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Selection Sort (3 slides)Selection Sort Alg. (3 slides)Search Algorithms (6 slides)
Illustrating the Binary SearchIllustrating the Binary Search-Successful (3 slides)-Unsuccessful (3 slides)
Binary Search Alg. (3 slides)Big-O NotationConstant Time AlgorithmsLinear Time AlgorithmsExponential Algs. (2 slides)Logarithmic Time Algorithms
Chapter 3 Chapter 3 – – Introduction to AlgorithmsIntroduction to Algorithms
Selection Sort AlgorithmSelection Sort Algorithm-Integer Version-String Version
Template Syntax (4 slides)
Recursive Defn of the Power Fnc
Stopping Conditions for-Recursive Algorithms
Implementing the Recursive-Power Function
Tower of Hanoi w/ Recursion(3 slides)
Fibonacci Numbers Using- Iteration (2 slides)
Summary Slides (5 slides)
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Selection SortSelection Sort- 5 Element Array- 5 Element Array
Pass 0: Scan the entire list from arr[0] to arr[4] and
identify 20 at index 1 as the smallest element. Exchange 20 with arr[0] = 50, the first element
in the list.
P as s 0 : S e lec t 20 a t in d ex 1 E x ch an ge a rr[1 ] an d a rr[0 ]
5 0 4 0 7 5 3 5
p as s = 0
2 0
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Selection SortSelection Sort- 5 Element Array- 5 Element Array
Pass 1: Scan the sublist 50, 40, 75, and 35. Exchange the smallest element 35 at index 4
with arr[1] = 50.
2 0 5 0 4 0 7 5
p as s = 1
3 5
P as s 1 : S e lec t 35 a t in d ex 4 E x ch an ge a rr[4 ] an d a rr[1 ]
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Selection SortSelection Sort- 5 Element Array- 5 Element Array
Pass 2: Locate the smallest element in the sublist 40,
75, and 50.
2 0 3 5 7 5 5 0
p as s = 2
4 0
P as s 2 : S e lec t 40 a t in d ex 2 N o ex ch an ge n ece s s a ry
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Selection SortSelection Sort- 5 Element Array- 5 Element Array
Pass 3: Two elements remain to be sorted. Scan the sublist 75, 50 and exchange the
smaller element with arr[3]. The exchange places 50 at index 4 in arr[3].
p as s = 3
2 0 3 5 4 0 7 5 5 0
P as s 3 : S e lec t 50 a t in d ex 4 E x ch an ge a rr[4 ] an d a rr[3 ]
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Selection SortSelection Sort- 5 Element Array- 5 Element Array
2 0 3 5 4 0
S o rted lis t
5 0 7 5
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Selection Sort AlgorithmSelection Sort Algorithmvoid selectionSort(int arr[], int n)
{
int smallIndex;// index of smallest // element in the
sublist
int pass, j;
int temp;
// pass has the range 0 to n-2
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Selection Sort AlgorithmSelection Sort Algorithmfor (pass = 0; pass < n-1; pass++)
{
// scan the sublist starting at index // pass
smallIndex = pass;
// j traverses the sublist arr[pass+1] // to arr[n-1]
for (j = pass+1; j < n; j++)
// if smaller element found, assign // smallIndex to that position
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Selection Sort AlgorithmSelection Sort Algorithmif (arr[j] < arr[smallIndex])
smallIndex = j;
// if smallIndex and pass are not the // same location, exchange the // smallest item in the sublist
with // arr[pass]
if (smallIndex != pass)
{
temp = arr[pass];
arr[pass] = arr[smallIndex];
arr[smallIndex] = temp;}
}
}
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Search AlgorithmsSearch Algorithms
0 1 2 6 7 8
firs t las t
A rray arr
543
Search algorithms start with a target value and employ some strategy to visit the elements looking for a match.– If target is found, the index of the matching
element becomes the return value.
