This chapter presents the following content: Running time of function, running time of cubic function, running time of quadratic function, running time of linear function, running time of logarithmic function, logarithm, static searching problem,. | Lecture 18 Recap Running Time of Function Running Time of Cubic Function Running Time of Quadratic Function Running Time of Linear Function Running Time of Logarithmic Function Logarithm Bits in Binary Numbers Repeated Doubling Repeated Halving Static Searching Problem Sequential Search Binary Search Interpolation Search Checking an Algorithm Analysis Once we have performed an algorithm analysis, we want to determine whether it is correct and as good we can possibly make it One way to do so is to code the program and see if the empirically observed running time matches the running time predicted by the analysis Continued . When N increases by a factor of 10, the running time goes up by a factor of 10 for linear programs, 100 for quadratic programs, and 1000 for cubic programs Programs that run in O(N log N) take slightly more than 10 times as long to run under the same circumstances These increases can be hard to spot if the lower order terms have relatively large coefficients and N . | Lecture 18 Recap Running Time of Function Running Time of Cubic Function Running Time of Quadratic Function Running Time of Linear Function Running Time of Logarithmic Function Logarithm Bits in Binary Numbers Repeated Doubling Repeated Halving Static Searching Problem Sequential Search Binary Search Interpolation Search Checking an Algorithm Analysis Once we have performed an algorithm analysis, we want to determine whether it is correct and as good we can possibly make it One way to do so is to code the program and see if the empirically observed running time matches the running time predicted by the analysis Continued . When N increases by a factor of 10, the running time goes up by a factor of 10 for linear programs, 100 for quadratic programs, and 1000 for cubic programs Programs that run in O(N log N) take slightly more than 10 times as long to run under the same circumstances These increases can be hard to spot if the lower order terms have relatively large coefficients and N is not large enough An example is the jump from N = 10 to N = 100 in the running time for the various implementations of the maximum contiguous subsequence sum problem Differentiating linear programs from O(N log N) programs, based purely on empirical evidence, also can be very difficult Continued . Another commonly used trick to verify that some program is O(F(N)) is to compute the values T(N)/F(N) for a range of N, where T(N) is the empirically observed running time If F(N) is a tight answer for the running time, the computed values converge to a positive constant If F(N) is an overestimate, the values converge to zero If F(N) is an underestimate, and hence wrong, the values diverge Example Suppose that we write a program to perform N random searches, using the binary search algorithm Because each search is logarithmic, we expect the total running time of the program to be O(N log N) Figure in next slide shows the actual observed running time for the routine for various input sizes on
