 Chapter 1: Speaking Mathematically
 Chapter 10: Graphs and Trees
 Chapter 2: THE LOGIC OF COMPOUND STATEMENTS
 Chapter 3: The Logic of Quantied Statements
 Chapter 4: Elementary Number Theory and Methods of Proof
 Chapter 5: Sequences, Mathematical Induction, and Recursion
 Chapter 6: Set Theory
 Chapter 7: Functions
 Chapter 8: Relations
 Chapter 9: Counting and Probability
Discrete Mathematics: Introduction to Mathematical Reasoning 1st Edition  Solutions by Chapter
Full solutions for Discrete Mathematics: Introduction to Mathematical Reasoning  1st Edition
ISBN: 9780495826170
Discrete Mathematics: Introduction to Mathematical Reasoning  1st Edition  Solutions by Chapter
Get Full SolutionsSince problems from 10 chapters in Discrete Mathematics: Introduction to Mathematical Reasoning have been answered, more than 29646 students have viewed full stepbystep answer. Discrete Mathematics: Introduction to Mathematical Reasoning was written by and is associated to the ISBN: 9780495826170. This textbook survival guide was created for the textbook: Discrete Mathematics: Introduction to Mathematical Reasoning, edition: 1. The full stepbystep solution to problem in Discrete Mathematics: Introduction to Mathematical Reasoning were answered by , our top Math solution expert on 01/04/18, 08:37PM. This expansive textbook survival guide covers the following chapters: 10.

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.

Cross product u xv in R3:
Vector perpendicular to u and v, length Ilullllvlll sin el = area of parallelogram, u x v = "determinant" of [i j k; UI U2 U3; VI V2 V3].

Exponential eAt = I + At + (At)2 12! + ...
has derivative AeAt; eAt u(O) solves u' = Au.

Free variable Xi.
Column i has no pivot in elimination. We can give the n  r free variables any values, then Ax = b determines the r pivot variables (if solvable!).

Fundamental Theorem.
The nullspace N (A) and row space C (AT) are orthogonal complements in Rn(perpendicular from Ax = 0 with dimensions rand n  r). Applied to AT, the column space C(A) is the orthogonal complement of N(AT) in Rm.

GaussJordan method.
Invert A by row operations on [A I] to reach [I AI].

Hessenberg matrix H.
Triangular matrix with one extra nonzero adjacent diagonal.

Least squares solution X.
The vector x that minimizes the error lie 112 solves AT Ax = ATb. Then e = b  Ax is orthogonal to all columns of A.

Left nullspace N (AT).
Nullspace of AT = "left nullspace" of A because y T A = OT.

Network.
A directed graph that has constants Cl, ... , Cm associated with the edges.

Nilpotent matrix N.
Some power of N is the zero matrix, N k = o. The only eigenvalue is A = 0 (repeated n times). Examples: triangular matrices with zero diagonal.

Pascal matrix
Ps = pascal(n) = the symmetric matrix with binomial entries (i1~;2). Ps = PL Pu all contain Pascal's triangle with det = 1 (see Pascal in the index).

Plane (or hyperplane) in Rn.
Vectors x with aT x = O. Plane is perpendicular to a =1= O.

Right inverse A+.
If A has full row rank m, then A+ = AT(AAT)l has AA+ = 1m.

Saddle point of I(x}, ... ,xn ).
A point where the first derivatives of I are zero and the second derivative matrix (a2 II aXi ax j = Hessian matrix) is indefinite.

Semidefinite matrix A.
(Positive) semidefinite: all x T Ax > 0, all A > 0; A = any RT R.

Singular matrix A.
A square matrix that has no inverse: det(A) = o.

Toeplitz matrix.
Constant down each diagonal = timeinvariant (shiftinvariant) filter.

Unitary matrix UH = U T = UI.
Orthonormal columns (complex analog of Q).

Vector space V.
Set of vectors such that all combinations cv + d w remain within V. Eight required rules are given in Section 3.1 for scalars c, d and vectors v, w.