CS 235102
Data Structures
(資料結構)
Chapter 2:
Arrays and Structures
Spring 2012
Arrays

One Dimensional Array:


int A[10];
Two dimensional Array:

int A[10][25];
1
Polynomial
– Abstract Data Type (ADT)

Object: Polynomial.

Operations:




Boolean IsZero(poly)
::= return FALSE or TRUE.
Coefficient Coeff(poly, expon)
::= return coefficient of xexpon
Polynomial Add(poly1, poly2)
::= return poly1 + poly2
Polynomial Subtract(poly1, poly2)
::= return poly1 - poly2
2
First Representation as Arrays

Unique exponent arranged in decreasing order:
Coefficient
……
MaxDeg MaxDeg-1
0
degrees
degree (the degree of the polynomial)

Eg.
P(x)=x5+4x3+2x2+1
degree = 5
0
…
1
0
4
2
0
1
3
First Representation as Arrays

In C language:
#define MAXDEGREE 101
typedef struct {
int degree;
int coef[MAXDEGREE]
} polynomial;

Operations: addition, subtraction …


Easy to carry out
Just adding/subtracting the corresponding
terms, respectively. (See next slide …)
4
First Representation as Arrays

Eg. Addition of two polynomials:
polynomial addp(polynomial a, polynomial b)
{ polynomial c;
c.degree = max(a.degree, b.degree)
for (i=0; i<=MAXDEGREE; i++)
c.coef[i] = a.coef[i] + b.coef[i];
return c;
}

Disadvantage:

Sparse polynomial ==> waste of space !!
5
2nd Representation As Arrays


Only represent non-zero terms
Need to represent non-zero exponents and its
corresponding coefficients

Eg. A(x) = 2x1000 + 1
B(x) = x4 + 10x3 + 3x2 + 1
startA
startB
finishA
finishB
avail
Coef
2
1
1
10
3
1
…
Exp
1000
0
4
3
2
0
…
6
2nd Representation As Arrays

In C:
typedef struct {
int coeff, exp;
} polynomial;
polynomial terms[MAX_TERMS];
int avail = 0;

Comparisons of two representations:


If polynomial sparse, 2nd repre. is better.
If polynomial full, 2nd repre. is double size of 1st.
7
Addition of Polynomials (padd)

Operation: C(x) = A(x) + B(x)

Eg. A(x) = x5 + 9x4 + 7x3 + 2x
B(x) = x6 + 3x5 + 6x + 3
padd()
{ p  point to head of A
q  point to head of B
while ( !IsZero(p) && !IsZero(q) ) {
switch (compare(Exps of p and q) ) {
“>”: attach p to C;
advance p to next element in A; break;
“<”: attach q to C;
advance q to next element in B; break;
“=”: add coefs of p and q;
attach it to C;
advance p to next element in A;
advance q to next element in B; }
}
Attach remaining terms of A or B into C;
}
8
Addition of Polynomials (padd)

An running example for padd(): C(x) = A(x) + B(x)
A(x) = x5 + 9x4 + 7x3 + 2x
p
p
p
B(x) = x6 + 3x5 + 6x + 3
q
q
q
p
p
q
C(x) = x6 + (1+3)x5 + 9x4 + 7x3 + (2+6)x + 3
= x6 + 4x5 + 9x4 + 7x3 + 8x + 3
9
Time Complexity of padd


Inside the while loop:
O(1) time
How many times the “while loop” is executed in
the worst case ?





Let A(x) have m terms, and
B(x) have n terms.
In each iteration, we access next element
in A(x) or B(x), or both.
Worst case: m + n – 1
eg. It happens when
A(x) = 7x5 + x3 + x; B(x) = x6 + 2x4 + 6x2 +3
Remaining terms in A(x):
O(m)
Remaining terms in B(x):
O(n)
Hence, total running time = O(m + n)
10
Matrix

Object: Matrix.


– Abstract Data Type (ADT)
dimension = #rows x #cols
col. 1
col. 0
col. 2
-27 3
6 82
A = 109 -64
Operations:
12
8
48 27
 Matrix Transpose(matrixA)
::= return transpose of matrixA


4
-2
11
9
47
row 0
row 1
row 2
row 3
row 4
Matrix Add(matrixA, matrixB)
::= return (matrixA + matrix B)
Matrix Multiply(matrixA, matrixB)
::= return (matrixA * matrixB)
11
Matrix Representation

We use arrays to represent matrices.

