- Chapter 1: Mathematical Induction
- Chapter 10: FROM CAESAR CIPHER TO PUBLIC KEY CRYPTOGRAPHY
- Chapter 11: PERFECT NUMBERS
- Chapter 12: THE EQUATION x2 + y2 = z2
- Chapter 13: SUMS OF TWO SQUARES
- Chapter 14: THE FIBONACCI SEQUENCE
- Chapter 15: FINITE CONTINUED FRACTIONS
- Chapter 16: PRIMALITY TESTING AND FACTORIZATION
- Chapter 2: EARLY NUMBER THEORY
- Chapter 3: THE FUNDAMENTAL THEOREM OF ARITHMETIC
- Chapter 4: BASIC PROPERTIES OF CONGRUENCE
- Chapter 5: FERMAT'S LITTLE THEOREM AND PSEUDOPRIMES
- Chapter 6: THE SUM AND NUMBER OF DIVISORS
- Chapter 7: EULER'S PHI-FUNCTION
- Chapter 8: THE ORDER OF AN INTEGER MODULO n
- Chapter 9: EULER'S CRITERION
Elementary Number Theory 7th Edition - Solutions by Chapter
Full solutions for Elementary Number Theory | 7th Edition
Big formula for n by n determinants.
Det(A) is a sum of n! terms. For each term: Multiply one entry from each row and column of A: rows in order 1, ... , nand column order given by a permutation P. Each of the n! P 's has a + or - sign.
Characteristic equation det(A - AI) = O.
The n roots are the eigenvalues of A.
A = CTC = (L.J]))(L.J]))T for positive definite A.
Column picture of Ax = b.
The vector b becomes a combination of the columns of A. The system is solvable only when b is in the column space C (A).
Complete solution x = x p + Xn to Ax = b.
(Particular x p) + (x n in nullspace).
Determinant IAI = det(A).
Defined by det I = 1, sign reversal for row exchange, and linearity in each row. Then IAI = 0 when A is singular. Also IABI = IAIIBI and
Diagonalizable matrix A.
Must have n independent eigenvectors (in the columns of S; automatic with n different eigenvalues). Then S-I AS = A = eigenvalue matrix.
Echelon matrix U.
The first nonzero entry (the pivot) in each row comes in a later column than the pivot in the previous row. All zero rows come last.
Ellipse (or ellipsoid) x T Ax = 1.
A must be positive definite; the axes of the ellipse are eigenvectors of A, with lengths 1/.JI. (For IIx II = 1 the vectors y = Ax lie on the ellipse IIA-1 yll2 = Y T(AAT)-1 Y = 1 displayed by eigshow; axis lengths ad
Hermitian matrix A H = AT = A.
Complex analog a j i = aU of a symmetric matrix.
Left inverse A+.
If A has full column rank n, then A+ = (AT A)-I AT has A+ A = In.
Linear transformation T.
Each vector V in the input space transforms to T (v) in the output space, and linearity requires T(cv + dw) = c T(v) + d T(w). Examples: Matrix multiplication A v, differentiation and integration in function space.
Matrix multiplication AB.
The i, j entry of AB is (row i of A)·(column j of B) = L aikbkj. By columns: Column j of AB = A times column j of B. By rows: row i of A multiplies B. Columns times rows: AB = sum of (column k)(row k). All these equivalent definitions come from the rule that A B times x equals A times B x .
Every v in V is orthogonal to every w in W.
Rank r (A)
= number of pivots = dimension of column space = dimension of row space.
Spectral Theorem A = QAQT.
Real symmetric A has real A'S and orthonormal q's.
Transpose matrix AT.
Entries AL = Ajj. AT is n by In, AT A is square, symmetric, positive semidefinite. The transposes of AB and A-I are BT AT and (AT)-I.
Unitary matrix UH = U T = U-I.
Orthonormal columns (complex analog of Q).
Vandermonde matrix V.
V c = b gives coefficients of p(x) = Co + ... + Cn_IXn- 1 with P(Xi) = bi. Vij = (Xi)j-I and det V = product of (Xk - Xi) for k > i.
v + w = (VI + WI, ... , Vn + Wn ) = diagonal of parallelogram.