Linear Independence and Nonsingular Matrices
Why a square matrix is nonsingular exactly when its columns are linearly independent, the chained proof, and the growing list of nonsingular equivalences.
Engineering Mathematics articles in the KEVOS Engineering library. 1073 pages.
Why a square matrix is nonsingular exactly when its columns are linearly independent, the chained proof, and the growing list of nonsingular equivalences.
Relations of linear dependence and linear independence in any vector space: the definitions, the reduction to a homogeneous system, and worked tests in matrix and polynomial spa…
Relations of linear dependence, the definition of a linearly independent set of column vectors, and the homogeneous-system and rank tests that decide it.
The definition of a linear transformation between vector spaces: additivity, homogeneity, the zero-to-zero theorem, and how to tell a linear map from an affine one.
A linear transformation passes through any linear combination, so its values on a basis determine it everywhere. The superposition theorem and its use in prediction.
Sequences satisfying linear recurrences, their minimal polynomials, and the equivalence with rational generating functions.
Extending LLL to generating sets that are not independent, and using the resulting zero vectors to extract relations.
Why LLL terminates in polynomial time and what its output guarantees, via the potential function and the Lovasz condition.
Allowing a vector to move further than one position, the quality gain, and the loss of the polynomial time guarantee.
Using lattice reduction to control entry growth during Hermite normal form computation, and when this beats the modular approach.
Lucas sequences, the Lucas probable prime test, and why it complements the strong pseudoprime test rather than duplicating it.
The random access machine and Turing machine models, what counts as a primitive operation, and how the choice of model affects stated complexities.
Markov's and Chebyshev's inequalities, their proofs, and how they bound deviation from the mean.
The notational conventions, symbol set and standing assumptions used throughout the KEVOS computational number theory collection.
What a matrix is, the entry notation for row and column, and how appending the constants column turns a system of linear equations into a single rectangular array.
The correspondence between matrices and linear maps, change of basis, and why the correspondence depends on a choice.
Every matrix defines a linear transformation, and every linear transformation on column vectors comes from one unique matrix built from images of the unit vectors.
Entrywise complex conjugation of a matrix, how it respects addition and scalar multiplication, commutation with the transpose, and the adjoint or conjugate transpose.
Matrices over a ring, their arithmetic, and the properties that survive when the base ring is not a field.
Column vectors, the zero vector, coefficient matrix, vector of constants and solution vector: the notation that compresses a whole system of equations into the symbol LS(A,b).
Entry-by-entry definitions of matrix equality, matrix addition and scalar multiplication on the set of m by n matrices, with worked examples and engineering use.
Matrix multiplication defined column by column through the matrix-vector product, the inner dimension rule, why the operation is not commutative, and what it means.
The entry formula for a matrix product: each entry of AB sums over the shared dimension, why it follows from the column definition, and what it costs.
Dense and sparse matrix representations, the cost model for exact linear algebra, and why coefficient growth rather than operation count usually decides performance.
Changing basis converts one matrix representation of a transformation into another by a similarity transformation, which is why eigenvalues and rank are intrinsic.
An invertible linear transformation has an invertible matrix representation, and the representation of its inverse is the matrix inverse with the bases exchanged.
How a linear transformation between abstract vector spaces becomes a matrix: the definition of a matrix representation, the fundamental theorem, and basis choice.
Definition of the matrix transpose, symmetric matrices, why symmetric matrices are square, and how the transpose interacts with matrix addition and scalar multiplication.
Min-entropy, randomness extraction, and the leftover hash lemma that converts weak randomness into near-uniform bits.
Mertens' theorems on sums and products over primes, and their role in estimating smoothness probabilities.
Unconditionally secure message authentication from universal hash families, and how forgery probability is bounded.
Bounding the coefficients of any factor of an integer polynomial, and why such a bound makes modular and lifting methods complete algorithms.
Bounds on the norm of ideals needed to generate the class group, and why the conditional bound is what makes computation practical.
Comparing factoring methods by target size, expected factor size and available hardware, with a practical sequencing recommendation.
Modular reduction strategies, Montgomery representation, and how trading division for multiplication accelerates every exponentiation.
Modular functions, modular equations relating j-invariants of isogenous curves, and their use in locating isogenies.
Square-and-multiply exponentiation, its cost, windowed variants, and the side-channel hazards of the naive form.
Combining modular inversion with Chinese remaindering to move computations between a composite modulus and its coprime factors.
Computing modular inverses, and Montgomery's trick for inverting many elements at the cost of one inversion plus multiplications.
Module homomorphisms, kernels and images, and the isomorphism theorems in their module form.
Representing ideals and modules as Hermite normal form matrices relative to an integral basis, with a common denominator.
Modules over a commutative ring, the generalisation of vector spaces, and what changes when scalars need not be invertible.
The modulus of a complex number: definition as the square root of c times its conjugate, multiplicativity, the triangle inequality, and its role in norms and spectral radius.
Polynomials in several variables, total and partial degree, and the structural differences from the univariate case.
Carry and borrow propagation, sign handling, and why addition sets the baseline cost against which every other operation is measured.
Knuth's division algorithm, the normalisation step that makes quotient digit estimation reliable, and why division carries a larger constant than multiplication.
How arbitrary-precision integers are stored, why the base is chosen to match the machine word, and the consequences for every algorithm above.
Shamir's threshold secret sharing, its information-theoretic security, and the independence property that underlies it.