JAVA: Which of the following shows a list of Big-Oh running times in order from slowest to fastest?
O(1), O(N), O(N2), O(logN), O(2N) |
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O(1), O(N), O(N3), O(2N), O(N!) |
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O(logN), O(N!), O(N2), O(N3), O(2N) |
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O(N!), O(2N), O(N2), O(N), O(logN) |
Order of grows from shortest to largest running time is constant - 1 logarithmic - log(N) linear - N quadratic - N^2 cubic - N^3 exponential - e^N factorial - n!
list of Big-Oh running times in order from slowest to fastest is O(1), O(N), O(N3), O(2N), O(N!)
Option 2
JAVA: Which of the following shows a list of Big-Oh running times in order from slowest...
Arrange the following Big-O notations from the least expensive to the most expensive (or slowest to fastest), in terms of time complexity. O(N!), O(logN), O(N3 ), O(1), O(NlogN), O(N2 ), O(2n ), O(√n), O(n√n), O(N2 logN)
Once you have determined big-O bounds for each expression, order
the expressions from the slowest growing (best algorithm, takes the
least amount of time to execute) to the fastest growing.
1. n3 (log2n +5) +log2n 20 2. 532n2 10n log2n - 20n 25 3. nlog2n 20 10log2n + + 35n 4. 10n2 1000 2n 33n log2n
a) Prove that running time T(n)=n3+30n+1 is O(n3) [1 mark] b) Prove that running time T(n)=(n+30)(n+5) is O(n2) [1 mark] c) Count the number of primitive operation of algorithm unique1 on page 174 of textbook, give a big-Oh of this algorithm and prove it. [2 mark] d) Order the following function by asymptotic growth rate [2 mark] a. 4nlogn+2n b. 210 c. 3n+100logn d. n2+10n e. n3 f. nlogn
What is the order of the following growth function expressed using Big-Oh notation: T(N)=7*N3 + N/2 + 2 * log N + 38 ? O(2N) O(N3) O(N/2) O(N3 + log N)
1 question) Arrange the following in the order of their growth rates, from least to greatest: (5 pts) n3 n2 nn lg n n! n lg n 2n n 2 question)Show that 3n3 + n2 is big-Oh of n3. You can use either the definition of big-Oh (formal) or the limit approach. Show your work! (5 pts.) 3 question)Show that 6n2 + 20n is big-Oh of n3, but not big-Omega of n3. You can use either the definition of big-Omega...
4. Big-Oh and Rune time Analysis: describe the worst case running time of the following pseudocode functions in Big-Oh notation in terms of the variable n. howing your work is not required (although showing work may allow some partial t in the case your answer is wrong-don't spend a lot of time showing your work.). You MUST choose your answer from the following (not given in any particular order), each of which could be re-used (could be the answer for...
Give the time complexities (Big-O notation) of the following running times expressed as a function of the input size N. a) N12+ 25N10+ 8 b) N + 3logN + 12n√n c) 12NlogN + 15N2 logN
Order the following functions by growth rate: N, squrerootN, N1.5, N2, NlogN, N log logN, Nlog2N, Nlog(N2), 2/N,2N, 2N/2, 37, N2 logN, N3. Indicate which functions grow at the same rate.
Rank the following functions from slowest growing to fastest growing (i.e. fastest to slowest) 1 (constant) log2n (logarithmic) n (linear) n * log2 n (“n log n”) n2 (quadratic)
Rank the following nucleophiles in order from slowest (1) to fastest (4). Explain your answer SH SO OH 00