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6 4 2 9 5 10
ind ex = s eq S earc h(arr, 0 , 8 , 3);
7
Ind ex 0 1 2 3 4 5 6 7
3
target = 38
m atc h at ind ex = 5return ind ex 5
Search Search AlgorithmsAlgorithms
6 4 2 9 5 3 10 7
Ind ex 0 1 2 3 4 5 6 7target = 9
8
no m atc hreturn ind ex 8
ind ex = s eq S earc h(arr, 0 , 8 , 9);
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Search AlgorithmsSearch Algorithms- Sequential Search Algorithm- Sequential Search Algorithm
int seqSearch(const int arr[], int first, int last, int target)
{
int i = first;// scan indices in the range first <= I < last; // test for a match or index out of range.
while(i != last && arr[i] != target)
i++;
return i;// i is index of match or i = last if no
match
}
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Search Algorithms Search Algorithms
Case 1.
A match occurs. The search is complete and mid is the index that locates the target.
if (midValue == target)// found match
return mid;
m idfirs t
target
C as e 1: target = m id valueS earc h is d o ne
las t-1 las t
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Search Algorithms Search Algorithms
Case 2.The value of target is less than midvalue and the search must continue in the lower sublist. Reposition the index last to the
end of the sublist (last = mid).
// search the lower sublist
if (target < midvalue)<reposition last to mid><search sublist arr[first]…arr[mid-1]
las t-1firs t
target
C as e 2: target < m id valueS earc h lo w er s ub lis t
m id -1 las t
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Search Algorithms Search Algorithms
Case 3.The value of target is greater than midvalue and the search
must continue in the upper sublist . Reposition the index first to the front of the sublist (first = mid+1).
// search upper sublist
if (target > midvalue)
<reposition first to mid+1>
<search sublist arr[mid+1]…arr[last-1]>
C as e 3: target > m id valueS earc h up p er s ub lis t
las t-1new firs t = m id + 1
firs t
target
las t
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Illustrating the Binary SearchIllustrating the Binary Search- Successful Search- Successful Search
1. Search for target = 23
Step 1: Indices first = 0, last = 9, mid = (0+9)/2 = 4.
Since target = 23 > midvalue = 12, step 2 searches the upper sublist with first = 5 and last = 9.
m id
-7 3 5 8 12 16arr
0 1 2 3 4 5
23 33 55
6 7 8 9
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Illustrating the Binary SearchIllustrating the Binary Search- Successful Search- Successful Search
Step 2:
Indices first = 5, last = 9, mid = (5+9)/2 = 7.
Since target = 23 < midvalue = 33, step 3 searches the lower sublist with first = 5 and last = 7.
m id
-7 3 5 8 12 16arr
0 1 2 3 4 5
23 33 55
6 7 8 9
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Illustrating the Binary SearchIllustrating the Binary Search- Successful Search- Successful Search
Step 3: Indices first = 5, last = 7, mid = (5+7)/2 = 6.
Since target = midvalue = 23, a match is found at index mid = 6.
m id
-7 3 5 8 12 16arr0 1 2 3 4 5
23 33 55
6 7 8 9
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Illustrating the Binary SearchIllustrating the Binary Search- Unsuccessful Search- Unsuccessful Search
Search for target = 4.
Step 1: Indices first = 0, last = 9, mid = (0+9)/2 = 4.
m id
-7 3 5 8 12 16arr
0 1 2 3 4 5
23 33 55
6 7 8 9
Since target = 4 < midvalue = 12, step 2 searches the lower sublist with first = 0 and last = 4.
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Illustrating the Binary SearchIllustrating the Binary Search- Unsuccessful Search- Unsuccessful Search
Step 2: Indices first = 0, last = 4, mid = (0+4)/2 = 2.
Since target = 4 < midvalue = 5, step 3 searches the lower sublist with first = 0 and last 2.
m id
-7 3 5 8 12 16arr0 1 2 3 4 5
23 33 556 7 8
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Illustrating the Binary SearchIllustrating the Binary Search- Unsuccessful Search- Unsuccessful Search
Step 3: Indices first = 0, last = 2, mid = (0+2)/2 = 1.
Since target = 4 > midvalue = 3, step 4 should search the upper sublist with first = 2 and last =2. However, since first >= last, the target is not in the list and we return index last = 9.
m id
-7 3 5 8 12 16arr
0 1 2 3 4 5
23 33 55
6 7 8 9
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Binary Search AlgorithmBinary Search Algorithm
Int binSearch(const int arr[], int first, int last, int
target)
{
int mid; // index of the midpoint
int midvalue;// object that is // assigned arr[mid]
int origLast = last;// save original value of last
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Binary Search AlgorithmBinary Search Algorithm
while (first < last)// test for nonempty sublist
{
mid = (first+last)/2;
midvalue = arr[mid];
if (target == midvalue)
return mid; // have a match
// determine which sublist to // search
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Binary Search AlgorithmBinary Search Algorithm
else if (target < midvalue)
last = mid;// search lower sublist. reset
last
elsefirst = mid+1;// search upper sublist. Reset
first
}
return origLast;// target not found
}
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Big-O notationBig-O notation
For the selection sort, the number of comparisons is T(n) = n2/2 - n/2.
