. The proved criterion therefore gives density of continuous piecewise linear functions in in the supremum norm. Concretely, interpolation on a partition of maximum gap also gives error at most by uniform continuity.
The Korovkin theorem says that a sequence of positive linear operators on continuous functions on converges uniformly to the identity on every continuous function if it does so on , and . Necessity is immediate; the quadratic barrier argument establishing sufficiency is given in Solution 6.
There is an endpoint misprint in the original PDF: the repeated knots at the right end must equal , not . Otherwise the advertised nondecreasing spline knot sequence on does not exist. Use the corrected clamped spline knot sequences, with fixed order and maximum gap . Let denote the number of B-splines for the th sequence. Nonnegativity and partition of unity show that the Schoenberg spline operator is positive and .
The monomial B-spline coefficients reproduce the first two nonconstant test polynomials. Put
The pair sum uses , as in the PDF; the converted TeX incorrectly includes . Every sampling point and every knot entering these coefficients lie in , whose length is at most . Thus . Also all these numbers belong to , so
The positive B-spline weights give
Together with , the three limits prove
Under the usual interior knot multiplicities at most , the splines are continuous and the quoted form of the Korovkin theorem applies directly. If full interior multiplicity is allowed, the output can be discontinuous. The same positive-operator quadratic barrier proof still works with bounded output functions and the supremum norm, and proves exactly the stated uniform error limit without asserting continuity of each output. Indeed the stronger local-support error bound for a Schoenberg spline operator, , follows by comparing with each active sample value. The restriction is needed for the displayed quadratic reproduction argument, not for this direct support estimate.
Write the spline interpolation operator as , where samples at the interpolation sites and synthesizes the normalized B-spline expansion. Sampling has operator norm at most one, and nonnegative B-splines with sum at most one give . Hence
Here the matrix norm is the maximum absolute row sum. Existence of the inverse matrix requires the usual ordered distinct interpolation sites and the Schoenberg–Whitney theorem support conditions, with endpoint values interpreted by their one-sided limits. If the displayed diagonal condition is read without the implicit ordering and distinctness, it is insufficient: all three sites equal to in the quadratic example give positive diagonal entries but three identical rows. The estimate applies whenever the interpolating linear map in the question is defined uniquely.
For the quadratic Bernstein basis, , and . Sampling and inverting give
The three cardinal Lagrange interpolation polynomials are
The Lebesgue constant of interpolation gives the exact operator norm: the upper bound follows from . To attain it at a maximizing point, prescribe at the three sites the signs of the corresponding cardinal polynomials and extend those values by a continuous piecewise linear function of norm one.
On , the first two cardinal polynomials are nonnegative and the last is nonpositive. Since their sum is one, their absolute sum is . Its maximum is at . Reflection gives the same maximum at on the other half. In particular the data produce , attaining . Thus
The coefficient estimate loses cancellation among the B-splines, which explains why it is not sharp for the resulting function norm.
The cubic spline is even, so the symmetry of the normalized B-splines gives . It therefore suffices to extract three coefficients. The two polynomial pieces join with matching values and first two derivatives at zero, so the function belongs to the stated cubic spline space. At zero its right-hand derivatives are
Use the supplied De Boor–Fix spline coefficient functional with the unnormalized knot polynomial . In this sign convention the coefficient is
For , . Taking the right-hand limit at kills the first three terms and gives . For the next two coefficients take the right-hand limit at zero. Their knot polynomials and derivatives there are
Consequently and . Although the third derivative jumps at zero, its multiplier vanishes for these two functionals; taking either one-sided limit gives the same coefficients. Symmetry gives and . Thus
For a normalized B-spline basis, define the synthesis linear map from the maximum coefficient norm into the spline space with its supremum norm. Nonnegativity and partition of unity give , including equality on the all-ones vector. The coefficient condition number of a normalized B-spline basis is
On , . The only interior extremum is at , where ; at zero and one, . Reflection therefore gives . Its largest absolute coefficient is , proving
Let be the unique sonic point, . For a differentiable convex function, is nondecreasing, negative below and positive above it. Split the flux into the parts propagating right and left:
Thus ; explicitly, for and zero below, while for and zero above. The Engquist-Osher flux takes the right-going contribution from the left state and the left-going contribution from the right state:
It is consistent, . For a transonic rarefaction wave with it gives ; reversed transonic states give . This is sonic-point splitting of a convex Engquist-Osher flux.
On cells of width let approximate the cell average and . The explicit Engquist-Osher method is the finite volume method
The shared interface flux makes it conservative: sums telescope for periodic data, and the same applies on the infinite grid when boundary contributions vanish. To prove stability, choose an interval containing the initial states and impose the CFL condition
The partial derivatives of the update are
Therefore the update is a monotone conservative scheme. Because , componentwise comparison with the constant states proves . Induction gives a mesh-independent maximum bound for every time level, and ensures the same CFL speed bound remains valid.
