GSC101 MIDTERM SOLVED PAPERS. FREE DOWNLOAD ALL VU SUBJECT PAST PAPERS FROM HERE. gsc101 past papers, gsc101 quiz, gsc101 past papers by moaaz, gsc101 past quizzes for mid term 2022, Algorithm performance While modern machines can perform millions or millions of commands per second, efficiency is still a major problem in algorithm construction.

Often the choice between efficiency and non-efficiency algorithms can make the difference between an effective solution to a problem and an ineffective one. Let us consider the problem of a university registrar dealing with the task of restoring and rehabilitating student records. Although the university has about 10,000 students enrolled at one time Semester, its “current students” file contains records of more than 30,000 students.

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current in the sense that they enrolled in at least one study a few years ago but have not yet enrolled
graduated. In the meantime, let us assume that these records are stored on the registrar’s computer as a list
ordered with student ID numbers. To find any student record, the registrar will search this is a list of a specific ID numbers. GSC101 MIDTERM SOLVED PAPERS

We have introduced two search algorithms for such a list: sequential search and binary search. Our question now is whether choosing between these two algorithms makes any difference in the matter of registrar. We consider consecutive searches first. Once a student ID number is assigned, the sequential search algorithm starts at the beginning of the list again compares the entries found with the ID number you want. GSC101 MIDTERM SOLVED PAPERS

Not knowing anything about the source of the target number, we cannot conclude how far this search should go in the list. We can say that after that many searches we expect the depth of the search in the middle of the list; others will be short, but some will be longer. GSC101 MIDTERM SOLVED PAPERS

Therefore, we estimate that over time, consecutive searches will be possible to search for about 15,000 records per search. When you return and check each record for identification number requires 10 milliseconds (10 thousand per second), such a search would require an average of 150 seconds or 2.5 minutes — the unbearable length of time for a registrar to wait for a student record to appear on the computer screen. GSC101 MIDTERM SOLVED PAPERS

Even if the time required to retrieve and inspect each record has been reduced to a minimum of Only 1 millisecond, the search will still require an average of 15 seconds, which is longer linda. In contrast, the binary search continues by comparing the target value with the entries in the list. If this is not the entry you want, so at least the rest of the search is limited to only part of the actual list. GSC101 MIDTERM SOLVED PAPERS

So, after an internal search of a record of 30,000 student records, a binary search of at least 15,000. records to be considered. After the second investigation, at least 7,500 were left, and after the third return, the list mentioned dropped to more than 3,750. Continuing in this way, we see that it is a target the record will be available after retrieving at least 15 entries from a list of 30,000 records.

So, if each of this retrieval can be done in 10 milliseconds, the process of searching for a particular record is only required 0.15 per second — which means access to any student record will appear to be the instantaneous view of the registrar. We conclude by choosing between a sequential search algorithm and a binary search algorithm can have a huge impact on this application. GSC101 MIDTERM SOLVED PAPERS

This example illustrates the importance of the area of ​​computer science known as algorithm analysis includes resource research, such as time or location, required by algorithms. Great the use of that research to assess the relative relevance of other algorithms. Algorithm analysis often involves the worst, worst situations, and the worst situations.GSC101 MIDTERM SOLVED PAPERS

In our example, we did an intermediate analysis of consecutive search algorithms and an analysis of the worst case scenario algorithm to estimate the time required to search the list of 30,000 entries. Usually, such analysis is done in a general context. That is, when considering search engine algorithms, we are not focusing on a specific length list, but instead trying to identify a formula that can show algorithm performance at a random length list. It is not difficult to reconcile our preconceived ideas in the list of unreasonable lengths.