2
n
2
2n
T(n)
Entire expression is called the "Big-O" measure for the algorithm.
** Big-O notation provides a machine independent means for determining the efficiency of an
Algorithm.
n = 100: T(100) = 1002/2 -100/2 = 10000/2 - 100/2 = 5,000 - 50 = 4,950
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Constant Time AlgorithmsConstant Time AlgorithmsAn algorithm is O(1) when its running time is independent of the number of data items. The algorithm runs in constant time.
The storing of the element involves a simple assignment statement and thus has efficiency O(1).
fro nt rear
D irec t In se r t a t R ear
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Linear Time AlgorithmsLinear Time AlgorithmsAn algorithm is O(n) when its running time is proportional to the size of the list.
When the number of elements doubles, the number of operations doubles.
S equential S e arch fo r the Minim um E lem ent in an A rray
32 46 8 12 3
m in im u m elem en t fou n d in th e list a fter n com p a rison s
n = 51 2 3 4 5
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Exponential AlgorithmsExponential AlgorithmsAlgorithms with running time O(n2) are
quadratic.– practical only for relatively small values of n.
Whenever n doubles, the running time of the algorithm increases by a factor of 4.
Algorithms with running time O(n3)are cubic.– efficiency is generally poor; doubling the size
of n increases the running time eight-fold.
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Exponential AlgorithmsExponential Algorithms
n log2n n log2n n2 n3 2n 2 1 2 4 8 4 4 2 8 16 64 16 8 3 24 64 512 256 16 4 64 256 4096 65536 32 5 160 1024 32768 4294967296 128 7 896 16384 2097152 3.4 x 1038
1024 10 10240 1048576 1073741824 1.8 x 10308
65536 16 1048576 4294967296 2.8 x 1014 Forget it!
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Logarithmic Time AlgorithmsLogarithmic Time AlgorithmsThe logarithm of n, base 2, is commonly used when analyzing computer algorithms.Ex. log2(2) = 1
log2(75) = 6.2288
When compared to the functions n and n2, the function log2 n grows very slowly.
nn 2
lo g 2n
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Selection Sort AlgorithmSelection Sort AlgorithmInteger VersionInteger Version
void selectionSort(int arr[], int n) { . . .int temp; // int temp used for the exchange
for (pass = 0; pass < n-1; pass++)
{. . .
if (arr[j] < arr[smallIndex]) // compare integer elements . . .
}
}
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Selection Sort AlgorithmSelection Sort AlgorithmString VersionString Version
void selectionSort(string arr[], int n) { . . .
string temp; // double temp used for the exchange
for (pass = 0; pass < n-1; pass++) {
. . .
if (arr[j] < arr[smallIndex])// compare string element
. . .}
}
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Template SyntaxTemplate Syntax template function syntax includes the keyword
template followed by a non-empty list of formal types enclosed in angle brackets.
In the argument list, each type is preceded by the keyword typename, and types are separated by commas.
// argument list with a multiple template // types
template <typename T, typename U, typename V, ...>
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Template Syntax ExampleTemplate Syntax Example
template <typename T>
void selectionSort(T arr[], int n)
{
int smallIndex; // index of smallest element in the // sublist
int pass, j;
T temp;
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Template Syntax ExampleTemplate Syntax Example
// pass has the range 0 to n-2
for (pass = 0; pass < n-1; pass++)
{
// scan the sublist starting at // index pass
smallIndex = pass;
// j traverses the sublist // a[pass+1] to a[n-1]
for (j = pass+1; j < n; j++)
// update if smaller element found
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Template Syntax ExampleTemplate Syntax Example
if (arr[j] < arr[smallIndex])smallIndex = j;
// if smallIndex and pass are not // the same location, exchange the // smallest item in the sublist
with // arr[pass]
if (smallIndex != pass){
temp = arr[pass];arr[pass] =
arr[smallIndex];arr[smallIndex] = temp; }
} }
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Recursive Definition of the Recursive Definition of the Power FunctionPower Function
A recursive definition distinguishes between the exponent n = 0 (starting point) and n 1 which assumes we already know the value xn-1.
