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MACM 201 Discrete Math II Winter 2017

Find unique solution to a given recurrence

Given $$ a_{n+1} - 1.5a_n = 0$$

Rearrange equation so $$ a_{n+1} = 1.5a_n$$

then we can see that $$ a_{n+2} = 1.5a_{n+1} = 1.5 * 1.5 * a_{n}$$

so the unique solution is $$ a_{n} = 1.5^n * a_0 $$

Solve a recurrence using characteristic polynomial

Given an equation such as $$ a_n = 2a_{n-1} + 3a_{n} \quad a_0 = 1 \quad a_1 = 2 $$

Substitute $$ a_n = cr^n $$

so the equation becomes $$ cr^n = 2cr^{n-1} + 3cr^{n-2} $$

the key part is to divide both sides by $$ cr^{n - lowest exponent} $$

in this case its 2, it becomes $$ r^2 - 2r - 3 = 0 $$

move terms to one side rearrange and find the root $$ (r+1)(r-3) = 0 $$

with r = -1 and r = 3, general equation for second order homogenous solution is $$ a_n=A3^n+B(-1)^n $$

solve for A and B by using initial information $$ a_1 = 1 \quad a_0 = 0 $$

we get $A = 3/4 \quad B=1/4$

The solution is thus $a_n = 3/4*3^n + 1/4*(-1)^{n}$

Table of reference for particular solutions in recurrence

g(n) of $n^t$ is $\quad A_on^t + A_1n^{t-1} + A_2n^{t-2} + ... + A_0$

g(n) of $r^n$ is $\quad Ar^n$

g(n) of $n^tr^n$ is $\quad r^n(A_tn^t + A_{t-1}n^{t-1} + A_{t-2}n^{t-2} + ... + A_1n^1 + A_0)$

Solve a nonhomogenous equation

1. Determine the recurrence solution, like $$ a_n = 3a_{n-1} + 3^{n-1} $$
2. find the general solution for the homogenous portion $$ a_n^h = Ar_1^n + Br_2^n $$ where A B are constants that solves the equation.
3. Find the particular solution $a_n^p$ using textbook table, note if found to be symmetric with $a_n^h$ you need to introduce a $n$ to break the symmetry. $$ a_n^p = C2^n + Dn^23^n $$ where C D are constants to solve for the particular solution
4. the solution is now in the form of $$ a_n = a_n^h + a_n^p = Ar_1^n + Br_2^n + C2^n + Dn^2(3^n) $$
5. Use coefficient comparision to solve for C and D in $a_n^p$ by plugging $a_n^p$ into the recurrence soluation at 1.
6. Once we found C, D , we can solve for A, B by setting up 4. $a_n$ with the values from 5.

Extended Binomial Theorem

Formula for (fraction, n) and (negative, n) follows the form

for (negative, n) this simplies to

$(-1)^r(n+r-1, r)$

Coefficient Extraction

Coefficient extraction is used to solve recurrence relations, and find combinatoric sums , amongsts other things.

There are several rules we can follow

1. Sum Rule $[x^n](A(x)+B(x)) = ([x^n]A(x) + [x^n]B(x))$ (We can think of this like two mutually exclusive series, so we need to use sum rule to count them)
2. Power Rule $[x^n](A(kx) = k^n([x^n]A(X))$
3. Reduction Rule $[x^n](kx^mA(X)) = k[x^{n-m}A(x)]$ (This is because we can think $x^m$ is a shift of terms to the series, so we can find the correct coefficient by offset)
4. Product Rule $[x^n](A(x) * B(x)) = [x^k]A(x) * [x^{k-1}]B(x$)

What is a graph

graph is a collection of vertices, V, and edges E.

Traversal terms

• Cycle is a closed Path, a Path has no repeat vertices
• Circuit is a closed Trail, a Trail has no repeat edges
• Walk is the most general case, no restrictions.

Euler Trail and Euler Circuits

if G is a connected graph. G has a Euler circuit if the circuit traverses every edge of the graph exactly once.

it’s a Euler trail if it’s open and traverse every edge once.

$G=(V,E)$ be a connected graph or multigraph, G has a Eulerian circuit IFF every vertex of G has even degree.

Corollary $G=(V,E)$ be a connected graph or multigraph. G has an Eulerian Trail IFF G has exactly two vertices of odd degree.

Hamilton Path and Hamilton Cycle

This is a path/cycle (so no repeat vertice) that traverses all the vertices exactly once in the graph.

finding a hamilton path is NP complete, meaning there is no known algorithm that can find the solution in an appropriate amount of time.

Necessary and Sufficient Condition

• Sufficient if p is true then q is true.
• Necessary if q is true, then p is true. as in, example, if p is necessary for q, then q cannot be true unless p is true

• Necessary and Sufficient p is true IFF q is true. Acing the test is necessary and sufficient for getting 100% on a test.

