On the nonzero axis the function equals one, whereas its assigned value at the origin is zero. Therefore does not tend to as . It is not a continuous function there, and differentiability implies continuity shows
Put . Since , the nonzero-point expression satisfies
Thus the defining remainder condition for differentiability holds with the zero linear map:
The estimate controls every approach direction at once, which is stronger than merely finding the two zero partial derivatives.
The dual constraints and objective coefficients on are unchanged, so remains dual feasible. Since every component of is positive, complementary slackness forces every constraint of any primal optimum paired with it to bind. Let be the original three-by-three constraint matrix and . It is invertible, and the only possible paired primal point is
If this point is nonnegative, it is primal feasible with zero slacks and , proving optimal. Conversely, if is optimal, linear programming duality supplies a paired primal optimum, and complementary slackness forces it to be exactly this point. This proves the strictly positive dual certificate and right-hand-side sensitivity criterion:
Non-strict inequalities are essential: boundary cases can be degenerate but retain the same optimal dual vector. This is the complete range, not just a small-perturbation sufficient condition for linear programming sensitivity within a fixed optimal basis.
The dual linear program is
The negatives of the final slack payoff coefficients give . Its three dual constraints are equalities, and its objective is . Equality with the primal objective proves optimality by weak duality, independently of tableau sign conventions. It is also the complementary slackness certificate, with all primal variables and all dual variables strictly positive.
Continue the simplex algorithm using dictionaries, with nonbasic variables zero at the current vertex. The given basis has payoff
Thus enters. Its positive row coefficients give ratios for and for ; the row does not restrict this increase. Therefore leaves. After that pivot,
Now enters. Only the row decreases along this move, and its ratio is , so leaves. The final dictionary is
All reduced payoff coefficients are nonpositive. Since the slacks must be nonnegative, , attained with . Hence . Direct substitution makes all three original constraints equalities. The final dictionary is the optimal tableau expressed with basic variables on the left, not just a proposed feasible vertex.
The polynomial satisfies the Eisenstein criterion at the prime two: every nonleading coefficient is even, the leading coefficient is not, and the constant term is not divisible by four. Hence it is irreducible over . Its ideal is maximal in ; explicitly, for any polynomial not divisible by , the Euclidean algorithm gives , furnishing the inverse of its residue class. Thus
For the integer quotient, division by the monic polynomial gives a unique remainder of degree below five with integer coefficients. If an integer polynomial becomes zero in the rational quotient, its integer remainder is still divisible by over , so must be zero. This gives an injective ring map , proving the monic irreducible integer-polynomial quotient is a domain.
The same unique-remainder argument makes the integer quotient a free abelian group with basis . The class of two is nonzero and cannot have an inverse: twice an integer remainder cannot equal the remainder one. Consequently
A nonzero primitive polynomial in has greatest common divisor of its coefficients equal to one. Suppose primitive polynomials had a product whose coefficients were all divisible by some prime . Their reductions in are both nonzero, because neither polynomial has every coefficient divisible by . A polynomial ring over a field is an integral domain, so their product is nonzero modulo , a contradiction. Hence the product of primitive polynomials is primitive.
To deduce unique factorization, write any nonzero integer polynomial as its positive polynomial content times a primitive polynomial, with its sign treated as a unit. The preceding result gives multiplicativity of content. Factor the primitive part over the Euclidean polynomial ring , and rescale every irreducible rational factor to a primitive integer polynomial. If with all polynomials primitive and in lowest terms, the equality has contents and . Thus , forcing . The rational factorization therefore lifts to an integer factorization.
A primitive integer polynomial is irreducible over exactly when it is irreducible over : an integer factorization gives a rational one, and a rational factorization lifts by the same primitive-content argument. Factor the content into integer primes. Existence and uniqueness over the integers and over now give existence and uniqueness of the resulting factorization into constant prime factors and primitive irreducible polynomial factors, up to order and signs. Therefore . This is the full lifting step in Gauss lemma for polynomials.
