The linear Diophantine equation solvability criterion says that has an integer solution exactly when divides . The Euclidean algorithm gives
Thus , which divides . Therefore
Applying the Euclidean algorithm,
Hence . Since does not divide , the linear Diophantine equation solvability criterion gives
The quadratic form factors as a sum of two squares:
The change of variables
has determinant , so its matrix is a unimodular matrix and it maps bijectively to itself. The original equation is therefore equivalent to
The sum of two squares theorem also decides this systematically: , and every prime factor congruent to modulo occurs to an even power, vacuously here. Thus is a sum of two integer squares. Explicitly,
Inverting the change of variables gives, for example, . Consequently
This is the unimodular reduction of a discriminant-minus-four form to two squares.
The Liouville approximation theorem states that if is an irrational algebraic number of degree , then there is a constant such that every reduced rational satisfies
Let be the minimal polynomial of , of degree . Since is irreducible of degree greater than one, . Moreover,
and hence
On the compact interval , put
If , the mean value theorem gives a point between and such that
Thus . If , then . Taking any
proves the stated strict inequality.
Because both fractions are reduced and , they are distinct. Their difference is
The numerator is a nonzero integer, so its absolute value is at least one. Therefore
where the strict inequality uses . This is the separation of distinct reduced fractions.
Since the positive series converges, . We construct the subsequence recursively. Choose arbitrarily. Once have been chosen, write the reduced partial sums as
Choose so far out that
This is possible because .
Let
For every fixed , each later choice includes in its minimum, so
First, is irrational. If were reduced and rational, the strictly increasing rational sequence would have unbounded denominators ; only finitely many reduced fractions in a bounded interval have bounded denominator. Choose with . Part (b) would give
contrary to .
If were algebraic of degree , part (a) would give a constant with
For our construction instead gives
which contradicts the lower bound once . Hence is transcendental. This proves the transcendental subseries of a positive rational convergent series construction.
A binary code is a prefix code when no codeword is a proper prefix of another. If the source letters have probabilities and codeword lengths , the expected value of its codeword length is
It is an optimal prefix code when it minimizes among all binary prefix codes for that source.
Suppose but . Exchanging the codewords assigned to and leaves the set of words, and hence prefix-freeness, unchanged. The change in expected length is
contradicting optimality. Thus
which is the probability monotonicity of optimal prefix-code lengths.
Represent the code by its binary prefix tree and choose a codeword of maximal length, where . Its sibling is . If that sibling were not a codeword, it could not have a codeword below it either, since such a word would be longer than . Replacing by would then preserve prefix-freeness and strictly reduce . This is impossible for an optimal code, so is also a maximal-length codeword. The pair differs only in its last digit, proving the deepest sibling property of an optimal prefix code.
The Huffman coding merges may be chosen as
Resolving the tied weights so that the original symbol is merged only at the final step gives the code
Its lengths are and its expected length is
The Huffman coding theorem proves optimality, so an optimal coding with all but one word of the same length does exist. This is an instance of a nonunique optimal prefix code: a different resolution of the ties also gives the optimal profile .
An optimal code cannot have five distinct lengths. By the deepest sibling property of an optimal prefix code, at least two maximal-length codewords have the same length. Thus the answers are respectively
A context-free grammar is in Chomsky normal form when every production is of one of the forms
where are nonterminal symbols and is a terminal symbol. The conventional exceptional production may be allowed when the language contains the empty word, with excluded from right-hand sides.
The length is
Indeed, the parse tree has terminal-production nodes and, as a full binary tree with leaves, binary-production nodes. This is the Chomsky-normal-form derivation length.
One parse tree can be recorded unambiguously by the nested expression
Every internal node uses one of the given productions, and the leaves read from left to right are
Equivalently, the corresponding leftmost grammar derivation is
Hence the required word is generated by the grammar.
Take
where
This grammar is in Weak Chomsky normal form. In the original grammar, occurs only in and has the sole production . Thus every use of
is replaced in by , while every later production is unchanged. Conversely, the ternary production can always be expanded into those two original steps. Therefore is equivalent to in the sense of an equivalent grammar.
The compressed initial production gives the leftmost derivation
It has steps, whereas part (b) gives for every derivation in Chomsky normal form. The Weak Chomsky normal form ternary rule has compressed exactly one pair of binary productions, so the derivation is strictly shorter as required.
For , the last flip must be the th head. Among the preceding flips, exactly are tails and are heads. There are choices for their positions, and every resulting sequence has probability . The negative binomial stopping argument therefore gives
so has the failures-before-the-th-success negative binomial distribution.
To put the mass function into exponential family form, set
Since ,
where
Thus the natural parameter of an exponential family is , and the Fisher-Neyman factorization theorem shows that is a sufficient statistic. This is the negative binomial exponential family.
