give as accurate (big-ohan analysis as you can of the expected running time of each algorithm write (separateprograms to execute each algorithm timesto get good average run program ( for , , program ( for , , , , , , and program ( for , , , , , , , , , , compare your analysis with the actual running times what is the worst-case running time of each algorithm complete the table in figure with estimates for the running times that were too long to simulate interpolate the running times for these algorithms and estimate the time required to compute the maximum subsequence sum of million numbers what assumptions have you madedeterminefor the typical algorithms that you use to perform calculations by handthe running time to do the followinga add two -digit integers multiply two -digit integers divide two -digit integers an algorithm takes ms for input size how long will it take for input size if the running time is the following (assume low-order terms are negligible) linear ( log nc quadratic cubic an algorithm takes ms for input size how large problem can be solved in min if the running time is the following (assume low-order terms are negligible) linear ( log nc quadratic cubic how much time is required to compute (xn = ai using simple routine to perform exponentiationb using the routine in section consider the following algorithm (known as horner' ruleto evaluate (  = ai poly fori ni > -- poly poly [ ] show how the steps are performed by this algorithm for ( explain why this algorithm works what is the running time of this algorithm