Use first-kind Chebyshev polynomials , characterized by . Substituting converts the weighted inner product into . Hence these polynomials are orthogonal, with squared norms for and for .
For the specified function, . Its constant projection coefficient is . For the coefficient is . Symmetry about makes it zero for odd . For , the product-to-sum identity gives
Therefore the Chebyshev projection of a semicircle is
In particular the best degree-at-most- polynomial is even, so for odd its actual degree is at most .
Take nonzero of exact degree , so are an orthogonal basis of the polynomials of degree at most . Their orthogonal projection is
Indeed its residual is orthogonal to every such polynomial. For another polynomial of degree at most , the squared error is
since the cross term vanishes. It is uniquely minimized at . The approximation need not have exact degree when the highest coefficient happens to vanish.
Suppose two distinct minimizers have the same minimum distance from . Convexity puts their midpoint in . Expanding the Euclidean norm gives
contradicting minimality. Therefore the nearest point is unique. Together with the preceding existence proof, this defines the metric projection onto a nonempty closed convex set.
Choose and put . The intersection is nonempty, closed and bounded in , hence compact. The continuous distance function attains a minimum at some . For its value is strictly greater than , while the value at is at most that at , namely . Thus this minimum on is a minimum on all of :
The set itself need not be bounded; only the compact portion needed to find the nearest point is bounded.
The cyclic matrix acts by . For each fourth root of unity , the vector satisfies , because . This supplies the four distinct eigenvalues and nonzero eigenvectors:
Eigenvectors for distinct eigenvalues are linearly independent. Thus, with these columns in the indicated order,
They form a basis over , so there are no other eigenvalues. Equivalently the characteristic polynomial is .
For a complex square matrix, define its determinant by
The cofactor is , where deletes row and column . The adjugate is the transposed cofactor matrix, .
The entry of is . For , expansion along column gives . For , this same sum is the determinant of the matrix obtained by replacing column by column : deleting the replaced column leaves all its cofactors unchanged. The new matrix has two equal columns, so its determinant is zero. Thus
This proof does not assume invertibility and therefore also covers singular matrices.
A particle pathline follows the time-dependent velocity field, so its coordinates satisfy and . The second equation and the initial data give . Using the integrating factor in the first,
Therefore the complete pathline is
If , its first coordinate grows without bound in magnitude. If , the first coordinate remains bounded and periodic while . The unique release coordinate giving a bounded particle path is .
Integrating and imposing gives the streamfunction
up to an arbitrary function of time. At the specified time, the streamlines are
For , the connected branches are rectangular hyperbolas. The zero level consists of the lines and , with the stagnation point at their intersection; their half-lines and the stationary point are the distinct streamlines.
The PDF has the doubly exponential denominator . For its positive summands, the successive-term ratio is
Thus the ratio test proves absolute convergence. Concretely, the tail is eventually dominated by a geometric series of ratio ; the polynomial numerator cannot offset the doubly exponential decay.
Let denote the summand. Its denominator is positive for every : odd gives , while even gives . Dividing numerator and denominator by their highest powers gives
The terms are positive for sufficiently large , so the limit comparison test with the divergent harmonic series gives divergence to . More explicitly, for the numerator is at least and the denominator is at most , giving . Any finitely many early negative terms change partial sums by only a finite constant and cannot prevent divergence of this positive tail.
For odd , the denominator is ; for even , it is . There is no zero denominator in the printed index range. For all ,
The comparison test for series with the convergent p-series gives absolute convergence. In fact the consecutive pair contributes , so the sum is the same as . Positivity justifies this paired reordering; an alternating-sign appearance in the denominator does not make this an alternating series.
For , . The upper bound has telescoping partial sums, bounded by one. The positive partial sums of the original series are consequently bounded by two and increasing, so they converge as a monotone bounded sequence. The series converges absolutely. This also supplies the case of a p-series without requiring its exact sum.
The cubic terms are the expansion of . Subtracting therefore gives
Here the expression is a polynomial in and its complex conjugate, rather than a holomorphic polynomial in alone.

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