After determining a starting point, each step uses a known power of 2 and doubles it to compute the next result.– Using this process gives us a new definition for the
power function, xn.
We compute all successive powers of x by multiplying the previous value by x.
1,*
0,11 nxx
nx n
n
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Stopping Conditions for Stopping Conditions for Recursive AlgorithmsRecursive Algorithms
Use a recursive function to implement a recursive algorithm. – The design of a recursive function consists
of1. One or more stopping conditions that can be directly evaluated for certain arguments.
2. One or more recursive steps in which a current value of the function can be computed by repeated calling of the function with arguments that will eventually arrive at a stopping condition.
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Implementing the Recursive Implementing the Recursive Power FunctionPower Function
Recursive power():
double power(double x, int n) // n is a non-negative integer
{
if (n == 0)
return 1.0; // stopping condition
else
return x * power(x,n-1);// recursive step
}
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Solving the Tower of Hanoi Solving the Tower of Hanoi Puzzle using RecursionPuzzle using Recursion
N eed le A
. . . . . . . .
N eed le CN eed le B N eed le C
. . . . . . . .
N eed le BN eed le A
N e e dle A N e e dle B N e e dle C
1
N e e dle B N e e dle CN e e dle A
2
3
N e e dle A N e e dle B N e e dle CN e e dle A N e e dle B N e e dle C
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Solving the Tower of Hanoi Solving the Tower of Hanoi Puzzle using RecursionPuzzle using Recursion
N eed le A
. . . . . . . .
N eed le CN eed le B N eed le C
. . . . . . . .
N eed le BN eed le A
N e e dle A N e e dle B N e e dle C N e e dle A N e e dle B N e e dle C
4
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Solving the Tower of Hanoi Solving the Tower of Hanoi Puzzle using RecursionPuzzle using Recursion
N eed le A
. . . . . . . .
N eed le CN eed le B N eed le C
. . . . . . . .
N eed le BN eed le A
N eed le A N eed le B N eed le C N eed le A N eed le B N eed le C
56
N eed le A N eed le B N eed le C N eed le A N eed le B N eed le C
7
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Fibonacci Numbers using Fibonacci Numbers using IterationIteration
int fibiter(int n)
{// integers to store previous two
// Fibonacci value
int oneback = 1, twoback = 1, current;
int i;
// return is immediate for first two numbers
if (n == 1 || n == 2)
return 1;
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Fibonacci Numbers using Fibonacci Numbers using IterationIteration
else// compute successive terms beginning at 3
for (i = 3; i <= n; i++)
{
current = oneback + twoback;
twoback = oneback;// update for next calculation
oneback = current;
}
return current;
}
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Summary Slide 1Summary Slide 1
§- The simplest form of searching is the sequential search.
§- It compares the target with every element in a list until matching the target or reaching the end of
the list.
§- If the list is in sorted order, the binary search algorithm is more efficient.
§- It exploits the structure of an ordered list to produce very fast search times.
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Summary Slide 2Summary Slide 2
§- Big-O notation measures the efficiency of an algorithm by estimating the number of certain
operations that the algorithm must perform.
- For searching and sorting algorithms, the operation is data comparison.
- Big-O measure is very useful for selecting among competing algorithms.
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Summary Slide 3Summary Slide 3
§- The running time of the sequential search is O(n) for the worst and the average cases.
§- The worst and average case for the binary search is O(log2n).
§- Timing data obtained from a program provides experimental evidence to support the greater
efficiency of the binary search.
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Summary Slide 4Summary Slide 4
§- C++ provides a template mechanism that allows a programmer to write a single version of a function with general type arguments.
- If a main program wants to call the function several times with different runtime arguments, the compiler looks at the types of the runtime arguments and creates different versions of the function that matches the types.
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Summary Slide 5Summary Slide 5
§- An algorithm is recursive if it calls itself for smaller problems of its own type.
§- Eventually, these problems must lead to one or more stopping conditions.
- The solution at a stopping condition leads to the solution of previous problems.
- In the implementation of recursion by a C++ function, the function calls itself.
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