 Chapter 1.1:
 Chapter 1.1: Variables
 Chapter 1.2:
 Chapter 1.2: The Language of Sets
 Chapter 1.3:
 Chapter 1.3: The Language of Relations and Functions
 Chapter 10.1:
 Chapter 10.1: Graphs: Definitions and Basic Properties
 Chapter 10.2:
 Chapter 10.2: Trails, Paths, and Circuits
 Chapter 10.3:
 Chapter 10.3: Matrix Representations of Graphs
 Chapter 10.4:
 Chapter 10.4: Isomorphisms of Graphs
 Chapter 10.5:
 Chapter 10.5: Trees
 Chapter 10.6:
 Chapter 10.6: Rooted Trees
 Chapter 10.7:
 Chapter 10.7: Spanning Trees and Shortest Paths
 Chapter 11.1:
 Chapter 11.1: RealValued Functions of a Real Variable and Their Graphs
 Chapter 11.2:
 Chapter 11.2: O, , and Notations
 Chapter 11.3:
 Chapter 11.3: Application: Analysis of Algorithm Efficiency I
 Chapter 11.4:
 Chapter 11.4: Exponential and Logarithmic Functions: Graphs and Orders
 Chapter 11.5:
 Chapter 11.5: Application: Analysis of Algorithm Efficiency II
 Chapter 12.1:
 Chapter 12.1: Formal Languages and Regular Expressions
 Chapter 12.2:
 Chapter 12.2: FiniteState Automata
 Chapter 12.3:
 Chapter 12.3: Simplifying FiniteState Automata
 Chapter 2.1:
 Chapter 2.1: Logical Form and Logical Equivalence
 Chapter 2.2:
 Chapter 2.2: Conditional Statements
 Chapter 2.3:
 Chapter 2.3: Valid and Invalid Arguments
 Chapter 2.4:
 Chapter 2.4: Application: Digital Logic Circuits
 Chapter 2.5:
 Chapter 2.5: Application: Number Systems and Circuits for Addition
 Chapter 3.1: Predicates and Quantified Statements I
 Chapter 3.2:
 Chapter 3.2: Predicates and Quantified Statements II
 Chapter 3.3:
 Chapter 3.3: Statements with Multiple Quantifiers
 Chapter 3.4:
 Chapter 3.4: Arguments with Quantified Statements
 Chapter 4.1:
 Chapter 4.1: Direct Proof and Counterexample I: Introduction
 Chapter 4.2:
 Chapter 4.2: Direct Proof and Counterexample II: Rational Numbers
 Chapter 4.3:
 Chapter 4.3: Direct Proof and Counterexample III: Divisibility
 Chapter 4.4:
 Chapter 4.4: Direct Proof and Counterexample IV: Division into Cases and the QuotientRemainder Theorem
 Chapter 4.5:
 Chapter 4.5: Direct Proof and Counterexample V: Floor and Ceiling
 Chapter 4.6:
 Chapter 4.6: Indirect Argument: Contradiction and Contraposition
 Chapter 4.7:
 Chapter 4.7: Indirect Argument: Two Classical Theorems
 Chapter 4.8:
 Chapter 4.8: Application: Algorithms
 Chapter 5.1:
 Chapter 5.1: Sequences
 Chapter 5.2:
 Chapter 5.2: Mathematical Induction I
 Chapter 5.3:
 Chapter 5.3: Mathematical Induction II
 Chapter 5.4:
 Chapter 5.4: Strong Mathematical Induction and the WellOrdering Principle for the Integers
 Chapter 5.5:
 Chapter 5.5: Application: Correctness of Algorithms
 Chapter 5.6:
 Chapter 5.6: Defining Sequences Recursively
 Chapter 5.7:
 Chapter 5.7: Solving Recurrence Relations by Iteration
 Chapter 5.8:
 Chapter 5.8: SecondOrder Linear Homogeneous Recurrence Relations with Constant Coefficients
 Chapter 5.9:
 Chapter 5.9: General Recursive Definitions and Structural Induction
 Chapter 6.1:
 Chapter 6.1: Set Theory: Definitions and the Element Method of Proof
 Chapter 6.2:
 Chapter 6.2: Properties of Sets
 Chapter 6.3:
 Chapter 6.3: Disproofs, Algebraic Proofs, and Boolean Algebras
 Chapter 6.4:
 Chapter 6.4: Boolean Algebras, Russells Paradox, and the Halting Problem
 Chapter 7.1:
 Chapter 7.1: Functions Defined on General Sets
 Chapter 7.2:
 Chapter 7.2: OnetoOne and Onto, Inverse Functions
 Chapter 7.3:
 Chapter 7.3: Composition of Functions
 Chapter 7.4:
 Chapter 7.4: Cardinality with Applications to Computability
 Chapter 8.1:
 Chapter 8.1: Relations on Sets
 Chapter 8.2:
 Chapter 8.2: Reflexivity, Symmetry, and Transitivity
 Chapter 8.3:
 Chapter 8.3: Equivalence Relations
 Chapter 8.4:
 Chapter 8.4: Modular Arithmetic with Applications to Cryptography
 Chapter 8.5:
 Chapter 8.5: Partial Order Relations
 Chapter 9.1:
 Chapter 9.1: Introduction
 Chapter 9.2:
 Chapter 9.2: Possibility Trees and the Multiplication Rule
 Chapter 9.3:
 Chapter 9.3: Counting Elements of Disjoint Sets: The Addition Rule
 Chapter 9.4:
 Chapter 9.4: The Pigeonhole Principle
 Chapter 9.5:
 Chapter 9.5: Counting Subsets of a Set: Combinations
 Chapter 9.6:
 Chapter 9.6: rCombinations with Repetition Allowed
 Chapter 9.7:
 Chapter 9.7: Pascals Formula and the Binomial Theorem
 Chapter 9.8:
 Chapter 9.8: Probability Axioms and Expected Value
 Chapter 9.9:
 Chapter 9.9: Conditional Probability, Bayes Formula, and Independent Events
Discrete Mathematics with Applications 4th Edition  Solutions by Chapter
Full solutions for Discrete Mathematics with Applications  4th Edition
ISBN: 9780495391326
Discrete Mathematics with Applications  4th Edition  Solutions by Chapter
Get Full SolutionsThis expansive textbook survival guide covers the following chapters: 131. Since problems from 131 chapters in Discrete Mathematics with Applications have been answered, more than 13360 students have viewed full stepbystep answer. Discrete Mathematics with Applications was written by Sieva Kozinsky and is associated to the ISBN: 9780495391326. The full stepbystep solution to problem in Discrete Mathematics with Applications were answered by Sieva Kozinsky, our top Math solution expert on 07/19/17, 06:34AM. This textbook survival guide was created for the textbook: Discrete Mathematics with Applications , edition: 4th.

