Empirical analyses of algorithms involves implementing, running and then analyzing the run-time data collected against theoretical predictions. This homework asks you to find/design, implement, and theoretically and empirically compute the complexities of three algorithms with different orders of complexity to solve the problem of Maximum Subsequence Sum defined as follows:
Maximum Subsequence Sum (MSS) Problem: Compute the sum of the of the subsequence of numbers (a subsequence is a consecutive set of numbers) in an array of numbers (for this HW, we will use integers) that sum to the largest value possible. E.g., in an array of all positive (or negative) numbers , this subsequence consists of all the positive numbers (or the largest negative integer if all are negative). There may be multiple subsequences in an array, all of which sum to the same largest sum. E.g., in an array of all zeroes, MSS=0. Remember that the input array may contain zeroes, negative and/or positive integers. For example, if the input is [1,2,-4,3,-5,2,0] then MSS=3 and there are two subsequences that sum to the same MSS: A+A or A=3.
1. Implement the provided algorithms of different complexity orders to solve this problem. The implementation should be faithful to the algorithms provided in this homework; use the same variable names; if you turn in code that implements different algorithms, perhaps you find on the web, then you will get a zero.
2. Calculate T(n) and the order of complexity of each algorithm in the big-Oh or Theta notation using any of the methods we discussed in class. (a) Show your calculations in the provided tables, then (b) determine and state the polynomial T(n) and (c) the complexity order of the algorithm. Incomplete answers to this part will get zero, not a partial grade.
3. You must program in C, Java or C++. With prior permission, other languages may be acceptable – talk to the TA and obtain his permission.
4. Write a main program that carries out the following tasks, one after the other:
5. First, the main program should read from a file named “phw_input.txt” containing 10 comma-delimited integers in the first line, create an array containing these 10 integers, and run each of the algorithms on that
input array, and print out the answer produced by each on the console as follows: “algorithm-1: <answer>; algorithm-2:<answer>; algorithm-3:<answer>; algorithm-4:<answer> where <answer> is the MSCS as
determined by each of the algorithms.
6. Next, create 19 integer sequences of length 10,15,20,25,……90,95,100, containing randomly generated negative, zero and positive integers, and store these in 19 arrays of size 10,15,…,95,100: A -A .
7. Then use the system clock to measure time t1, run one of the four algorithms on array A (starting with i=1) N times (where N is at least 100, but if your system clock does not have a good resolution you may need N
to be larger, like 500 or 1000 in order to get meaningful running times), then measure time t2, and compute average time needed by that algorithm to solve the problem with input size = size of A . Do this for each of
the algorithms executing on each of the 19 input arrays to fill the first four columns of a 19X8 matrix of integers with average execution times. Each row of this matrix corresponds to one input size, from 10-100.
8. Fill the last four columns of this matrix with values ceiling(T (n)), ceiling(T (n)), ceiling(T (n)), and ceiling(T (n)) where n = each input size and T(n) are the polynomials representing the theoretically calculated complexity of the three algorithms that you determined in step 2 part (b). So, column 1 will have measured running times of your first algorithm and column 5 will have the calculated complexity for the same algorithm; similarly for columns 2 & 6, 3 & 7, and 4 & 8. You may need to scale the complexity values (or use an appropriate time unit such as nano/micro/milli seconds for the measured running times) in order to bring all data into similar ranges.
9. Your main program should write one text line of algorithm and complexity order titles separated by commas (e.g., “algorithm-1,algorithm-2,algorithm-3,algorithm-4,T (n),T (n),T (n), T (n)”), followed by the above matrix also in comma-delimited format (19 lines of 8 integers separated by commas) to a file called “yourname_phw_output.txt”.
10. Open yourname_phw_output.txt with a spreadsheet and produce a labeled graph with 10-100 on the x- axis and 8 curves showing the actual time taken and predicted time (the complexity order) for each
algorithm. Label the curves appropriately.