Dynamic programming
Greedy method
Divide and conquer
Backtracking
C. Divide and conquer
Application level
Abstract level
Implementation level
All of the above
The item is somewhere in the middle of the array
The item is not in the array at all
The item is the last element in the array
The item is the last element in the array or is not there at all
underflow
overflow
housefull
saturated
Breath first search cannot be used to find converted components of a graph.
Optimal binary search tree construction can be performed efficiently using dynamic programming.
Given the prefix and post fix walks over a binary tree.The binary tree cannot be uniquely constructe
Depth first search can be used to find connected components of a graph.
Arrays
Records
Pointers
Stacks
Processor and memory
Complexity and capacity
Time and space
Data and space
mn
max(m,n)
min(m,n)
m+n-1
grounded header list
circular header list
linked list with header and trailer nodes
none of above
Stacks linked list
Queue linked list
Both of them
Neither of them
AVL tree
Red-black tree
Lemma tree
None of the above
Lists
Strings
Graph
Stacks
Divide and conquer strategy
Backtracking approach
Heuristic search
Greedy approach
LOC(Array[5]=Base(Array)+w(5-lower bound), where w is the number of words per memory cell for the array
LOC(Array[5])=Base(Array[5])+(5-lower bound), where w is the number of words per memory cell for the array
LOC(Array[5])=Base(Array[4])+(5-Upper bound), where w is the number of words per memory cell for the array
None of above
Stacks
Dequeues
Queues
Binary search tree
Array
Stack
Tree
queue
Sorting
Merging
Inserting
Traversal
elementary items
atoms
scalars
all of above
FIFO lists
LIFO list
Piles
Push-down lists
3,4,5,2,1
3,4,5,1,2
5,4,3,1,2
1,5,2,3,4
When Item is somewhere in the middle of the array
When Item is not in the array at all
When Item is the last element in the array
When Item is the last element in the array or is not there at all
True, False
False, True
True, True
False, False
FAEKCDBHG
FAEKCDHGB
EAFKHDCBG
FEAKDCHBG
List
Stacks
Trees
Strings
O(n)
O(log n)
O(n2)
O(n log n)
Abstract level
Implementation level
Application level
All of the above
Counting the maximum memory needed by the algorithm
Counting the minimum memory needed by the algorithm
Counting the average memory needed by the algorithm
Counting the maximum disk space needed by the algorithm
Trees
Graphs
Arrays
None of above
Graph
Binary tree
Trees
Stack
Stack
Queue
List
Link list
The list must be sorted
there should be the direct access to the middle element in any sublist
There must be mechanism to delete and/or insert elements in list
none of above