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For the sieve technique we solve the problem,
Recursively (Handsouts Page 35 Line 4)
mathematically
precisely
accurately
We do sorting to,
keep elements in random positions
keep the algorithm run in linear order
keep the algorithm run in (log n) order
keep elements in increasing or decreasing order (Handsouts Page 39 Line 6)
The reason for introducing Sieve Technique algorithm is that it illustrates a very
important special case of,
divide-and-conquer (handsouts page 34 )
decrease and conquer
greedy nature
2-dimension Maxima
4. In Sieve Technique we don’t know which item is of interest
True (handsouts page 34)
Fals
5. In the analysis of Selection algorithm, we make a number of passes, in fact it could
be as many as,
T(n)
T(n / 2)
log n (handsouts page 37)
n / 2 + n / 4
6. Divide-and-conquer as breaking the problem into a small number of pivot
Sieve
smaller sub problems (handouts page 27)
Selection
7. A heap is a left-complete binary tree that conforms to the
increasing order only
decreasing order only
heap order (handouts page 40)
(log n) order
8. Slow sorting algorithms run in, T(n^2)
T(n)
T(log n)
T(n log n) (handsouts page 40)
9. One of the clever aspects of heaps is that they can be stored in arrays
without using any .
pointers (handsouts page 40)
constants
variables
functions
10. Sorting is one of the few problems where provable bonds exits on how fast
we can sort,
upper
lower (handsouts page 39)
average log n
2nd
11. For the sieve technique we solve the problem,
mathematically
precisely
accurately
recursively (handsouts page 35)
12. Sieve Technique can be applied to selection problem?
true (handsouts page 35)
false
13. How much time merge sort takes for an array of numbers? (n^2)
T(n)
T(log n)
T(n log n) (handouts page 30)
14. For the Sieve Technique we take time
T(nk) (handouts page 34)
T(n / 3) n^2
n/3
15. Heaps can be stored in arrays without using any pointers; this is due to the
nature of the binary tree,
left-complete Repeat
right-complete tree nodes
tree leaves
16. How many elements do we eliminate in each time for the Analysis of Selection
algorithm?
n / 2 elements
(n / 2) + n elements
n / 4 elements 2 n elements
17. We do sorting to,
keep elements in random positions
keep the algorithm run in linear order
keep the algorithm run in (log n) order
keep elements in increasing or decreasing order ( page 39 Repeat)
18. In which order we can sort?
increasing order only
decreasing order only
increasing order or decreasing order Repeat
both at the same time
19. A heap is a left-complete binary tree that conforms to the
increasing order only
decreasing order only
heap order (page 40)
(log n) order
20. In the analysis of Selection algorithm, we make a number of passes, in fact it could
be as many as,
T(n)
T(n / 2)
log n (page 37)
n / 2 + n / 4
21. A heap is a left-complete binary tree that conforms to the
increasing order only
decreasing order only
heap order (page 40)
(log n) order
22. How much time merge sort takes for an array of numbers? T(n^2)
T(n)
T(log n)
T(n log n) (page 30)
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