• Necessary But Not Sufficient Steering well is necessary for driving well, but not sufficient , because you need to follow traffic rules, not hit pedestrians, etc.

• Sufficient But not Necessary Boiling a potato is sufficient for cooking a potato, but not necessary, because its not the only way to cook, you could fry, roast, etc.

Sufficient Condition 1 for Identifying Hamilton Path / Cycle

/$for all vertices x, y/, with |V| >=2 x != y /, deg(x) + deg(y) >= n-1$ and /$for all vertices x, y/, with |V| >=3 x != y /, deg(x) + deg(y) >= n$

This is a sufficient condition for a graph to admit a Hamilton cycle.

Sufficient Condition 2 for Identifying Hamilton Path / Cycle

/$for all vertices x /, deg(x) >= (n-1)/2$ This is another sufficient condition for having a HP

Necessary Condition for Identifying Hamilton Cycle in Bipartite Graph

given a bipartite graph organize by set V1 and set V2 . There needs to be |V1| = |v2| for there to be a hamilton cycle

Theorem - If G=(V, E) is an undirected graph or multi graph , then sum of all the degrees of the vertices are 2|E|

Proof - For each edge [a, b] in graph G, the edge countributes a count of 1 to deg(a) and deg(b) respectively.

consequently, for each edge, it contributes 2 to the sum of total degrees. Thus 2|E| accounts for deg(v), for all v in vertices.

Regular Graph

This is a graph where each vertex shares the same degrees. if deg(v) = k for all vertices v, then the graph is call k-regular.

if G is a k-regular graph, then $|E| = k|V|/2$

Isomorphic,

If two graphs G1, and G2 are isomorphic, then there is a function f that is bijective, and f maps an edge E1 to E2.

one can transform G1 into G2 by relabelling the vertice of G1 with the labels of vertices of G2 (AKA preserving adjacencies)

Tips on identifying graph isomorphism

1. If you think G and H are isomorphic, then you would need to show there is a bijective mapping between vertices of G and vertices of H that preserves the edge.
2. If not isomorphic, you need to show they are different, including:
   • number of vertices
   • number of edges
   • degrees of distribution
   • cycles length

Planar Graphs

Planar graph is a graph that can be drawn without intersecting edges. Each planar graph have planar embedding which are different variations of planar graph

A subgraph of a planar graph is planar

1. Given G is a planar graph, implying there’s a planar embedding of G
2. H is obtained from G by removing vertices and/or edges
3. Since G did not have crossing edges to begin with
4. H would not have crossing edges either
5. So H has a planar embedding, and is a planar graph

$K_5\,and\,K_{3,3}$ are the first non planar graphs

Kuratowsk Theorem says a graph is a planar IFF if it does not contain a subgraph of subdivision $K_5 \, and \, K_{3,3}$

Faces

Faces are regions in a planar graph. we can think of faces as regions closed off by a cycle within the graph. note not every cycle of G forms a face

Euler Formula

states $$ faces = 2 + edges - vertices $$

Hypercube

two binary sequences of w and w’ have distance 1 if they differ in a single position.

Hypercubes have strings as vertices, and edges if two strings differ by distance 1

Distance

The distance between two vertices is the shortest path between the two

Hamming Distance

two binary sequence w, and w’ of length n, the hamming distance is the number of positions in the string where they are different.

Graph Proofs

Proof for all x - y walks in a graph, there exists a x -y path

1. Define a w to be a minimal walk from x - y, so it is also the shortest.

2. theres two cases for w . A.) either w has a repeated vertex, or B.) it does not.

3. for case

   • A. Since there is a repeated vertex, call v1. this means this walk looks something like this . x - a - b - c -v1 - d -v1 - y. This means we can remove occurences of v1 so that only one v1 remains. This new walk w1 is now a path.
   • B. we are done, as by definition it is also a path.

Proof if graph G has a total number of vertices v. and total number of edges e. and G is loop free, undirected . prove that $2e <= v^2 - v$

the key is to note that the maximum edges a graph can have is given by the complete graph of vertices v. ie. (v, 2) edges.

then its a matter of rearranging the formula of (v,2) to get v(v-1)/2

General notes on graph proofs

We should remember the property Sum of all degrees = 2 * total edges of a graph.

a k+1 regular graph would also have the properties of a k regular graph. so if k regular graph has a k walk, the k+1 graph would also have it.

in a induction proof, when we have establish a base case, an induction hypothesis, and we are proofing for n+1, it may be sufficient to just draw a graph n abstractly, namely, we dont care about whether its bipartite, complete, or whatever.