The Hadamard matricesH0,H1,H2, are defined as follows:

  • H0 is the 1×1matrix[1]
  • For k>0,Hkisthe2k×2k matrix

localid="1658916810283" Hk=[Hk-1|Hk-1Hk-1|-Hk-1]

Show that if υ is a column vector of lengthlocalid="1658916598888" n=2k, then the matrix-vector product localid="1658916618774" Hkvcan be calculated using localid="1658916637767" O(nlogn) operations. Assume that all the numbers involved are small enough that basic arithmetic operations like addition and multiplication take unit time.

Short Answer

Expert verified

If is a column vector of length n=2k, then the matrix-vector product Hkv is calculated using role="math" localid="1658916653689" O(nlogn)operations.

Step by step solution

01

Explain Hadamard matrices.

The Hadamard matrix H0 is the matrix. Consider the 2k×2k matrix is defined asHk.,fork>0

. The Hadamard matrices should be defined with the mentioned properties as follows,

localid="1658916992923" Hk=[Hk-1|Hk-1Hk-1|-Hk-1]

02

Show that the matrix-vector product   can be calculated using   operations.

Consider the Hadamard matrices H0,H1,H2,defined as follows,

Hk=[Hk-1|Hk-1Hk-1|-Hk-1]

Consider that If is a column vector of length n=2k,, then the matrix-vector product Hkv is calculated as follows,

HkV=Hk-1Hk-1Hk-1-Hk-1VuVd=Hk-1vu+VdHk-1vu+Vd

The above calculation has the arithmetic operations that takes unit time. Then the complexity of operations can be calculated as follows,

Tn=2Tn2+On=Onlogn

Therefore, the matrix-vector product Hkv is calculated usingOnlognoperations.

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Most popular questions from this chapter

Question: Use the divide-and-conquer integer multiplication algorithm to multiply the two binary integers 10011011and10111010 and .

Suppose we want to evaluate the polynomial P(x) = a0 + a1x + a2x2 + ... + anxn at point x.

  1. Show that the following simple routine, known as Horner’s rule, does the job and leaves the answer in z.
    z = an
    for I = n-1 down to 0 :
    z = zx + ai
  2. How many additions and multiplications does this routine use, as a function of n ? Can you find a polynomial for which an alternative method is substantially better?

In Section 1.2.3, we studied Euclid’s algorithm for computing the greatest common divisor (gcd) of two positive integers: the largest integer which divides them both. Here we will look at an alternative algorithm based on divide-and-conquer.

(a) Show that the following rule is true.

gcd(a,b)={2gcd(a2,b2)ifa,bareevengcd(ab2)ifaisodd,bisevengcd(a-b2,b)ifa,bareodd

(b) Give an efficient divide-and-conquer algorithm for greatest common divisor.

(c) How does the efficiency of your algorithm compare to Euclid’s algorithm if a and b are n-bit -bit integers? (In particular, since n might be large you cannot assume that basic arithmetic operations like addition take constant time.)

Practice with polynomial multiplication by FFT.

(a) Suppose that you want to multiply the two polynomials x + 1 and x2+1using the FFT. Choose an appropriate power of two, find the FFT of the two sequences, multiply the results component wise, and compute the inverse FFT to get the final result.

(b) Repeat for the pair of polynomials 1+x+2x2and 2 + 3x.

Suppose you are choosing between the following three algorithms: • Algorithm A solves problems by dividing them into five sub-problems of half the size, recursively solving each sub-problem, and then combining the solutions in linear time. •

Algorithm B solves problems of size n by recursively solving two sub-problems of size n-1and then combining the solutions in constant time. • Algorithm C solves problems of size n by dividing them into nine sub-problems of size n/3, recursively solving each sub-problem, and then combining the solutions in O(n2)time.

What are the running times of each of these algorithms (in big- O notation), and which would you choose?

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