Description
1. (25 pts)
(a) In CRLS do Exercise 3.1-2 on page52, Problem 3-2 on page 61, Problem 3.2-7
(b) Place the following functions in order from asymptotically smallest to largest – using
f(n) ∈ O(g(n) notation. When two functions have the same asymptotic order, put an
equal sign between them.
n
2 + 3n log(n) + 5 , n2 + n
−2
, nn
2
+ n! , n
1
n , nn
2−1
, ln n , ln (ln n) , 3
ln n
, 2
n
,
(1 + n)
n
, n1+cos n
,
log
Xn
k=1
n
2
2
k
, 1 , n2 + 3n + 5 , log(n!) ,
Xn
k=1
1
k
,
Yn
k=1
(1 −
1
k
2
) , (1 − 1/n)
n
(c) Substitute
T(n) = c1n + c2 nlog2(n)
into T(n) = 2T(n/2) + n to find the values of c1, c2 to determine the exact solution.
(d) Extra Credit(10 pt): Generalize this to the case for T(n) = aT(n/b) + n
k with trial
solution
T(n) = c1n
γ + c2n
k
using b
γ = a. What happens when γ = k?
Now set γ = k but start over with the guess T(n) = c1n
γ + c2n
γ
log2(n) to determine
new values of c1, c2.
2. (25 pts) Here is a classical problem that is often part of interviews. You are given n nuts
and n bolts, such that one and only one nut fits each bolt. Your only means of comparing
these nuts and bolts is with a function TEST(x, y), where x is a nut and y is a bolt. The
function returns +1 if the nut is too big, 0 if the nut fits, and -1 if the nut is too small.
Design and analyze an algorithm for sorting the nuts and bolts from smallest to largest using
the TEST function, such that the worst case performance of the algorithm has asymptotic
complexity O(n
2
).
Pseudo-code.
You can also do a version of quick-sort, by picking a bolt and pivoting the nut array, and
similarly picking a nut and pivoting the bolt array.
1
3. (50 pts) Binary search of a large sorted array is a classic devide and conquer algorithms.
Given a value called the key you search for a match in an array int a[N] of N objects
by searching sub-arrays iteratively. Starting with left = 0 and right = N-1 the array is
divides at the middle m = (right + left)/2. The routine, int findBisection(int key,
int *a, int N) returns either the index position of a match or failure, by returning m =
-1. First write a function for bisection search. The worst case is O(log N) of course. Next
write a second function, int findDictionary(int key, int *a, int N) to find the key
faster, using what is called, Dictionary search. This is based on the assumption of an
almost uniform distribution of number of in the range of min = a[0] and max = a[N-1].
Dictionary search makes a better educated search for the value of key in the interval between
a[left] and a[right] using the fraction change of the value, 0 ≤ x ≤ 1:
x = double(key – a[left])/(double(a[right]) – a[left]);
to estimate the new index,
m = int(left + x * (right – left)); // bisection uses x = 1/2
Write the function int findDictionary(int key, (int *a, int N) for this. For a uniform sequence of numbers this is with average performance: (log(log( N)), which is much
faster than log(N) bisection algorithm. Graph the data to see this scaling behavior. –
much more fun that a bunch of numbers! You may use any graphing routine you like but
in class we will discuss how to use gnuplot which is a basic unix tool. ( See gnuplot
instructions on GitHub at GeneralComputingInfo/Plotting_and_Fitting ),
Implement your algorithm as a C/C++ functions. On the class GitHub there is the
main file that reads input and writes output the result. You only write the required
functions. Do not make any changes to the infield reading format or the outfile
writing format in main() . Place your final code in directory HW1. The grader will
copy this and run∗
the makeFind to verify that the code is correct. There are 3 input
files Sorted100.txt , Sorted100K.txt, Sorted1M.txt for N = 102
, 105
, 106
respectively.
You should report on the following:
(1)The code should run for each file on the command line.
(2)The code should print to a file a table of the 100 random keys,
execution time and the number of bisections.
(3)And another simliar file for dictionary with “bisections” the number of
dictionary sections
(In the future we use this basic procedure to some data analysis by running it for many
values of N and many values of keys and to plot it to see as best we can the claimed scaling
of log(N) and (log(log( N)) respectively. This require more instruction in how to plot
data and do curve fitting and even error analysis. These skills will be useful later in the
class project phase.)
2 EC 504