Use array a[M][N] to store a matrix A(M, N).

Use a[i][j] to store A(i, j).
12
Operations: Transpose & Add

c transpose(a)
// a: m x n matrix
for (i=0; i<rowA; i++)
for (j=0; j<colA; j++)
c[j][i]=a[i][j];
// O(m)
// O(n)
Running time = O(mn)

c add(a, b)
// a, b: m x n matrices
for (i=0; i<rowA; i++)
// O(m)
for (j=0; j<colA; j++)
// O(n)
c[i][j]=a[i][j]+b[i][j];
Running time = O(mn)
13
Operations: Multiply

c multiply(a, b)
//a: m x n mat., b: n x p mat.
x
c: m x p mat.
=
for (i=0; i<rowA; i++) {
// O(m)
for (j=0; j<colB; j++) {
// O(p)
sum=0;
for (k=0; k<colA; k++)
// O(n)
sum += a[i][k]*b[k][j];
c[i][j]=sum;
}
}
Running time = O(mnp)
14
Sparse Matrices

An example sparse matrix:
A=


15
0
0
0
91
0
0
11
0
0
0
0
0 22
3 0
0 -6
0 0
0 0
28 0
0 -15
0
0
0
0
0
0
0
0
0
0
A lot of “zero” entries.
Thus large memory space is wasted.
Could we use other representation to save
memory space ??
15
Representation for Sparse Matrices


Use triple <row, col, value> to characterize an
element in the matrix.
Use array of triples a[] to represent a matrix.


row by row
within a row,
column by column
row
a[0]
a[1 ]
a[2]
a[3]
a[4]
a[5]
a[6]
a[7]
a[8]
6
0
0
0
1
1
2
4
5
col value
6
0
3
5
1
2
3
0
2
8
15
22
-15
11
3
-6
91
28
16
Representation for Sparse Matrices

In C:
typedef struct {
int col, row, value;
} term;
term a[MAX_TERMS];
17
Operations: Transpose

c transpose(a)
// a: m x n matrix
//Algorithm 1:
for each row i {
take element (i, j, value) and
store it as (j, i, value).
}
row col value
row col value

Eg.
a[0]
a[1 ]
a[2]
a[3]
a[4]
a[5]
a[6]
a[7]
a[8]
6
0
0
0
1
1
2
4
5
6
0
3
5
1
2
3
0
2
8
15
22
-15
11
3
-6
91
28
c[0]
c[1 ]
c[2]
c[3]
c[4]
c[5]
c[6]
c[7]
c[8]
6
0
3
5
1
2
3
0
2
6
0
0
0
1
1
2
4
5
8
15
22
-15
11
3
-6
91
28
18
Operations: Transpose

Problem:

If we just place them consecutively, we need to
do a lot of insertions to make the ordering
right.
row col value
c[0]
c[1 ]
c[2]
c[3]
c[4]
c[5]
c[6]
c[7]
c[8]
6
0
3
5
1
2
3
0
2
6
0
0
0
1
1
2
4
5
8
15
22
-15
11
3
-6
91
28
19
Alg. 2 for Transpose

Algorithm 2:
a[0]
a[1 ]
a[2]
a[3]
a[4]
a[5]
a[6]
a[7]
a[8]

row col value
6
0
0
0
1
1
2
4
5
6
0
3
5
1
2
3
0
2
8
15
22
-15
11
3
-6
91
28
row col value
c[0]
c[1 ]
c[2]
c[3]
c[4]
c[5]
c[6]
c[7]
c[8]
6
0
0
1
2
2
3
3
5
6
0
4
1
1
5
0
2
0
8
15
91
11
3
28
22
-6
-15
Find all elements in col. 0, and store them in row 0;
Find all elements in col. 1, and store them in row 1;
… … … … etc
20
Alg. 2 for Transpose

