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TEST OF FUNDAMENTALS

- all the concepts/vocabulary…

WEEK 1 TOPICS

- Course overview
- Propositional logic
- LaTeX

CONCEPTS / VOCABULARY

- Propositions
- Boolean variables
- Bit strings
- Truth value
- Truth table
- Operators: Negation, conjunction, disjunction, exclusive or, implication, XOR, biconditional
- Converse, inverse, contrapositive

CONCEPTS / VOCABULARY

- Tautology, contradiction
- Table 1.2.5 (p. 17) of logical equivalences:
- Identity laws
- Domination laws
- Idempotent laws
- Double negation law
- Commutative laws
- Associative laws
- Distributive laws
- De Morgan’s laws

WEEK 2 TOPICS

- Predicate logic and quantifiers
- Sets and set operations

CONCEPTS / VOCABULARY

- Predicate (a.k.a. propositional function)
- Arguments (n-tuple)
- Universe of discourse, syntax vs. semantics
- Universal quantification
- Existential quantification
- Nesting of quantifiers
- Binding variables, propositions
- Negated quantifiers / equivalences

Definition of consistency: A set of propositions is consistent if there is an assignment of truth values to the variables that makes each expression true.

CONCEPTS / VOCABULARY

- Sets, elements, members
- N, Z, Z+, R
- Set equality
- Intensional (set builder) vs. extensional (enumerated) set definitions
- Universal set
- Empty/null set (), subset, proper subset, power set
- Infinite sets, finite sets, cardinality
- Ordered n-tuples, Cartesian product

CONCEPTS / VOCABULARY

- Venn diagram
- Union, intersection, difference (complement), symmetric difference
- Disjoint sets
- Principle of inclusion-exclusion
- Set identities (Table 1.5.1: identity, domination, idempotent, complementation, commutative, associative, distributive, and De Morgan’s laws)

WEEK 3 TOPICS

- Functions
- Sequences and summations
- Growth of functions; big-O notation

CONCEPTS / VOCABULARY

- Function/mapping
- Domain, codomain, image, pre-image, range
- One-to-one/injective, onto/surjective, one-to-one correspondence/bijective
- Inverse/invertible functions, compositions, graphs
- Floor, ceiling

CONCEPTS / VOCABULARY

- Sequences, terms, strings
- Arithmetic progressions, geometric progressions, geometric series
- Summation , index of summation, lower and upper limit
- Standard summation formulas: Table 1.6.2
- Cardinality, countability

CONCEPTS / VOCABULARY

- Big-O notation (upper bound on growth of f(x))
- f(x) is O(g(x)) if there exist constants C and k such that |f(x)| C |g(x)|whenever x k
- Triangle inequality
- Growth of (f1 + f2)(x), (f1 f2)(x)
- Big-Omega (lower bound on growth of f(x))
- Big-Theta (upper and lower bound)

WEEK 4 TOPICS

- Algorithms
- Algorithmic complexity
- Integers

CONCEPTS / VOCABULARY

- Algorithm: “Finite set of precise instructions”
- Properties: Input, output, definiteness (preciseness), correctness, finiteness, effectiveness, generality
- Pseudocode
- Searching algorithms
- Linear (sequential) search
- Binary search

CONCEPTS / VOCABULARY

- Computational complexity, time complexity, (space complexity)
- Worst-case, average-case, best-case
- Exponential complexity, polynomial complexity, linear complexity, logarithmic complexity
- Tractability, intractability, unsolvability (halting problem, Alan Turing), NP vs. P, NP-complete

CONCEPTS / VOCABULARY

- Fundamental Theorem of Arithmetic
- Primes, composite numbers
- Factoring
- Division (quotient, remainder)
- a | b
- Greatest common divisor, least common multiple, relative primes
- Modular arithmetic, congruence
- “The Division Algorithm”
- Note: We’ll revisit pp. 120-125 when we get to Section 2.5.