Block matrix.
A matrix can be partitioned into matrix blocks, by cuts between rows and/or between columns. Block multiplication ofAB is allowed if the block shapes permit.

Companion matrix.
Put CI, ... ,Cn in row n and put n  1 ones just above the main diagonal. Then det(A  AI) = ±(CI + c2A + C3A 2 + .•. + cnA nl  An).

Cramer's Rule for Ax = b.
B j has b replacing column j of A; x j = det B j I det A

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].

Cyclic shift
S. Permutation with S21 = 1, S32 = 1, ... , finally SIn = 1. Its eigenvalues are the nth roots e2lrik/n of 1; eigenvectors are columns of the Fourier matrix F.

Dot product = Inner product x T y = XI Y 1 + ... + Xn Yn.
Complex dot product is x T Y . Perpendicular vectors have x T y = O. (AB)ij = (row i of A)T(column j of B).

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 IIA1 yll2 = Y T(AAT)1 Y = 1 displayed by eigshow; axis lengths ad

Fourier matrix F.
Entries Fjk = e21Cijk/n give orthogonal columns FT F = nI. Then y = Fe is the (inverse) Discrete Fourier Transform Y j = L cke21Cijk/n.

Indefinite matrix.
A symmetric matrix with eigenvalues of both signs (+ and  ).

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.

Multiplier eij.
The pivot row j is multiplied by eij and subtracted from row i to eliminate the i, j entry: eij = (entry to eliminate) / (jth pivot).

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

Particular solution x p.
Any solution to Ax = b; often x p has free variables = o.

Positive definite matrix A.
Symmetric matrix with positive eigenvalues and positive pivots. Definition: x T Ax > 0 unless x = O. Then A = LDLT with diag(D» O.

Reflection matrix (Householder) Q = I 2uuT.
Unit vector u is reflected to Qu = u. All x intheplanemirroruTx = o have Qx = x. Notice QT = Q1 = Q.

Similar matrices A and B.
Every B = MI AM has the same eigenvalues as A.

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

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.
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