SEO for Desklib - Title, Meta Title , Meta Description, Slug, Summary

This SEO optimization guide provides suggestions for Desklib - an online library for study material. It includes title, meta title, meta description, slug, summary for better SEO. The subject, course code, course name, and college/university are not mentioned.

Solution to Question A
Hash Function: h(i) = (3 * i + 5) % 11
=> 94, h(94) = (3*94 + 5)%11 = 287%11 = 1
=> 11, h(11) = (3*11 + 5)%11 = 38%11 = 5
=> 39, h(39) = (3*39 + 5)%11 = 1 , So Linear Probing will take it to 2 as 1 is filled
=> 20, h(20) = (3*20 + 5)%11 = 65%11 = 10
=> 16, h(16) = (3*16 + 5)%11 = 53%11 = 9
=> 5, h(5) = (3*5 + 5)%11 = 20%11 = 9, 9 and 10 is full, So it will go to 0
=> 12, h(12) = (3*12 + 5)%11 = 41%11 = 8
=> 44, h(44) = (3*44 + 5)%11 = 5 As 5 is filled it will go to 6
=>13, h(13) = (3*13 + 5)%11 = 44%11 = 0 As 1,2 is also filled it will go to 3
=> 88, h(88) = (3*88 + 5)%11 = 5 As 5,6 is filled it will go to 7
=> 23, h(23) = (3*23 + 5)%11 = 74%11 = 8 As 8,9,10,0,1,2,3 is filled it will go to 4
0:==>5
1:==>94
2:==>39
3:==> 13
4:==>23
5:==>11
6:==>44
7:==>88
8:==>12
9:==>16
10:==>20
Solution to Question B
Hash Function: h'(k) = 7 - (k % 7)
0:==>13
1:==>94

2:==>5
3:==> 44
4:==>39
5:==>11
6:==>23
7:==>88
8:==>12
9:==>16
10:==>20
Solution to Question C
1. Correct answer is b O(n log n)
2. A red black tree, upon one insertion may require O(log n) in changing the colour of the node. Find()
operation will take O(log n) time to get the node just like binary search tree but balanced. So n find will
take O(n log n) time and 15 max() will take O(15*log n) time. So overall complexity is O(n log n)
3. Correct answer is c O(n2)
4. Each insertion will take O(log n) time in binary heap but complexity of creating new binary heap is
O(n). Find operation performs bad on binary heap since left and right child are not related. So n find will
take O(n2). Max() will take O(1) time which is the root node only. So 15 Max will take O(1) time. So total
complexity is O(n2).
5. Correct answer is a O(n)
6. A good hash function will take O(1) time to insert. So n insert will take O(n) time. Find() operation also
take O(1) time for each entry, So n Find() will take O(n) time. Each Max() will require need to scan entire
hash table to find maximum element so take O(n) time. So 15 find will take O(n) time.
7. Overall complexity is O(n) for hash table assuming good hash function will less collision. So hash table
seems to be good choice.