WEEK 5 TOPICS

- Integers and algorithms
- Applications of number theory
- Matrices

CONCEPTS / VOCABULARY

- Euclidean algorithm
- Base b expansions of integers (especially binary, hexadecimal)
- Binary addition, binary multiplication, bit shifting

CONCEPTS / VOCABULARY

- gcd as linear combination
- Linear congruence
- Fermat’s Little Theorem
- Applications:
- From Section 2.3: Hashing, pseudorandom numbers, cryptology
- From Section 2.5: Chinese remainder theorem, computer arithmetic, pseudoprimes / Fermat’s Little Theorem, public key cryptography, RSA encryption/decryption

CONCEPTS / VOCABULARY

- mxn matrices, rows, columns, equality
- Matrix arithmetic, products
- Identity matrix
- Transpose At, symmetric matrices
- Zero-one matrix, join (), meet (), Boolean product

WEEK 6 TOPICS

- Proof methods
- Mathematical induction

CONCEPTS / VOCABULARY

- Theorems
- Axioms / postulates / premises
- Hypothesis / conclusion
- Lemma, corollary, conjecture
- Rules of inference
- Modus ponens (law of detachment)
- Modus tollens
- Syllogism (hypothetical, disjunctive)
- Universal instantiation, universal generalization, existential instantiation (skolemization or Everybody Loves Raymond), existential generalization

CONCEPTS / VOCABULARY II

- Fallacies
- Affirming the conclusion [abductive reasoning]
- Denying the hypothesis
- Begging the question (circular reasoning)
- Proof methods
- Direct proof
- Indirect proof, proof by contradiction
- Trivial proof
- Proof by cases
- Existence proofs (constructive, nonconstructive)

CONCEPTS/VOCABULARY

- Proof by mathematical induction
- Inductive hypothesis
- Basis step: P(1) is true (or sometimes P(0) is true).
- Inductive step: Show that P(n) P(n+1) is true for every integer n > 1 (or n > 0).
- Strong mathematical induction (“second principle of mathematical induction”)
- Inductive step: Show that [P(1) … P(n)] P(n+1) is true for every positive integer n.

WEEK 7 TOPICS

- Recursion
- Recursive and iterative algorithms
- Program correctness

Concepts/Vocabulary

- Recursive (a.k.a. inductive) function definitions
- Recursively defined sets
- Special sequences:
- Factorial F(0)=1, F(n) = F(n-1)(n) = n!
- Fibonacci numbers f0 = 0, f1 = 1, fn = fn-1 + fn-2
- Strings
- *: Strings over alphabet
- Empty string
- String length l(s)
- String concatenation

Concepts / Vocabulary

- Recursive algorithm
- Iterative algorithm

Concepts / Vocabulary

- Initial assertion, final assertion
- Correctness, partial correctness, termination
- “Partially correct with respect to initial assertion p and final assertion q”
- Rules of inference
- Composition rule
- Conditional rules
- Loop invariants

WEEK 8 TOPICS

- Counting
- Inclusion-exclusion
- Tree diagrams
- Pigeonhole principle

Concepts/Vocabulary

- Counting
- Sum rule |A1 A2 … Am| = |A1| + … + |Am| for disjoint Ai
- Product rule |A1x A2 x … x Am| = |A1| |A2| … |Am|
- Inclusion-exclusion |A1 A2| = |A1| + |A2| - |A1 A2|
- Tree diagrams

Concepts / Vocabulary

- Pigeonhole Principle
- If k+1 or more objects are in k boxes, at least one box has two or more objects
- Generalized pigeonhole principle
- If N objects are in k boxes, one box has at least N/k objects

WEEK 9 TOPICS

- Permutations
- Combinations
- Binomial theorem
- Discrete probability
- Probability theory

Concepts/Vocabulary

- Permutation, r-permutation
- P(n, r) = n! / (n-r)!
- r-combination
- C(n, r) = (n choose r) = n! / (r! (n-r)!)
- Pascal’s identity
- (n+1 choose k) = (n choose k-1) + (n choose k)
- Pascal’s triangle
- Binomial theorem / binomial coefficients
- (x+y)n = j=0n (n choose j) xn-j yj

Concepts / Vocabulary

- Experiment, sample space, event
- Laplace’s probability – p(E) = |E| / |S|
- OK for finitely many equally likely outcomes
- p(~E) = 1 – P(E)
- p(E1 E2) = p(E1) + p(E2) when E1, E2 are disjoint

