Short Notes: Algorithms-Divide & Conquer | Short Notes for Computer Science Engineering - Computer Science Engineering (CSE) PDF Download

 Page 1


Algorithms : Divide and Conquer
Divide and Conquer Design Technique:
Divides the problem into smaller but similar sub problems (divide), solve it 
(conquer), and (combine) these solutions to create a solution to the original 
problem.
• Divide: Breaking the problem into several sub-problems that are similar to the 
original problem but smaller in size,
• Conquer: Solve the sub-problem recursively (successively and 
independently). If the sub problems are small enough, solve them in brute 
force fashion.
• Combine: Combine these solutions to sub-problems to create a solution to the 
original problem.
Divide & Conquer Algorithms:
• Merge sort
• Quicksort
• Binary search
• Multiplication of large integers
• Matrix multiplication: Strassen’s algorithm
• Closest-pair algorithms
Divide & Conquer Algorithms can be solved using master theorem to find the 
performance of algorithms.
Page 2


Algorithms : Divide and Conquer
Divide and Conquer Design Technique:
Divides the problem into smaller but similar sub problems (divide), solve it 
(conquer), and (combine) these solutions to create a solution to the original 
problem.
• Divide: Breaking the problem into several sub-problems that are similar to the 
original problem but smaller in size,
• Conquer: Solve the sub-problem recursively (successively and 
independently). If the sub problems are small enough, solve them in brute 
force fashion.
• Combine: Combine these solutions to sub-problems to create a solution to the 
original problem.
Divide & Conquer Algorithms:
• Merge sort
• Quicksort
• Binary search
• Multiplication of large integers
• Matrix multiplication: Strassen’s algorithm
• Closest-pair algorithms
Divide & Conquer Algorithms can be solved using master theorem to find the 
performance of algorithms.
Master theorem.
T (n ) = a T ( ^ \ + f(n),
for constants a (> 1) ) and 6 (> 1) with / asymptotically positive, 
the following statements are true:
Case 1, if f(n ) = 0 ( n io&,n_'') for some e > 0, then r ( n ) — 0 (n 1 ^ 0}. 
Case 2. If f(n ) = 0 (n lo g * “ ) , then T ’(n ) = 0 (n lo & > “ log n ) .
Case 3. If f(n ) = ft ( t i1 " ® * a+< j for some e > 0
(and « / ( j ) < cf(n) far some c < 1 for all n sufficiently large). then 
T ( n ) = 0 ( / ( n ) ) .
Maximum and Minimum:
MaxMin(i, j, max, min)
{
if (i=j) then max := min := a[i]; 
else if (i=j-1) then 
{
if (a[i] < a[j]) then max := a[j]; min := a[i]; 
else max := a[i]; min := a[j];
}
else
{
mid := ( i + j )/2;
// Solve the sub-problems.
MaxMin( i, mid, max, min );
MaxMin( mid+1, j, maxi, mini );
// Combine the solutions.
if (max < m axi) then max := maxi;
if (min > m ini) then min := mini;
}
}
Time Complexity:
T(n ) = 0; if n=1 
= 1; if n=2 
=2T(n/2)=2; if n>2
When n is a power of two, n = 2k , for some positive integer k, then 
T(n ) = 2T(n/2) + 2
= 2(2T(n/4) + 2) + 2 
= 4T(n/4) + 4 + 2
Page 3


Algorithms : Divide and Conquer
Divide and Conquer Design Technique:
Divides the problem into smaller but similar sub problems (divide), solve it 
(conquer), and (combine) these solutions to create a solution to the original 
problem.
• Divide: Breaking the problem into several sub-problems that are similar to the 
original problem but smaller in size,
• Conquer: Solve the sub-problem recursively (successively and 
independently). If the sub problems are small enough, solve them in brute 
force fashion.
• Combine: Combine these solutions to sub-problems to create a solution to the 
original problem.
Divide & Conquer Algorithms:
• Merge sort
• Quicksort
• Binary search
• Multiplication of large integers
• Matrix multiplication: Strassen’s algorithm
• Closest-pair algorithms
Divide & Conquer Algorithms can be solved using master theorem to find the 
performance of algorithms.
Master theorem.
