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Foundations II: Data Structures and AlgorithmsPowerPoint Presentation

Foundations II: Data Structures and Algorithms

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### Foundations II: Data Structures and Algorithms

Instructor : Yusu Wang

Lecture 1 : Introduction and

Asymptotic notation

CSE 2331 / 5331

Course Information

- Course webpage
http://www.cse.ohio-state.edu/~yusu/courses/2331

- Office hours
- TuTh 11:00 – 11:30 am

- Grading policy:
- homework: 20%,
- two midterms: 40%,
- final: 40%

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Exams

- Midterm 1
- Sept 30th in class.

- Midterm 2
- Nov. 4th in class.

- Final exam:
- Dec. 12th, 10:0am--11:45am, Room DL0264

Mark you calendar.

Contact me within first two full weeks if any schedule conflict for midterms

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Notes

- All homework should be submitted before or in class on the due date. Late homework during the same day receives a 10% penalty. Late homework submitted the next day receives a 25% penalty.
- You may discuss homework with your classmates. It is very important that you write up your solutions individually, and acknowledge any collaboration / discussion with others.

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More Information

- Textbook
- Introduction to Algorithms
byCormen, Leiserson, Rivest and Stein

(third edition)

- Introduction to Algorithms
- Others
- CSE 2331 course notes
- By Dr. Rephael Wenger (SBX)

- The Art of Computer Programming
By Donald Knuth

- CSE 2331 course notes

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Introduction

- What is an algorithm?
- Step by step strategy to solve problems
- Should terminate
- Should return correct answer for any input instance

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This Course

- Various issues involved in designing good algorithms
- What do we mean by “good”?
- Algorithm analysis, language used to measure performance

- How to design good algorithms
- Data structures, algorithm paradigms

- Fundamental problems
- Graph traversal, shortest path etc.

- What do we mean by “good”?

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A = < , , …. >

a

a

a

n

2

1

Sorting problem- Input:
- Output: permutation of A that is sorted

- Example:
- Input: < 3, 11, 6, 4, 2, 7, 9 >
- Output: < 2, 3, 4, 6, 7, 9, 11 >

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3, 4, 6, 11, 2, 7, 9

2, 3, 4, 6, 11, 7, 9

Insertion Sort1

i

i+1

n

3, 11, 6, 4, 2, 7, 9

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Pseudo-code

InsertionSort(A, n)

for i = 1 to n-1 do

key = A[i+1]

j = i

while j > 0 and A[j] > key do

A[j+1] = A[j]

j = j - 1

A[j+1] = key

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Analysis

- Termination
- Correctness
- beginning of for-loop: if A[1.. i] sorted, then
- end of for-loop: A[1.. i+1] sorted.
- when i = n-1, A[1.. i+1] is sorted.

- Efficiency: time/space
- Depends on input size n
- Space: roughly n

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Running Time

- Depends on input size
- Depends on particular input
- Best case
- Worst case

First Idea:

Provide upper bound (as a guanrantee)

Make it a function of n : T(n)

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Running Time

- Worst case
- T(n) = max time of algorithm on any input of size n

- Average case
- T(n) = expected time of algorithm over all possible inputs of size n
- Need a statistical distribution model for input
- E.g, uniform distribution

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c

c

c

c

c

c

1

2

4

2

1

3

5

n

T(n) = ( + i ) = + n +

i=2

2

n

Insertion SortInsertionSort(A, n)

for i = 1 ton-1do

key = A[i]

j = i

while j > 0 and A[j] > key do

A[j+1] = A[j]

j = j - 1

A[j+1] = key

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Asymptotic Complexity

- Why shall we care about constants and lower order terms?
- Should focus on the relative growth w.r.t. n
- Especially when n becomes large !
- E.g: 0.2n1.5 v.s 500n

Second Idea:

Ignore all constants and lower order terms

Only order of growth

--- Asymptotic time complexity

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lim ≤ c

g(n)

n

Big O Notation- f(n) O(g(n))if and only if c > 0, and n0 , so that
0 ≤ f(n) ≤ cg(n) for all n ≥ n0

- f(n) = O(g(n)) means f(n) O(g(n))(i.e, at most)
- We say that g(n) is an asymptotic upper bound for f(n).

- It also means:
- E.g, f(n) = O(n2) if there exists c, n0 > 0 such that f(n) ≤ cn2, for all n ≥ n0.

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Illustration

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lim ≥ c

g(n)

n

Omega Notation- f(n) (g(n)) if and only if c > 0, and n0 , so that 0 ≤ cg(n) ≤ f(n) for all n ≥ n0
- f(n) = (g(n)) means f(n) (g(n)) (i.e, at least)
- We say g(n) is an asymptotic lower bound of f(n).

- It also means:

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Illustration

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Theta Notation

- Combine lower and upper bound
- Means tight: of the same order
- if and only if , and, such that for any
- means
- We say g(n) is an asymptotically tight bound for f(n).

- It also means:

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Remarks -- 1

- if and only if , and .
- Insertion sort algorithm as described earlier has time complexity .

- Transpose symmetry:
- If , if and only if

- Transitivity:
- If and , then .
- Same for and

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Remarks -- 2

- It is convenient to think that
- Big-O is smaller than or equal to
- Big- is larger than or equal to
- Big- is equal

- However,
- These relations are modulo constant factor scaling
- Not every pair of functions have such relations

If , this does not imply that .

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n

2

2

Remarks -- 3- Provide a unified language to measure the performance of algorithms
- Give us intuitive idea how fast we shall expect the alg.
- Can now compare various algorithms for the same problem

- Constants hidden!
- O( n lg lg n ), 2n lg n

- T(n) =+ O (n)
means T(n) = + h(n), and h(n) = O(n)

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Pitfalls

OK

- O(n) + O(n) = O(n)
- T(n) = n + O(n)
= n + O(n)

k

?

i = 1

k should not depend on n !

It can be an arbitrarily large constant …

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lim = 0

g(n)

n

Yet Another Notation- Small o notation:
- f(n) = o(g(n))means for any s.t. for all In other words,

- Examples:
- Is n - lg n = o(n) ?
- Is n = o (n lg n) ?

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g(n)

lim =

lim = 0

g(n)

f(n)

n

n

Yet Another Notation- Small ω notation:
- f(n) = ω(g(n)) means for any s.t. for all In other words,

or

- Examples:
- Is n - lg n = ω(n) ?
- Is n = ω(n / lgn) ?

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- Review some basics
- Chapter 3.2 of CLRS
- See the board

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Summary

- Algorithms are useful
- Example: sorting problem
- Insertion sort

- Asymptotic complexity
- Focus on the growth of complexity w.r.t input size

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