There is also a stronger L1 contraction of a monotone conservative scheme. Let denote one whole-grid update, and let and componentwise. Monotonicity gives . Hence componentwise, and conservation yields
This applies to periodic grids or summable perturbations with vanishing end fluxes. Apply the same result to and its one-cell translate; translation commutes with , so
Thus the method is a total variation diminishing scheme. Under the stated CFL condition it preserves the initial range, contracts differences in discrete , and cannot increase total variation. These bounds give a precise nonlinear stability result, including across the sonic point.
Convexity alone does not remove the time-step restriction omitted in the question. For the Inviscid Burgers equation , linearize about a positive constant . The update becomes . Its alternating Fourier mode has multiplier , whose modulus exceeds one if . Hence unrestricted explicit time steps are unstable even for a convex flux with one sonic point. This is CFL necessity for explicit Engquist-Osher stability.
For data values, define the discrete Fourier transform by
Direct evaluation costs operations. For even , split the sum into even and odd indices. If and are the length- transforms of and , then
The fact that makes the two smaller transforms reusable; gives the second output. This is the FFT butterfly. For recursively splitting until length one gives
An iterative Cooley-Tukey FFT algorithm can arrange the input in bit-reversed order and apply butterflies of lengths . The factors are the twiddle factors. Each level uses work, and in-place organization uses storage. The inverse uses the conjugate roots with the final normalization . Mixed-radix factorizations extend the same idea beyond powers of two. For a two-dimensional array, transforming rows and then columns implements the two-dimensional transform, again exploiting separability.
For a concrete Poisson application, choose in the unit square with zero Dirichlet boundary conditions. Let and use interior indices . The five-point finite difference equation is
The product sine vectors diagonalize this matrix. Their eigenvalues are
Define the two-dimensional discrete sine transform coefficients by
The sine orthogonality identity gives the inverse reconstruction. The discrete sine transform Poisson solver is therefore
All eigenvalues are positive, so there is no division by a zero mode for Dirichlet data. Nonzero prescribed boundary values are moved to the right side at neighboring interior points before transforming.
To compute the sine transform with a Fast Fourier transform, extend a one-dimensional interior vector oddly to length : set and . Its length- Fourier coefficient is . Thus ordinary FFTs compute each sine transform. Apply this along all rows and columns, divide the coefficients, and apply inverse transforms. The total cost is operations and storage for unknowns. The method solves the finite-difference linear system directly to rounding accuracy; discretization error is separate and is second order for sufficiently smooth data and solution.
Boundary conditions determine the appropriate transform. Periodic data use the ordinary two-dimensional discrete Fourier transform and eigenvalues . The constant mode then requires zero-mean forcing, and the solution mean is fixed separately. Neumann conditions similarly use an appropriate discrete cosine transform, with a compatibility condition and an undetermined additive constant. A Fourier spectral discretization would use continuous spectral eigenvalues instead of the finite-difference eigenvalues above. The speed comes from a constant-coefficient separable operator on a regular rectangle; a general irregular domain or variable-coefficient equation is not diagonalized by this simple FFT construction.
For an initial value problem, convergence of a numerical method means that on each fixed interval , the largest error at mesh points tends to zero as the step size tends to zero and the starting errors vanish. Order of a numerical method describes how rapidly an exact-start local defect decreases; stability of a numerical method controls propagation of that defect and of errors already present. These are related but different properties. A high-order expansion alone cannot ensure that accumulated error remains small.
For a one-step map , define the exact-start defect . Order means for sufficiently smooth solutions. The normalized local truncation error divides this defect by and is ; explicitly specifying the normalization prevents an apparent one-order discrepancy. Suppose the map satisfies the local Lipschitz bound
For the error recurrence is
Iteration, or the discrete Gronwall inequality, yields for . Stable propagation converts an order- local formula into global error , provided starting data are equally accurate. Consistency is the weaker requirement that the normalized local defect tend to zero.
For a linear multistep method, zero-stability concerns the recurrence at . Its roots must have modulus at most one, and unit-modulus roots must be simple. Roots outside the circle amplify errors exponentially in the number of steps; repeated unit roots introduce polynomial growth. The requirement is stronger than bounded propagation for one fixed number of steps: as on a fixed physical interval there are steps. Under the usual Lipschitz assumptions and consistent starting values, the Dahlquist equivalence theorem says consistency plus zero-stability is equivalent to convergence. Its sufficiency combines a bounded discrete recurrence propagator with the sum of local defects, while necessity follows by exciting an unstable homogeneous root or by observing the persistent error of an inconsistent formula.
For example, satisfies the first-order consistency identities, but its root polynomial is . For , a small starting error can excite a mode and destroy convergence as the number of steps increases. In contrast, simple parasitic unit roots such as the root minus one of a two-step centered method need not destroy zero-stability: they remain bounded, though they can create unwanted oscillations and affect practical behavior. Accurate starting procedures and suppression of accumulated parasitic error remain relevant.
Absolute stability asks a different question: for , does the discrete solution decay or stay bounded for a specified finite ? The Forward Euler method has and, for real , requires . It converges as , but a stiff decay mode can impose a severe practical step restriction unrelated to the accuracy needed for slowly varying components. The Backward Euler method has , hence is A-stable and L-stable. The trapezoidal rule has , is A-stable, but tends to minus one on a very stiff negative mode and can retain alternating transients rather than damping them rapidly.