T (n ) = a T ( ^ \ + f(n),
for constants a (> 1) ) and 6 (> 1) with / asymptotically positive, 
the following statements are true:
Case 1, if f(n ) = 0 ( n io&,n_'') for some e > 0, then r ( n ) — 0 (n 1 ^ 0}. 
Case 2. If f(n ) = 0 (n lo g * “ ) , then T ’(n ) = 0 (n lo & > “ log n ) .
Case 3. If f(n ) = ft ( t i1 " ® * a+< j for some e > 0
(and « / ( j ) < cf(n) far some c < 1 for all n sufficiently large). then 
T ( n ) = 0 ( / ( n ) ) .
Maximum and Minimum:
MaxMin(i, j, max, min)
{
if (i=j) then max := min := a[i]; 
else if (i=j-1) then 
{
if (a[i] < a[j]) then max := a[j]; min := a[i]; 
else max := a[i]; min := a[j];
}
else
{
mid := ( i + j )/2;
// Solve the sub-problems.
MaxMin( i, mid, max, min );
MaxMin( mid+1, j, maxi, mini );
// Combine the solutions.
if (max < m axi) then max := maxi;
if (min > m ini) then min := mini;
}
}
Time Complexity:
T(n ) = 0; if n=1 
= 1; if n=2 
=2T(n/2)=2; if n>2
When n is a power of two, n = 2k , for some positive integer k, then 
T(n ) = 2T(n/2) + 2
= 2(2T(n/4) + 2) + 2 
= 4T(n/4) + 4 + 2
= 2k _ 1 T(2 ) + £(1SISk-1)2k 
_ 2^-1 + 2k — 2 
= 3n/2 - 2 = 0(n)
Matrix Multiplication:
Simple approach of multiplication:
The product of two n x n matrices X and Y is a third n x n matrix Z = X Y , with (i, j)th 
entry:
n
Z ij ^ ^ Xu-Yfcj.
k = i
• Input: X, Y: Array(1 ... n) of number
• Output: Z: Array(1 ... n) := X x Y
• Obvious algorithm: Z has n2 entries, each of which multiples n pairs
° Zij=ZXik Y k j
• Break X into X II, X12, X21, X22
• Break Y into Y11, Y12, Y21.Y22
• Break Z into Z11, Z12, Z21.Z22
• Let Z = product of two n/2 by n/2 arrays
Z11=X11xY11+X12xY21
Z12=X11xY12+X12xY22
Z21=X21xY11+X22xY21
Z22=X21xY12+X22xY22
• T(n)=0(1)+8T(n/2)+0(n2 )
o 0 (1 ) time to partition matrices 
° 8 Multiplications of arrays of size n/2n/2 
° 0 (n 2 ) time to add nxn matrices
• T(n)= 0 (n 3 ) by master method
Strassen's Algorithm:
Matrix multiplication is particularly easy to break into subproblems, because it can 
be performed block wise. Here divide X into four n/2 x n/2 blocks, and also Y:
A B 
C D ’
Y
E F 
G H
Therefore, the product of X and Y is as follows: •
A B ~E F AE + BG AF + BE
C D G H CE + DG CF + DH
• Uses 7 rather than 8 multiplications of n/2 by n/2 arrays
• Has more additions and subtractions of n/2 by n/2 arrays
Page 4


Algorithms : Divide and Conquer
Divide and Conquer Design Technique:
Divides the problem into smaller but similar sub problems (divide), solve it 
(conquer), and (combine) these solutions to create a solution to the original 
problem.
• Divide: Breaking the problem into several sub-problems that are similar to the 
original problem but smaller in size,
• Conquer: Solve the sub-problem recursively (successively and 
independently). If the sub problems are small enough, solve them in brute 
force fashion.
• Combine: Combine these solutions to sub-problems to create a solution to the 
original problem.
Divide & Conquer Algorithms:
• Merge sort
• Quicksort
• Binary search
• Multiplication of large integers
• Matrix multiplication: Strassen’s algorithm
• Closest-pair algorithms
Divide & Conquer Algorithms can be solved using master theorem to find the 
performance of algorithms.
Master theorem.
T (n ) = a T ( ^ \ + f(n),
for constants a (> 1) ) and 6 (> 1) with / asymptotically positive, 
the following statements are true:
Case 1, if f(n ) = 0 ( n io&,n_'') for some e > 0, then r ( n ) — 0 (n 1 ^ 0}. 