The exponential-family derivative identities now give
and
With population , the transition diagram has the two outgoing arrows
Thus this is a batch-birth linear-death process. If and for , probability enters state from by a birth and from by a death. Hence the master equation is
The Markov jump-process generator is
For , this gives . Therefore the expected value satisfies
so
For ,
Writing and using the generator identity gives
The variance consequently obeys
If , substitution of the formula for yields
All transient terms vanish, and the moments of a batch-birth linear-death process therefore give
Taking reciprocals in the finite product defining the gamma function gives
Let . Since
and , where is the Euler--Mascheroni constant, passage to the limit gives the Weierstrass product for the reciprocal gamma function
The logarithmic derivative of this identity is
Since , the digamma function therefore satisfies
For real , termwise differentiation gives the trigamma function
Thus is strictly increasing on the positive real axis. At the two positive integers needed here, telescoping gives
The intermediate value theorem supplies a zero in , and strict increase makes it unique. This is the positive zero of the digamma function.
Under the stated gauge transformation, the charged-particle electromagnetic Lagrangian becomes
Thus
The action changes only by , whose variation vanishes for fixed endpoints. By total-time-derivative invariance of a Lagrangian, the Euler-Lagrange equation is unchanged.
In Cartesian components,
and
The Euler-Lagrange equation therefore gives
Using
and , this becomes the Lorentz force
Let . For a constant uniform magnetic field, choose the symmetric gauge
which has and . With , one may take . The coordinate is then absent from the Lagrangian, so it is an ignorable coordinate. Its generalized momentum
is conserved. Hence
which is the canonical momentum parallel to a uniform magnetic field in this gauge.
For a comoving region, and . The work law gives
so differentiation with respect to time yields
Since , division by gives the cosmological perfect-fluid continuity equation
Put . Differentiating gives
The continuity equation and the Friedmann acceleration equation imply
and
These terms cancel, so
This is the First integral of the Friedmann acceleration equation.
For the barotropic equation of state , the continuity equation separates as
Since ,
which is the constant-equation-of-state density scaling.
When , the expanding branch satisfies
where . Because , integration gives
and hence the flat constant-equation-of-state scale factor
It vanishes at
as ensures .
Apply the hypothetical quantum cloning device repeatedly to obtain copies of the unknown state. If , the two possibilities are
whose overlap has modulus . For equal prior probabilities, the Helstrom-Holevo bound gives optimal success probability
Thus, for any , sufficiently many clones permit success probability above . Orthogonal states already have and can be distinguished perfectly. This is clone-assisted asymptotic state discrimination.
Here distinct states means distinct rays. Vectors differing only by a global phase represent the same quantum state and cannot be distinguished.
Run the perfect discriminator on the supplied state and record whether the result is or . For each , choose a unitary satisfying
which is possible by extending to an orthonormal basis. Prepare two fresh systems in and, controlled by the classical outcome , apply . The output is
The discriminator may destroy its input; the two freshly prepared outputs still implement cloning on this known pair. This is the construction that perfect discrimination implies cloning for a known state family.
Suppose first that the stated unitary and environment states exist. Preservation of the inner product gives
The environment states are normalized, so the Cauchy-Schwarz inequality gives . Therefore
Conversely, write
and assume . If , put , so , and choose
Then and the desired output vectors have inner product , exactly matching the input vectors. If , the inequality forces , and one may take .
In either case the input pair and output pair have the same Gram matrix. The isometry taking one pair to the other extends to a unitary matrix, so the required exists. Hence
which is the environment-assisted two-state pure-state transformation criterion.
A binary symmetric channel independently flips each input bit with probability , so, with rows indexed by the input and columns by the output, its channel matrix is
If , complementing every received bit converts the channel into one with crossover probability without changing its information-carrying ability. The case has identical rows and capacity zero, so it suffices to study .
The Shannon second coding theorem states that for every rate below the channel capacity there are arbitrarily long codes whose decoding error tends to zero, whereas no sequence of codes with rate above capacity can have vanishing error. It also identifies
For the present channel,
The output entropy is at most one bit, with equality for a uniform input. Hence the binary symmetric channel capacity is
bits per channel use.
Let denote whether or was sent, and write
where are independent Bernoulli variables of parameter . Because is uniform, is uniform, while . Thus the mutual information in the first received digit is
By the chain rule for mutual information, the extra information in the second digit is . Conditional on , the second channel error is independent of the first output, so
Moreover,
Given either value of , the second output therefore differs from it with probability , and
Consequently the incremental information in a twofold binary repetition code is
bits.
Every row of the channel matrix is a permutation of , and every column sum is one. It is therefore a weakly symmetric channel. The uniform input makes the output uniform, with entropy , while the conditional output entropy is the entropy of one row:
The weakly symmetric channel capacity formula now gives the ternary symmetric channel capacity
bits per channel use, for , with the convention .
The relation means that there is a total computable function such that
for every word . Such a is a many-one reduction.