Zero-stability therefore does not imply unrestricted absolute stability; the three-step family analyzed earlier in this paper is a particularly strong illustration. Conversely a stable large step need not be accurate. This distinction matters for a stiff equation. The Second Dahlquist barrier limits A-stable linear multistep methods to order at most two, whereas implicit Runge-Kutta methods can have much higher order and still be A-stable. That barrier concerns the specified method class, not every numerical time integrator.
Finally, the mathematical order depends on smoothness: nonsmooth solutions or forcing can lower observed rates. Roundoff, imperfect implicit solves and inaccurate derivative evaluations introduce additional defects. Smaller steps increase the number of such perturbations; truncation error alone does not describe arbitrarily fine computation. The useful organizing principle is consistency supplies vanishing defects, stability bounds their accumulation, convergence follows, and order quantifies its rate under the stated regularity and starting-error assumptions.
Interpret the printed missing parenthesis as ending before the next summand. The characteristic polynomials of a linear multistep method are then
They satisfy and , so the consistency identities hold. The two nonprincipal roots are reciprocal. If , they are , distinct unit roots also distinct from the principal root one. If , one reciprocal root lies outside the unit circle. At the root one is triple, and at the root minus one is double. The root condition for a multistep method therefore holds exactly when . The Dahlquist equivalence theorem, with standard smoothness and consistent starting data, gives
To determine order rather than guess it from the number of steps, the exponential-symbol order criterion for a multistep method gives
Thus the formal order is three for , and four for . At the exceptional value the coefficient is . That fourth-order formula is not zero-stable. Every convergent member consequently has global order three. At the third-order Taylor cancellation is only a formal defect property of a degenerate, nonconvergent recurrence.
Here is a direct A-stability test for the convergent parameters. For a nonprincipal unit root , the simple root of the amplification polynomial of a multistep method satisfies
Reduction using gives
For small negative real , it follows that
So even a small negative real test parameter has an amplifying parasitic mode. None of the convergent parameters is A-stable. Parameters outside this range already fail the necessary zero-step root condition, so there is no real giving an A-stable original method.
The endpoint is worth keeping separate from a misleading cancellation: there and . The original amplification polynomial retains the double root one for every , giving unbounded polynomial parasitic modes for general starting values. Canceling would yield a different, reduced Backward Euler method; it does not stabilize the original recurrence. This is the common-factor cancellation defect in a multistep recurrence. The family is summarized by the reciprocal-root three-step multistep family.
Let be the periodic forward-shift matrix, with indices modulo . The semidiscrete operator is
For , , the vector satisfies . These vectors divided by form the unitary discrete Fourier transform basis. Hence the normal matrix has eigenvalues
Every Fourier coefficient is multiplied by of modulus at most one. Parseval's identity therefore proves
The same estimate holds for differences of solutions and is uniform in the mesh. This is the required semidiscrete stability of a numerical method. The mean mode has eigenvalue zero and is conserved, consistently with periodic advection.
One can also verify the energy estimate without diagonalization. Since ,
The dissipative second-order forward advection semidiscretization thus damps nonconstant modes rather than amplifying them. This does not assert stability of an arbitrary further time-stepping method; its absolute stability region must contain the scaled eigenvalues. No nonnegativity condition on the periodic boundary value is needed: the printed trailing specifies the time range.
Use the undirected graph of the symmetric off-diagonal sparsity pattern. Reading the original matrix gives the edges
This connected nine-vertex, eight-edge graph is a tree. A leaf-removal perfect elimination ordering is
The corresponding lists of remaining neighbors are, in order, . Thus simultaneous row and column permutation to this ordering allows Gaussian elimination without fill-in.
Here are the precise facts that justify the procedure. First, eliminating index makes the remaining entries
Only pairs of remaining neighbors of can acquire a new nonzero entry. Consequently a vertex whose remaining neighbors form a clique can be eliminated without structural fill. Such a vertex is simplicial. An ordering in which each successive vertex is simplicial is a perfect elimination ordering. The graph characterization says that a graph admits such an ordering if and only if it is a chordal graph, meaning every cycle of length at least four has a chord. A tree has no cycles; more concretely, every nontrivial finite tree has a leaf, and removing a leaf leaves a forest. These statements explain why successive leaf removal works here without having to find a general-purpose chordal ordering.
Second, for a symmetric positive-definite matrix, every diagonal pivot and every successive Schur complement is positive definite. For example, if the first pivot is and its coupling column is , then for any nonzero remaining vector ,
Thus no zero pivot requires abandoning the proposed ordering. Since each eliminated vertex here has at most one remaining neighbor, its Schur update changes only a diagonal entry. This proves the claimed fill-free LDL decomposition or Cholesky decomposition for every positive-definite matrix with the given pattern, including cases with additional accidental zeros. It is leaf elimination of a tree-pattern positive-definite matrix.

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