Concepts and Vocabulary

- Axioms of probability: for a set of mutually exclusive outcomes sS,
- 0 p(s) 1
- sS p(s) = 1
- Event: set of outcomes
- Conditional probability p(E|F) = p(EF) / p(F)
- Independence p(EF) = p(E) p(F), or p(E|F) = p(E)
- Bernoulli trials (2 outcomes)
- Binomial distribution b(k:n, p) = (n choose k) pk qn-k
- Random variables, expected values
- Independent random variables, variance

WEEK 10 TOPICS

- Recurrence relations and solutions
- Divide-and-conquer recurrences

Concepts/Vocabulary

- Recurrence relations
- Solution / solution sequence
- Initial conditions
- Useful examples: compound interest, bunny rabbits / Fibonacci, Tower of Hanoi, Catalan numbers

Concepts / Vocabulary

- Divide-and-conquer recurrence relations
- f(n) = a f(n/b) + g(n)

WEEK 11 TOPICS

- (Probability theory cont.)
- Generalized combinations and permutations

Concepts / Vocabulary

- Permutations and combinations with repetition
- “sampling with replacement”
- Number of r-permutations of n objects with repetition = nr
- Number of r-combinations of n objects with repetition = C(n+r-1, r) [D’Alembert’s method / bars and stars]
- Table 4.6.1 gives formulas
- Permutations with indistinguishable objecs
- Theorem 3: Number of n-permutations of n objects, where there are ni objects of type i (i=1, …, k) = n! / (n1! n2! … nk!)

Concepts / Vocabulary

- Inclusion-exclusion revisited…
- |AB| = |A| + |B| - |AB|
- Inclusion-exclusion generalized…
- |ABC| = |A| + |B| + |C| - |AB| - |AC| - |BC| + |ABC|
- Principle of Inclusion-Exclusion
- |A1A2…An| = 1in|Ai| - 1i<jn|AiAj| - … + (-1)n+1 |A1A2…An|
- Proof by mathematical induction…
- Derangements

Concepts/Vocabulary

- Binary relation R A x B (also written “a R b” or R(a,b))
- Relations on a set: R A x A
- Properties of relations
- Reflexivity: (a, a) R
- Symmetry: (a, b) R (b, a) R
- Antisymmetry: (a, b) R a=b
- Transitivity: (a, b) R (b, c) R (a, c) R
- Composite relation:
- (a, c) SR bB: (a, b)R (b, c) S
- Powers of a relation: R1 = R, Rn+1 = Rn R
- Inverse relation: (b,a) R-1 (a,b) R
- Complementary relation: (a,b) R (a,b) R

Concepts/Vocabulary II

- n-ary relation: R A1 x A2 x … x An
- Ai are the domain of R; n is its degree (or arity)
- In a database, the n-tuples in a relation are called records; the entries in each record (i.e., elements of the ith set in that n-tuple) are the fields
- In a database, a primary key is a domain (set Ai) whose value completely determines which n-tuple (record) is indicated – i.e., there is only one n-tuple for a given value of that domain
- A composite key is the Cartesian product of a set of domains whose values completely determine which n-tuple is indicated
- Projection: delete certain fields in every record
- Join: merge (union) two relations using common fields

Concepts / Vocabulary

- Zero-one matrix representation of [binary] relations
- Matrix interpretations of properties of relations on a set: reflexivity, symmetry, antisymmetry, and transitivity
- Digraph representation of [binary] relations
- Pictorial interpretations of properties of relations on a set
- Closure of R with respect to property P
- smallest relation containing R that satisfies P
- Transitive closure, reflexive closure, …
- Path analogy for transitive closures; connectivity relation R*; Algorithm 6.4.1 for computing transitive closure (briefly); Warshall’s algorithm (briefly)

Concepts/Vocabulary

- Equivalence relation: Relation that is reflexive, symmetric, and transitive (e.g., people born on the same day, strings that are the same length)
- Equivalence class: Set of all elements “equivalent to” a given element x (i.e., [x] = {y: (x,y) R}).
- Partition: disjoint nonempty subsets of S that have S as their union
- The equivalence classes of a set form a partition of the set

Concepts / Vocabulary [7.1]

- Simple graph G = (V, E) – vertices V, edges E
- A multigraph can have multiple edges between the same pair of vertices
- A pseudograph can also have loops (from a vertex to itself)
- In an undirected graph, the edges are unordered pairs
- In a directed graph, the edges are ordered pairs
- You should be familiar with all of these types of graphs, but for problem solving, you will only be using simple directed and undirected graphs