Case 2. If f(n ) = 0 (n lo g * “ ) , then T ’(n ) = 0 (n lo & > “ log n ) .
Case 3. If f(n ) = ft ( t i1 " ® * a+< j for some e > 0
(and « / ( j ) < cf(n) far some c < 1 for all n sufficiently large). then 
T ( n ) = 0 ( / ( n ) ) .
Maximum and Minimum:
MaxMin(i, j, max, min)
{
if (i=j) then max := min := a[i]; 
else if (i=j-1) then 
{
if (a[i] < a[j]) then max := a[j]; min := a[i]; 
else max := a[i]; min := a[j];
}
else
{
mid := ( i + j )/2;
// Solve the sub-problems.
MaxMin( i, mid, max, min );
MaxMin( mid+1, j, maxi, mini );
// Combine the solutions.
if (max < m axi) then max := maxi;
if (min > m ini) then min := mini;
}
}
Time Complexity:
T(n ) = 0; if n=1 
= 1; if n=2 
=2T(n/2)=2; if n>2
When n is a power of two, n = 2k , for some positive integer k, then 
T(n ) = 2T(n/2) + 2
= 2(2T(n/4) + 2) + 2 
= 4T(n/4) + 4 + 2
= 2k _ 1 T(2 ) + £(1SISk-1)2k 
_ 2^-1 + 2k — 2 
= 3n/2 - 2 = 0(n)
Matrix Multiplication:
Simple approach of multiplication:
The product of two n x n matrices X and Y is a third n x n matrix Z = X Y , with (i, j)th 
entry:
n
Z ij ^ ^ Xu-Yfcj.
k = i
• Input: X, Y: Array(1 ... n) of number
• Output: Z: Array(1 ... n) := X x Y
• Obvious algorithm: Z has n2 entries, each of which multiples n pairs
° Zij=ZXik Y k j
• Break X into X II, X12, X21, X22
• Break Y into Y11, Y12, Y21.Y22
• Break Z into Z11, Z12, Z21.Z22
• Let Z = product of two n/2 by n/2 arrays
Z11=X11xY11+X12xY21
Z12=X11xY12+X12xY22
Z21=X21xY11+X22xY21
Z22=X21xY12+X22xY22
• T(n)=0(1)+8T(n/2)+0(n2 )
o 0 (1 ) time to partition matrices 
° 8 Multiplications of arrays of size n/2n/2 
° 0 (n 2 ) time to add nxn matrices
• T(n)= 0 (n 3 ) by master method
Strassen's Algorithm:
Matrix multiplication is particularly easy to break into subproblems, because it can 
be performed block wise. Here divide X into four n/2 x n/2 blocks, and also Y:
A B 
C D ’
Y
E F 
G H
Therefore, the product of X and Y is as follows: •
A B ~E F AE + BG AF + BE
C D G H CE + DG CF + DH
• Uses 7 rather than 8 multiplications of n/2 by n/2 arrays
• Has more additions and subtractions of n/2 by n/2 arrays
XY
+ Pi ~ P-i + ¦ F ’ f i P\ + P 2 
Pi + A . Pi + Ps - P3 - P 7
where
Pi =
A{F - H)
P -2 =
(A-t-B)H
Pi - (1 0 + D )E
Pi -
D(G - E)
P> = ( 4 + £ > )(£ + # ) 
Pfi = (B -D )(G + H) 
P7 = (>1 — C )(E + F)
T(n) = 7T(n/2) + 0 (n 4 ),
• T(n)=7T(n/2)+0(n2 ) if n>1
• Solution (by Master Method): T(n )= 0(n 1 0 9 7 ) =0(n2-81)
• Note: There are other matrix multiplication algorithms which can run even 
faster with T(n )= 0(n 2-38)
Maximal Subarray:
Divide and conquer approach to find the maximal subarray: 
maxsub(int[] S; low, high: int)
if low = high then return (low, high, S(low)); // return (lowlndex, highlndex, sum) 
else mid = (low + high) / 2 ;
(How, Ihigh, Isum) = maxsub(S, low, m id );
(rlow, rhigh, rsum) = maxsub(S, mid+1, high );
(mlow, mhigh, msum) = middlemaxsub(S, low, mid, high ); 
end if;
return triple with highest sum 
end maxsub
middlemaxsub(int[] S; low, high, int) return (lowlndex, highlndex, sum)
start at mid and find bestleft and leftsum
start at mid and find bestright and rightsum
return (bestleft, bestright, rightsum+leftsum)
end middlemaxsub
Closest Pair Algorithm:
Input: P = (p(1), p ( 2 ) p ( n ) } where p(i) = ( x(i), y (i)). A set of n points in the 
plane.
