Goal
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到達目標:
The objectives of this course are to make students firmly laying
good foundation of data structures and algorithms, and one-step
further comprehensively understanding algorithm analysis and
having design and implementation skills in Python, Java, or
other programming language.
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Contents (L1 - Introduction)
What is an Algorithm?
How to design?
How to analyze?
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Why study algorithms?
Algorithms play the central role both in the
science
practice
From a practical standpoint- you have to know a standard set of important algorithms
- you should be able to design new algorithms
From theoretical standard - the study of algorithms is the core of computer science
related to many other fields
useful in developing analytical skills
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Of computing
IntroductionWhat is an Algorithm?
An algorithm is a sequence of unambiguous instructions for solving a problem, i.e., for obtaining a required output for any legitimate input in a finite amount of time.
A. Levitin “Introduction to the Design & Analysis of Algorithms,” 3rd ed., Ch. 1 ©2012 Pearson Education, Inc. Upper Saddle River, NJ. All Rights Reserved.
合理的な
ambiguousあいまいな
The algorithm is independent from any programming languages.
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Two main approaches
1. from typical problem types
2. from algorithm design techniques
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1. from typical problem types
(a number of algorithms to a problem type)
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Algorithm Features
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Difference between Algorithm and Pseudocode
An algorithm is a formal definition with some specific characteristicsthat describes a process, which could be executed by a Turing-complete computer machine to perform a specific task. Generally,the word "algorithm" can be used to describe any high level task incomputerscience.
Pseudocode is an informal and (often rudimentary) humanreadable description of an algorithm leaving many granular details ofit. Writing a pseudocode has no restriction of styles and its onlyobjective is to describe the high level steps of algorithm in a muchrealistic manner in natural language.
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Sorting
Searching (e.g. BFS, DFS, Red-black tree, 2-3-4 tree)
Shortest paths in a graph (e.g. Dijkstra’s algorithm)
Minimum spanning tree
(e.g. Prim’s algorithm, Kruskal’s algorithm )
Traveling salesman problem (TSP tour approximation)
Knapsack problem (Dynamic programming algorithm )
Chess
Primality testing
Towers of Hanoi
Program termination
Some Well-known Computational Problems
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e.g. Sorting problemThere are many different algorithms
e.g. Greatest common factor
Problem: Find gcd(m,n),
the greatest common divisor of two nonnegative,
not both zero integers m and n
e.g.: gcd(60,24) = 12, gcd(60,0) = 60, …
(1) Euclid’s algorithm: it is based on repeated application of equality
gcd(m,n) = gcd(n, m mod n)
until the second number becomes 0,
which makes the problem trivial.
e.g.: gcd(60,24) = gcd(24,12) = gcd(12,0) = 12
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最大公約数
Other methods(to the same problem: Greatest common factor)
(2) Brute force solution
Step 1 Assign the value of min{m,n} to t
Step 2 Divide m by t. If the remainder is 0, go to Step 3;otherwise, go to Step 4
Step 3 Divide n by t. If the remainder is 0, return t and stop;otherwise, go to Step 4
Step 4 Decrease t by 1 and go to Step 2
(3) Finding the prime factors
Step 1 Find the prime factorization of m
Step 2 Find the prime factorization of n
Step 3 Find all the common prime factors
Step 4 Compute the product of all the common prime factorsand return it as gcd(m,n)
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2. from algorithm design techniques
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Divide and conquer
Decrease and conquer
Transform and conquer
Brute force
Greedy approach
Dynamic programming
Backtracking and branch-and-bound
Space and time tradeoffs
Some well-known algorithm design techniques
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e. g. Divide and Conquer technique (分割統治) which is used in many different algorithms for solving different problems.
For example
- Searching
- Sorting
- Matrix multiplication
- ……
A design technique to solve different problems
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Which is better?
Two main issues:
(1) How to design algorithms?
(solve the problem)
(2) How to analyze algorithms?
(evaluate/optimize the algorithms)
Algorithm design techniques
Brute force
Divide and conquer
Decrease and conquer
Transform and conquer
Space and time tradeoffs
Greedy approach
Dynamic programming
Iterative improvement
Backtracking
Branch and bound
……
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Analysis of algorithms
How good is the algorithm? correctness
time efficiency
space efficiency
Does there exist a better algorithm? lower bounds
optimality
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1-22
For example: sorting
Rearrange the items of a given list in ascending order.
Input: A sequence of n numbers <a1, a2, …, an>
Output: A reordering <a´1, a´2, …, a´n> of the input sequence such that a´1≤ a´2 ≤ … ≤ a´n.
Why sorting?
Help searching
Algorithms often use sorting as a key subroutine.
Sorting key
A specially chosen piece of information used to guide sorting. E.g., sort student records by student ID.
Analysis of Algorithms 23
An example:
Approach
Pseudo code
Analysis of Algorithms 24
the best-case running time is (n)
the best case occurs if the array is already sorted, t j =1
Algorithm
Analysis of Algorithms 25
A worst-case running time of (n2)
O(n2)bound on worst-case running time of insertion sort
Analysis of Algorithm 26
Running Time
Most algorithms transform input objects into output objects.
The running time of an algorithm typically grows with the input size.
Average case time is often difficult to determine.
We focus on the worst case running time. Easier to analyze
Crucial to applications such as games, finance and robotics
0
20
40
60
80
100
120
Ru
nn
ing
Tim
e1000 2000 3000 4000
Input Size
best case
average case
worst case
Monday, May 14,
2018
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Experimental StudiesWrite a program implementing the algorithm
Run the program with inputs of varying size and composition
Use a method in Java like System.currentTimeMillis() or
to get an accurate measure of the actual running time
Plot the results
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1000
2000
3000
4000
5000
6000
7000
8000
9000
0 50 100
Input SizeT
ime
(m
s)
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Comparison of different algorithms
Analysis of Algorithm 29
Big-Oh Notation
To simplify the running time estimation,
for a function f(n), we ignore the constants and lower order terms.
Example: 10n3+4n2-4n+5 is O(n3).
Monday, May 14, 2018
Analysis of Algorithm 30
Big-Oh Notation
To simplify the running time estimation,
for a function f(n), we ignore the constants and lower order terms.
Example: 10n3+4n2-4n+5 is O(n3).
Monday, May 14, 2018
Function of Growth rate
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Some approximate of several functions important for analysis of algorithms
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Efficiency is very much depended on data structure
Apart from the linked list, there are other often used data structure.
About this course
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Reference book
Thomas H. Cor-men, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, MIT Press, 2009Anany Levitin, Addison-Wesley, 2011
Textbook
Anany Levitin, Addison-Wesley, 2011
About this course
Teaching plan
It is expected to have slight adjustments
Evaluation
- Mid-term: exercise problems (20%)
- Exams: final exam (80%)
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Exercise 1-1
What is the output of Test1(200) ?
Test1(200)の出力結果は何ですか?
Test1は次のアルゴリズムです。
Algorithm Test1(n)
b 0
for i 1 to n do
if i mod 6 = 0 then b b + 1
else if i mod 9 = 0 then b b +10
return b
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Exercise 1-2
What are the output of Test2(100)?
Test2(100)の出力結果は何ですか?
Test2は次のアルゴリズムです。
Algorithm Test2(n)b 0
for i 1 to n dofor j 1 to i do
b b +1return b
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Exercise 1-3
What are the output of Test3(1000) ?
Test3(1000 )の出力結果は何ですか?
Test3は次のアルゴリズムです。
Algorithm Test3(n)i 1b 0while i < n do
b b + 1i 2i
return b
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