N+1 Tests and the Lucas-Lehmer Test
Primality tests using the factorisation of one more than the candidate, and the Lucas-Lehmer test for Mersenne numbers.
Engineering Mathematics articles in the KEVOS Engineering library. 1073 pages.
Primality tests using the factorisation of one more than the candidate, and the Lucas-Lehmer test for Mersenne numbers.
Sums, scalar multiples and compositions of linear transformations are linear. The set of all such maps is a vector space, and matrix operations mirror each construction.
Sums, scalar multiples and compositions of linear transformations become sums, multiples and products of their matrix representations: multiplication is composition.
Using Newton polygons to decompose primes locally, handling the cases where factoring modulo p is insufficient.
Definition of a nonsingular square matrix via the trivial null space, the identity matrix, the row-reduction test and unique solutions for any right-hand side.
Proof that a square matrix is nonsingular exactly when it is invertible, via products with singular matrices and the sufficiency of a one-sided inverse.
Thirteen equivalent conditions on a square matrix: nonsingular, row-reduces to the identity, trivial null space, invertible, full rank, nonzero determinant.
Thirteen equivalent conditions on a square matrix: nonsingular, row-reduces to the identity, trivial null space, invertible, full rank, nonzero determinant.
The null space N(A) is the set of all vectors annihilated by a matrix: its definition, how to compute a spanning set from free variables, and why its dimension is n minus the rank.
Row reduction yields n-r linearly independent vectors spanning the null space of a matrix: a minimal, non-redundant basis for every homogeneous solution.
How the number field sieve achieves its complexity, why polynomial selection matters so much, and the role of number field arithmetic.
Number fields as finite extensions of the rationals, their embeddings and signature, and the presentation on which all computation depends.
The computer algebra systems and libraries implementing these algorithms, what each is suited to, and why implementing from scratch is usually the wrong choice.
Sum, intersection and quotient of subspaces, and how the analogous operations on modules over the integers require normal forms.
Orders as subrings that are full-rank lattices, the equation order, the maximal order, and the index that separates them.
Matrices whose adjoint is their inverse: the condition A*A = I, orthonormal columns, preservation of inner products and norms, adjoints and Hermitian matrices.
Orthogonality of complex vectors, orthogonal and orthonormal sets, why an orthogonal set of non-zero vectors is linearly independent, and how to test one.
How an orthonormal basis turns coordinate extraction into a single inner product, why every subspace has one, and how orthogonal matrices supply ready-made orthonormal bases.
Hermitian and real symmetric matrices diagonalise with a unitary or orthogonal change of basis: real eigenvalues, orthogonal eigenvectors and the spectral decomposition.
Finding roots in p-adic fields by lifting, and reading ramification structure off the Newton polygon of a polynomial.
Constructing pairwise independent hash families over finite fields and why the weaker independence suffices.
Where to obtain current key size recommendations, factoring records and post-quantum guidance, and why they are not reproduced here.
Every solution of a consistent linear system is one particular solution plus an element of the null space, so solution sets are affine translates of a subspace.
Detecting whether an integer is a perfect power, extracting the root, and why this precedes general factoring.
Congruences of polynomials modulo a fixed polynomial, and the parallel with integer congruences.
Euclidean division of polynomials, pseudo-division over a ring without inverses, and the coefficient growth pseudo-division introduces.
The three-stage pipeline used to factor polynomials over finite fields and the integers, and why the stages are ordered as they are.
Computing polynomial GCDs over the integers, the growth problem in remainder sequences, and the modular approach that sidesteps it.
Inverting a polynomial modulo another using the extended Euclidean algorithm, and the finite field application.
Schoolbook, Karatsuba and evaluation-interpolation methods for polynomial multiplication, and where the crossovers lie.
The structure of F[X]/(f), its basis, arithmetic, and the decomposition when f is reducible.
Dense and sparse polynomial representations, coefficient domains, and the normalisation invariants every implementation must maintain.
Evaluating a polynomial at a square matrix: matrix powers, why the constant term becomes a multiple of the identity, and why the factored form gives the same answer.
The distinction between a formal polynomial and the function it induces, and why the two differ over finite rings.
A linear system has no solution, exactly one, or infinitely many — never two. The trichotomy, its geometric reading, its proof, and what each case means in practice.
The pre-image of a vector under a linear transformation: when it is empty, why a non-empty pre-image is a translate of the kernel, and how it partitions the domain.
What a primality certificate is, why verification is cheaper than production, and what a certificate does and does not guarantee.
Why proving compositeness is easy, proving primality is harder, and factoring is harder still — and what this asymmetry means in practice.
How rational primes split, remain inert or ramify in a quadratic field, decided entirely by the Kronecker symbol.
Decomposing a prime that does not divide the index, by factoring the defining polynomial modulo that prime.
How rational primes factor in the maximal order, ramification indices and residue degrees, and the degree relation that constrains them.
Dirichlet's theorem on primes in arithmetic progressions, equidistribution across residue classes, and computational consequences.
The model of randomised computation, Las Vegas and Monte Carlo algorithms, and what a probabilistic guarantee means.
The standard proof patterns of linear algebra: definitions as two-way implications, set equality, equivalences, contrapositives, uniqueness, chains of identities and TFAE cycles.
Transpose invariance, multiplicativity over matrix products, and the equivalence between a zero determinant and singularity, with the consequences for inverses and rank.
Eigenvalue theorems: independence for distinct eigenvalues, zero eigenvalues and singularity, and the spectra of powers, polynomials, inverses and transposes.
Algebraic rules for matrix inverses: uniqueness, the reversal rule for products, inverse of an inverse, inverse of a transpose and of a scalar multiple.
Associativity, distributivity, the identity and zero matrices, scalar rules, and how transposition, conjugation and the inner product interact with matrix products.