Output: The distance between the two points that are closest.
Note: The distance DELTA( / , j ) between p(i) and p(j) is defined by the expression: 
Square root of { (x(i)-x(j))2 + (y(i)-y(j))2 } 
float function closest_pair (P: point set; n: integer) 
float DELTA-LEFT, DELTA-RIGHT; 
float DELTA;
Page 5


Algorithms : Divide and Conquer
Divide and Conquer Design Technique:
Divides the problem into smaller but similar sub problems (divide), solve it 
(conquer), and (combine) these solutions to create a solution to the original 
problem.
• Divide: Breaking the problem into several sub-problems that are similar to the 
original problem but smaller in size,
• Conquer: Solve the sub-problem recursively (successively and 
independently). If the sub problems are small enough, solve them in brute 
force fashion.
• Combine: Combine these solutions to sub-problems to create a solution to the 
original problem.
Divide & Conquer Algorithms:
• Merge sort
• Quicksort
• Binary search
• Multiplication of large integers
• Matrix multiplication: Strassen’s algorithm
• Closest-pair algorithms
Divide & Conquer Algorithms can be solved using master theorem to find the 
performance of algorithms.
Master theorem.
T (n ) = a T ( ^ \ + f(n),
for constants a (> 1) ) and 6 (> 1) with / asymptotically positive, 
the following statements are true:
Case 1, if f(n ) = 0 ( n io&,n_'') for some e > 0, then r ( n ) — 0 (n 1 ^ 0}. 
Case 2. If f(n ) = 0 (n lo g * “ ) , then T ’(n ) = 0 (n lo & > “ log n ) .
Case 3. If f(n ) = ft ( t i1 " ® * a+< j for some e > 0
(and « / ( j ) < cf(n) far some c < 1 for all n sufficiently large). then 
T ( n ) = 0 ( / ( n ) ) .
Maximum and Minimum:
MaxMin(i, j, max, min)
{
if (i=j) then max := min := a[i]; 
else if (i=j-1) then 
{
if (a[i] < a[j]) then max := a[j]; min := a[i]; 
else max := a[i]; min := a[j];
}
else
{
mid := ( i + j )/2;
// Solve the sub-problems.
MaxMin( i, mid, max, min );
MaxMin( mid+1, j, maxi, mini );
// Combine the solutions.
if (max < m axi) then max := maxi;
if (min > m ini) then min := mini;
}
}
Time Complexity:
T(n ) = 0; if n=1 
= 1; if n=2 
=2T(n/2)=2; if n>2
When n is a power of two, n = 2k , for some positive integer k, then 
T(n ) = 2T(n/2) + 2
= 2(2T(n/4) + 2) + 2 
= 4T(n/4) + 4 + 2
= 2k _ 1 T(2 ) + £(1SISk-1)2k 
_ 2^-1 + 2k — 2 
= 3n/2 - 2 = 0(n)
Matrix Multiplication:
Simple approach of multiplication:
The product of two n x n matrices X and Y is a third n x n matrix Z = X Y , with (i, j)th 
entry:
n
Z ij ^ ^ Xu-Yfcj.