Algorithm 2 in C:
for (j=0; j<#col; j++) {
//O(#col)
for all elements in col j { //O(#terms)
place element (i, j, value) in the
next position of array c[];
}
}
Running time = O(#col x #terms)
21
Alg. 3: Fast Transpose

Algorithm 3:



c transpose(a)
Find number of terms in a row in c[], and then
calculate the starting position of each row in c[].
Put the terms in a[] into correct position in c[]
Scan through all terms in a[] only twice.
row col value
a[0]
a[1 ]
a[2]
a[3]
a[4]
a[5]
a[6]
a[7]
a[8]
6
0
0
0
1
1
2
4
5
6
0
3
5
1
2
3
0
2
8
15
22
-15
11
3
-6
91
28
[0]
[1]
[2]
[3]
[4]
[5]
rowTerms
2
1
2
2
0
1
rowStart
1
3
4
6
8
8
22
Alg. 3: Fast Transpose

Algorithm 3:

c transpose(a)
Positions of rowStart[i]
row col value
c[0]
c[1 ]
c[2]
c[3]
c[4]
c[5]
c[6]
c[7]
c[8]

6
0
6
0
8
15
rowStart[0] = 1
rowStart[1] = 3
rowStart[2] = 4
rowStart[3] = 6
rowStart[4] = rowStart[5] = 8
rowStart[i] increment by 1 after it is occupied.
23
Alg. 3: Fast Transpose

Algorithm 3 in C:
FastTranspose(term a[], term c[])
{ initialize c[0];
initialize rowTerms[] to 0;
for ( i=1; i<=#terms; i++ )
rowTerms[a[i].col] += 1;
Compute rowStart[] from rowTerms[];
for ( i=1; i<=#terms; i++ ) {
assign data in a[i] to c[rowStart[a[i].col]];
rowStart[a[i].col] += 1;
}
}
24
Alg. 3: Fast Transpose

Algorithm 3 in C:
FastTranspose(term a[], term c[])
{ initialize c[0];
initialize rowTerms[] to 0;
// O(#col)
for ( i=1; i<=#terms; i++ )
rowTerms[a[i].col] += 1;
// O(#terms)
Compute rowStart[] from rowTerms[];
// O(#col)
for ( i=1; i<=#terms; i++ ) {
// O(#terms)
assign data in a[i] to c[rowStart[a[i].col]];
rowStart[a[i].col] += 1;
}
}
Running time = O(#col + #terms)
25
Running Times: Alg. 3 vs 2D-Array-Alg.



Alg. 3:
2D-array-alg.:
O(#col + #terms)
O(#col x #rows)
When #terms = #col x #rows (dense matrix),
 Alg. 3 has same time complexity as 2D-array-alg.
When #terms is small (sparse matrix),

Alg. 3 is faster than 2D array representation.
26
Fast Multiply for Sparse Matrices

c multiply(a, b)
//a: m x n mat., b: n x p mat.
x
c: m x p mat.
=
FastMultiply(term a[], term b[], term c[])
{ FastTranspose(b, newB);
for each row i of a[] {
for each row j of newB[] {
multiply row i & row j using similar
proc. as padd(); //see next slide for Eg.
}
}
}
“Good for sparse”
Running time = O(rows(a) x terms(b) + cols(b) x terms(a) )
For details, please refer to textbook !!
27
An Example for Fast Multiply
c FastMultiply(a, b)

c: m x p mat.
a: m x n mat.
x
0 5 2 0 0 7
=
b: n x p mat.
3
0
4
3
6
5
28
An Example for Fast Multiply
c FastMultiply(a, b)

c: m x p mat.
x
=
a: m x n mat.
newB: p x n mat.
0 5 2 0 0 7
3 0 4 3 6 5
p p
p
q
q q q q
x = (2)(4) + (7)(5) = 43
29
Time Complexity of Fast Multiply
a: m x n mat. newB: pxn mat.
c: m x p mat.


q
for computing one row of c = terms(b)
for computing all rows of c = rows(a) x terms(b)
Similarly, # of jumps of p = cols(b) x terms(a)
c: m x p mat.

=
# of jumps of q :


newB
=
newB
p
_Total time=O(rows(a) x terms(b) + cols(b) x terms(a) )
30