A set is -complete under many-one reductions when
The first condition is membership in the class; the second is -hardness. This is a many-one complete set.
A set is a Sigma-1 set when it is the domain of a partial computable function: there is a partial computable such that
Equivalently, iff for some computable predicate ; one may take to say that the truncated computation function observes a halt within steps.
For an effective enumeration of the computably enumerable languages, the diagonal halting problem is
Equivalently, if is the unary partial function coded by , then
Thus is the diagonal halting set.
First, : the partial procedure on input simulates and halts exactly when that computation halts.
For hardness, let . Choose a partial computable function with
For each fixed , define a unary program as follows: on any input , simulate ; if that simulation halts, halt and output , and otherwise run forever. The S-m-n theorem gives a total computable function that maps to a code for . Its accepted language is
In particular,
Thus is a many-one reduction . Since was arbitrary, is -hard; together with membership this proves the many-one completeness of the halting problem.
Membership is immediate from the supplied characterization of . Define the partial computable function
Then
so .
Now let . There is a partial computable such that
For each , define a unary partial function
Operationally, its program simulates and returns if that computation halts. By the S-m-n theorem, a total computable map produces an index for . Therefore
Thus every Pi-2 set many-one reduces to . Combined with membership, this proves that the totality problem is -complete.
Write
where and are independent, centered, and have covariance matrix . Then
The deterministic term is orthogonal in expectation to the two centered random terms, and the random terms are independent. Since the hat matrix is a symmetric idempotent projection of rank ,
while . Therefore
In this bias-variance decomposition for linear prediction, is squared model bias, is variance from fitting coefficients, and is irreducible noise in the future response. Enlarging the model tends to reduce the first term while increasing the fitted-model variance, which is the bias-variance tradeoff.
Because is an orthogonal projection of rank ,
It follows that Mallows Cp satisfies
where the last equality is part (i). Thus is an unbiased estimator of the independent-copy prediction error, as summarized by the unbiased prediction-error identity for ordinary least squares.
Let
The pivotal quadratic form has an distribution, so the normal linear-model confidence ellipsoid is
Here is the quantile of the indicated distribution.
Let be the least-squares estimate after deleting observation , and put and . Cook's distance is
Thus is the squared displacement caused by deleting observation , measured in the same metric and scale as the confidence ellipsoid. In particular, lies outside the ellipsoid centered at exactly when
Here and . The supplied quantile gives
Since
the coefficient estimate after deleting this observation lies outside the confidence ellipsoid centered at the full-data estimate. It also greatly exceeds the common rough reference value . The observation is therefore highly influential and merits investigation:
After division by , the coefficient functions are
Both and are analytic at zero, so the regular singular point criterion for a second-order equation shows that is regular singular. Substitution of into the leading terms gives the indicial equation
The exponent is repeated. Directly substituting gives
Thus determines the entire power series, so there is only one such solution up to scale. The logarithmic solution from a repeated Frobenius exponent predicts
so its leading nonanalytic term is proportional to .
For the contour ansatz, differentiation under the integral and one integration by parts give
The integral vanishes when
whose solution is the Laguerre contour-integral amplitude
The contour and branches must make single-valued along the traversed path and must kill the endpoint term
Now suppose is nonintegral. Near , is integrable and the endpoint factor is . We may therefore choose
a finite loop based at the branch point and avoiding except by encirclement. A second choice is
along one bank of the negative real axis, with a consistent branch. At , kills the algebraic endpoint factor because .
The integral is analytic in because its contour is finite, and it is a nonzero solution. By uniqueness of the analytic local solution, it is a constant multiple of . This is the Finite Laguerre contour solution construction.
Let be nonintegral. This time is integrable at , and the endpoint factor behaves as . Suitable contours are therefore
and
where the second path makes a fixed detour above or below the branch point and the branch of is continued along it. The exponential again controls the endpoint at for .
As in part (i), the finite integral is analytic near zero and hence is a constant multiple of the unique power-series solution .
Write with . The amplitude is now the rational function
Choose to be a small positively oriented circle around the pole , and choose from along the negative real axis to . At zero the endpoint factor behaves as and vanishes; at negative infinity the exponential decays.
By the residue theorem, the finite-contour solution is
Every derivative term contains times a power of of degree at most , and the highest-degree term is nonzero. Hence
up to a constant, where is a polynomial of degree . This is the negative-integer case of the Integer-parameter Laguerre contour residues.
Write with . Then
Take to be a small positively oriented circle around . For , start at , travel to , and avoid the pole at zero by a fixed upper or lower detour. The endpoint factor vanishes at , and the exponential controls .
The finite contour extracts the residue at zero:
Only powers occur, and the coefficient of is nonzero. Thus this solution is a polynomial of degree and, up to normalization, is the Laguerre polynomial :
This also follows from the recurrence in part (i), because and every later coefficient vanishes.
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