k = i
• Input: X, Y: Array(1 ... n) of number
• Output: Z: Array(1 ... n) := X x Y
• Obvious algorithm: Z has n2 entries, each of which multiples n pairs
° Zij=ZXik Y k j
• Break X into X II, X12, X21, X22
• Break Y into Y11, Y12, Y21.Y22
• Break Z into Z11, Z12, Z21.Z22
• Let Z = product of two n/2 by n/2 arrays
Z11=X11xY11+X12xY21
Z12=X11xY12+X12xY22
Z21=X21xY11+X22xY21
Z22=X21xY12+X22xY22
• T(n)=0(1)+8T(n/2)+0(n2 )
o 0 (1 ) time to partition matrices 
° 8 Multiplications of arrays of size n/2n/2 
° 0 (n 2 ) time to add nxn matrices
• T(n)= 0 (n 3 ) by master method
Strassen's Algorithm:
Matrix multiplication is particularly easy to break into subproblems, because it can 
be performed block wise. Here divide X into four n/2 x n/2 blocks, and also Y:
A B 
C D ’
Y
E F 
G H
Therefore, the product of X and Y is as follows: •
A B ~E F AE + BG AF + BE
C D G H CE + DG CF + DH
• Uses 7 rather than 8 multiplications of n/2 by n/2 arrays
• Has more additions and subtractions of n/2 by n/2 arrays
XY
+ Pi ~ P-i + ¦ F ’ f i P\ + P 2 
Pi + A . Pi + Ps - P3 - P 7
where
Pi =
A{F - H)
P -2 =
(A-t-B)H
Pi - (1 0 + D )E
Pi -
D(G - E)
P> = ( 4 + £ > )(£ + # ) 
Pfi = (B -D )(G + H) 
P7 = (>1 — C )(E + F)
T(n) = 7T(n/2) + 0 (n 4 ),
• T(n)=7T(n/2)+0(n2 ) if n>1
• Solution (by Master Method): T(n )= 0(n 1 0 9 7 ) =0(n2-81)
• Note: There are other matrix multiplication algorithms which can run even 
faster with T(n )= 0(n 2-38)
Maximal Subarray:
Divide and conquer approach to find the maximal subarray: 
maxsub(int[] S; low, high: int)
if low = high then return (low, high, S(low)); // return (lowlndex, highlndex, sum) 
else mid = (low + high) / 2 ;
(How, Ihigh, Isum) = maxsub(S, low, m id );
(rlow, rhigh, rsum) = maxsub(S, mid+1, high );
(mlow, mhigh, msum) = middlemaxsub(S, low, mid, high ); 
end if;
return triple with highest sum 
end maxsub
middlemaxsub(int[] S; low, high, int) return (lowlndex, highlndex, sum)
start at mid and find bestleft and leftsum
start at mid and find bestright and rightsum
return (bestleft, bestright, rightsum+leftsum)
end middlemaxsub
Closest Pair Algorithm:
Input: P = (p(1), p ( 2 ) p ( n ) } where p(i) = ( x(i), y (i)). A set of n points in the 
plane.
Output: The distance between the two points that are closest.
Note: The distance DELTA( / , j ) between p(i) and p(j) is defined by the expression: 
Square root of { (x(i)-x(j))2 + (y(i)-y(j))2 } 
float function closest_pair (P: point set; n: integer) 
float DELTA-LEFT, DELTA-RIGHT; 
float DELTA;
begin
if n = 2 then
return distance from p(1) to p(2); 
else
P-LEFT := ( p(1), p (2 ).....p(n/2));
P-RIGHT := ( p(n/2+1), p(n/2+2).....p (n ));
DELTA-LEFT := closestpair( P-LEFT, n/2 );
DELTA-RIGHT := closestpair( P-RIGHT, n/2 );
DELTA := minimum ( DELTA-LEFT, DELTA-RIGHT);
/* Determine whether there are points p(l) in P-LEFT and p(r) in P-RIGHT with
distance( p(l), p (r )) < DELTA. If there are such points, set DELTA to be the 
smallest
distance.*/ 
return DELTA; 
end if;
end closest_pair;
The section between the two comment lines is the 'combine' stage of the Divide- 
and-Conquer algorithm.
If there are points p(l) and p(r) whose distance apart is less than DELTA then it 
must be the case that
• The x-coordinates of p(l) and p(r) differ by at most DELTA.
• The y-coordinates of p(l) and p(r) differ by at most DELTA.
Integer Multiplication:
Multiplying two n-bit integers algorithm using divide and conquer approach is given 
below:
multiply
Input: Positive integers r and y , in binary
Output: Their product
n = max(size of x , size of y) 
if n= 1: return
XLf Xu = leftmost fn/2], rightmost [n/2J bits of a : 
yt., >j b — leftmost fn/2], rightmost [u/2j bits of y
P\ = m u ltip ly ^ , y,.}
P2 = m u ltip ly ^ * , ;;*)
P3 = multiply^ + yL + yK)
return Pt x 2" + - P2) x 2’1 /2 + A
T(n) = 3T(n/2) + 0(n).
Summary:
Algorithm
Worst-case running Best-case Average-case
time running time running time