Concepts/Vocabulary [6.2]

- Adjacent, neighbors, connected, endpoints, incident
- Degree of a vertex (number of edges), in-degree, out-degree; isolated, pendant vertices
- Complete graph Kn
- Cycle Cn (can also say that a graph contains a cycle)
- Bipartite graphs, complete bipartite graphs Km, n
- Wheels, n-Cubes (don’t need to know these)
- Subgraph, union

Concepts/Vocabulary

- Adjacency list, adjacency matrix, incidence matrix
- Isomorphism, invariant properties
- Paths, path length, circuits/cycles, simple paths/circuits
- Connected graphs, connected components
- Strong connectivity, weak connectivity
- Cut vertices, cut edges
- Euler circuit, Euler path
- Hamilton path, Hamilton circuit
- For this section (7.5), need to know terminology but not proofs

Concepts/Vocabulary

- Formal language, syntax, semantics
- Vocabulary (alphabet) V, word (sentence) V*, language V*
- Phrase-structure grammar G=(V,T,S,P)
- Alphabet V; terminal symbols TV; nonterminal elements N=V-T; start symbol SN; productions P: {xy: x, y V*}
- Derivation * (sequence of productions)
- Derivation tree / parse tree
- L(G): {wT*: S* w}

Concepts/Vocabulary cont.

- Types of grammars:
- Type 0: no restrictions
- Type 1 (context-sensitive): productions must be w1 or w1w2 where w2 has length w1
- Type 2 (context-free): All productions must have w1N (single symbol)
- Type 3 (regular): All productions must have w1N and w2N or w2=aB where B N
- (Top-down parsing, bottom-up parsing)
- (Backus-Naur form)

Concepts/Vocabulary

- Finite-state machine M=(S,I,O,f,g,s0):
- States S, input alphabet I, output alphabet O, transition function f: SIS, output function g SIg, initial state s0S
- State table, state diagram
- (Mealy machines, Moore machines)

Concepts/Vocabulary

- Language concatenation
- Kleene closure A*: concatenation of 0 or more strings from A
- Finite-state automaton (FSA) M=(S,I,f,s0,F): states S, input alphabet I, transition function f: SIS, initial state s0, final states F
- “Recognize” a string (series of inputs) that results in a series of transitions starting at s0 and ending in any sF
- Nondeterministic FSA M=(S,I,f,s0,F): transition function f: SIP(S) [power set of S]
- “Recognizes” a string that can result in some series of transitions starting at s0 and ending in any sF
- For any language recognized by a nondeterministic FSA, there is a deterministic FSA that recognizes the same language

Concepts/Vocabulary

- Regular expressions: , = {}, xI = {x}, (AB) [concatenation], (AB) [union], and A* [Kleene closure]
- Regular set: Any set that can be represented by a regular expression
- Can be recognized using (deterministic) finite-state automata (Kleene’s Theorem)
- “if” part proved by “constructive induction”
- “only if” part left as **exercise 20
- Regular set = regular (type 3) grammar!
- (More powerful automata: Pushdown automaton, linear bounded automata)

Concepts/Vocabulary

- Turing machine: general model of computation
- Inventor Alan Turing
- T=(S,I,f,s0): states S, alphabet I that includes blank symbol B, partial function f: SI SI{R,L}, and start state s0S
- Control unit has states S; read/write tape is infinite in both directions; single read/write head takes input from the tape, writes to the tape, and moves left or right
- Specify as 5-tuples (s, x, s’, x’, d): in state s, if you read x, transition to state s’, output x’, and then move one step in direction d

Concepts/Vocabulary II

- Halting and language recognition
- T halts if f is undefined (i.e., no 5-tuple) for (s, x)
- A final state is a state that no 5-tuple begins with (i.e., no transitions are defined from the state)
- A string is recognized if T halts in a final state
- A string is not recognized if T doesn’t halt, or halts in a state that isn’t final
- Any problem that can be solved, or algorithm that can be written, with a digital computer, can also be solved with a Turing machine, despite its simplicity!
- Church-Turing thesis: Any problem that can be solved with an effective algorithm can be solved with a Turing machine

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