ALGORITHM ENGINEERING HOMEWORK
Let g(n) = n. Is it true that g(n) 2 O(n2)? Why or why not?
f(n) = O(g(n)) means there are positive constants c and n0, such that 0 ≤ f(n) ≤ cg(n) for all n ≥ n0 g(n) = O(n^2) n = O(n^2) => n <= c*n^2 Let's assume c = 1 => n <= c*n^2 => n <= n^2 => 0 <= n so, g(n) = O(n^2) given c = 1 and n0 = 0
ALGORITHM ENGINEERING HOMEWORK Let g(n) = n. Is it true that g(n) 2 O(n2)? Why or...
is these true or false ?and explain why a)if f(n)=O(g(n)) then 2^(f(n)=O(2^(g(n)))... please solve without lim b)if f(n)=o(g(n)) then 2^(f(n)=o(2^(g(n)))... please solve without lim
1) True or false? a. n2 = O(n3) b. 2n2 + 1 = O(n2) c. n1/2 = O(log n) d. log n = O(n1/2 ) e. log n + n1/2 = O(n2) f. log n = O( n-1/2) g. log n = O( 1/n ) h. log (n + 3) = q(n1/2) i. n + n1/2= W( n2 - n )
In C++ How do you demo that selection sort has O(N2) complexity? Meaning of the O(N2). If you have N=1000 input values the selection sort need roughly 1000000 steps. What is the meaning of thee ‘step’ here? One ALU comparison, one swapping of values, or one calculation step on one value of the array. What is the total number of steps for selection sort? Let me use an example N=5, to help me think 4+1-__ 3+1-__ 2+1-__ 1+1-__ F(N)=__________________________ This...
5. Let G be a graph with order n and size m. Suppose that n 2 3 and n-n2)+2 m > Using Ore's Theorem, prove that G is Hamiltonian
5. Let G be a graph with order n and size m. Suppose that n 2 3 and n-n2)+2 m > Using Ore's Theorem, prove that G is Hamiltonian
Let f(n) = 5n^2. Prove that f(n) = O(n^3). Let f(n) = 7n^2. Prove that f(n) = Ω(n). Let f(n) = 3n. Prove that f(n) =ꙍ (√n). Let f(n) = 3n+2. Prove that f(n) = Θ (n). Let k > 0 and c > 0 be any positive constants. Prove that (n + k)c = O(nc). Prove that lg(n!) = O(n lg n). Let g(n) = log10(n). Prove that g(n) = Θ(lg n). (hint: ???? ? = ???? ?)???? ?...
2. (10 pts.) Show that either g(n) Ofn)) or frn) -O(g(n) for the following. g(n)n2 + 7 n, f(n)-ns_2㎡ a. b. g(n)= 2n + 4,f(n)=61g(n*)
O(log(log(N))) < O(log(N)) a. True b. False O(N ) < O(log(N)) a. True b. False O( N5) < O(N2 - 3N + 2) a. True b. False O(2N) < O(N2) a. True b. False
Question2 0/5 pts If exact running time of an algorithm is T(n)-5n3+ n2 + 3n -5 where n is the input size, then which of the following is true? T(n)- O(n) RCOECEQuestion 3 0/5 pts Which of the following is the correct ranking of the functions listed below: logn. n2 n2n, 2. 1500. nlogn, 5 Question 4 5/5 pts to search
Let f(n) = n^2 +200 Let g(n) = 200 n Select the first answer below that is true. f is Theta (g) f is O (g) f is Ohm (g)
O: Naive algorithm coloring graphs: dild Engineering use the foll?2, is its complexity , vn) be colored with k colors? We pseudo-code. What (2 Pts) naïve algorithm in pseu Naive Algorithm: 1. Try all possible ways of assigning k colors to the n verticesTry to color V1, V2, Vn With the colors 1,2,,k. 2. If a valid coloring is found then answer is yes. Otherwise, answer is no. Complexity: O(2)