Logarithm, Exponent Rules, Counting Principles, Permutations, Combinations, and Binomial, Study notes of Computer Science

An in-depth exploration of various mathematical concepts, including logarithm and exponent rules, basic principles of counting, permutations, combinations, and the binomial theorem. The rules for logarithms and exponents, as well as the addition, multiplication, and subtraction principles of counting. It also delves into permutations and combinations, explaining how to calculate them and providing examples. The document concludes with an explanation of the binomial theorem and its application.

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Date: 08/28/2003
Course: COT 5405
Semester: Fall 2003
Instructor: Arup Guha
Logarithm/Exponent Rules
yxxy
aaa
loglog)(log
Note: The rule does not apply to
)(log yx
a
. A way to bound
)(log yx
a
would be to use
the following relationship:
),max(log2log
)),max(2(log)(log
yx
yxyx
aa
aa
xyx
a
y
a
log)(log
a
x
x
b
b
a
log
log
log
Note: This rule allows us to change the base of a log if we don't like the one it is currently in.
yx
bb
xy
loglog
; to prove this,
Let
y
b
xc
log
.
Then, by definition of logarithm and exponent,
.
Using logarithm rule three then to change the base, we get:
b
y
x
c
2
2
2
2
log
log
log
log
.
Rewriting,
b
x
y
c
2
2
2
2
log
log
log
log
.
Then, by using the common base rule backwards,
xc
by
loglog
.
Use the definition of a log to yield
cy
x
b
log
.
Now, substitute for c:
yx
bb
xy
loglog
.
pf3
pf4
pf5
pf8

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Date: 08/28/

Course: COT 5405

Semester: Fall 2003

Instructor: Arup Guha

Logarithm/Exponent Rules

 log (^) a ( xy )log ax log ay

Note : The rule does not apply to log (^) a ( xy ). A way to bound log (^) a ( xy )would be to use

the following relationship:

log 2 log max( , )

log ( ) log ( 2 max( , ))

x y

x y x y

a a

a a

x y x a

y

a

log ( ) log

a

x

x

b

b

a

log

log

log

Note : This rule allows us to change the base of a log if we don't like the one it is currently in.

b x^ by

y x

log log

 ; to prove this,

Let

by c x

log .

Then, by definition of logarithm and exponent,

log (^) x c log (^) b y.

Using logarithm rule three then to change the base, we get:

b

y

x

c

2

2

2

2

log

log

log

log

Rewriting,

b

x

y

c

2

2

2

2

log

log

log

log

Then, by using the common base rule backwards,

log (^) y c log (^) b x.

Use the definition of a log to yield

y c

b x

log .

Now, substitute for c:

b x^ by

y x

log log

Exponents

 (^) a b ab x x x

 

a b ab ( x )  x

 (^) a ab ab x x x

b   

Note: This is a very common error. Avoid it!!

 x

a y

a = (xy)

a

Basic Principles of Counting (necessary for probability)

  1. Addition Principle: if counting the members in two disjoint sets, X and Y , the total

number of members of the union is found by simple addition:

X  Y  X  Y.

Where | X | is the cardinality of the set X.

  1. Multiplication Principle: when counting the number of elements of ordered pairs in the

Cartesian Product of the sets X and Y , multiply the cardinalities of the sets as so:

| X * Y | = | X | * | Y |.

Example: Given ( x , y ) for 0  x  10 , 0 ^ y^ ^20 , the total number of elements is:

10*20 = 200.

  1. Subtraction Principle: Counting elements from a universe, U , and you can count the

elements that you don’t want, then subtract this from | U | as so:

X  U  X.

This principle assumes that U is countable.

Permutations

Given n distinct objects (1, 2, 3, …, n ), you want to order any k of them. Each order is distinct

and is counted separately. What we want is a k -tuple:

Example: 3, 2, 1, 4, 5, 6, …, k

2, 3, 1, 4, 5, 6, …, k

In general,

n k

n P nn n n k n k

Binomial Theorem

Given, ( x + y )

n , in general we have:

n n n n n x y

n

n x y

n

n x y

n x y

n x y

n

x y x y x y x y

0 1 1 2 2 1 1 0

1

...

0 1 2

( )( )( )...( )

  

  

  

  

 

    

  

When we set x=1 and y=1, we find:

n

k

n

k

n

x y

0

Also, since any choice of k objects out of n corresponds to the remaining n-k objects, we have:

 

n k

n

k

n

With respect to the binomial coefficient, when bounding combinations,

 

   

 

 

 

  

1

1 ...

1

1 n k

k

n

k

n

k

n

where n , k are positive integers, and n  k.

When we subtract 1 from the numerator and denominator, successive terms are greater than or

equal to the previous term. Since

k

n is the minimum term, the lower bound is:

k

k

n

k

n

 

  

for large n and k.

To get the upper bound,

!

( 1 )...( 1 )

k

nn n k

k

n (^)   

 

.

Since each value in the numerator is less than or equal to n, we have

(i)

k!

n

k

n

k

Using Stirling’s approximation, we get

n

e

n n (^) 

 

2  = n!

or simply: n!

e

n

n

^ 

 

for most values.

So, by substituting

n

e

n

 

for k! in equation (i) above, we obtain

k

k

k

k

en

e

k

n

k

n

 

 

 

 

Example: With the roll of two dice, what is the probability of obtaining a sum of 2, 6

and 8?

The key here is to be certain that the sample space is labeled properly. If

it is not, you won’t get the correct answer!

There are 36 possibilities, each one occurring with equal probability.

P(2) =

P(6) =

P(9)=

Assumptions of probabilities

(where P ( e ) is the probability of an event e and s is the sample)

  1. 0 ^ P ( e )^1
  2.  

e S

P ( e ) 1

  1. If events of A, B, C are disjoint,

P ( A  B  C ) = P ( A ) + P ( B ) + P ( C )

In general,

P ( A  B ) = P ( A ) + P ( B ) - P ( A  B )

  1. Conditional probability

P B

P A B

P A B

The probability of an event not occurring can be found by: 1 ^ p ( failure ).

Example:

Given,

What is P(temp>90)?

Solution:

P(Temp > 90) = P(Rain  (Temp > 90)) + P((not rain)  (Temp>90))

P(Temp > 90) = (0.4)(0.3) + (0.6)(0.8)

P(Temp > 90) = 0.

In general,

P ( H )[ P ( R )* P ( H | R )][ P ( R )* P ( H | R )].

Submitted by: Fredrick Okumu and Christina Spradlin