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What is the time complexity of an algorithm that has a running time of nlogn?
The time complexity of an algorithm with a running time of nlogn is O(nlogn).
Is nlogn faster than n in terms of computational efficiency?
Yes, in terms of computational efficiency, nlogn is faster than n.
What is the relationship between the nlogn graph and the efficiency of algorithms in terms of time complexity?
The nlogn graph represents algorithms with a time complexity of O(n log n). This time complexity indicates that the algorithm's efficiency grows at a moderate rate as the input size increases. Algorithms with a nlogn time complexity are considered efficient for many practical purposes, striking a balance between speed and scalability.
What is the time complexity of algorithm to solve fractional knapsack problem using greedy paradigm?
if the objects in the knapsack are already being sorted then it requires only O(n) times to arrange the objects...so total time require by the knapsack problem is T(n)=(nlogn) because sorting the objects require O(nlogn) time...Remaining is to run for n objects O(n). Hence, bounded by O(nlogn)
How does the time complexity of a recursive algorithm change when the input size is halved and the algorithm makes two recursive calls with a cost of 2t(n/2) each, along with an additional cost of nlogn at each level of recursion?
When the input size is halved and a recursive algorithm makes two calls with a cost of 2t(n/2) each, along with an additional cost of nlogn at each level of recursion, the time complexity increases by a factor of nlogn.
What is the time complexity of an algorithm that has a running time of nlogn?
The time complexity of an algorithm with a running time of nlogn is O(nlogn).
Is nlogn faster than n in terms of computational efficiency?
Yes, in terms of computational efficiency, nlogn is faster than n.
What is the bound on complementing a Buchi automaton?
The tight bound is 2O(nlogn).
What is the time complexity of infix to post fix conversion algorithm?
O(nlogn)
What is the relationship between the nlogn graph and the efficiency of algorithms in terms of time complexity?
The nlogn graph represents algorithms with a time complexity of O(n log n). This time complexity indicates that the algorithm's efficiency grows at a moderate rate as the input size increases. Algorithms with a nlogn time complexity are considered efficient for many practical purposes, striking a balance between speed and scalability.
What is the time complexity of algorithm to solve fractional knapsack problem using greedy paradigm?
if the objects in the knapsack are already being sorted then it requires only O(n) times to arrange the objects...so total time require by the knapsack problem is T(n)=(nlogn) because sorting the objects require O(nlogn) time...Remaining is to run for n objects O(n). Hence, bounded by O(nlogn)
How does the time complexity of a recursive algorithm change when the input size is halved and the algorithm makes two recursive calls with a cost of 2t(n/2) each, along with an additional cost of nlogn at each level of recursion?
When the input size is halved and a recursive algorithm makes two calls with a cost of 2t(n/2) each, along with an additional cost of nlogn at each level of recursion, the time complexity increases by a factor of nlogn.
If quick sort and merge sort have same elements then what will be the complexity of both algorithms?
quicksort should be O(n^2), but merge sort should be O(nlogn). but if you can modify partition algorithm with checking all values same in array from p to r, it could be O(nlogn).
Is it derivative of or derivative from?
"Derivative of"
What is the second derivative of a function's indefinite integral?
well, the second derivative is the derivative of the first derivative. so, the 2nd derivative of a function's indefinite integral is the derivative of the derivative of the function's indefinite integral. the derivative of a function's indefinite integral is the function, so the 2nd derivative of a function's indefinite integral is the derivative of the function.
How is motion and position related?
Velocity is the derivative of position.Velocity is the derivative of position.Velocity is the derivative of position.Velocity is the derivative of position.
Prove that a constant vector always has a perpendicular derivative?
A dot A = A2 do a derivative of both sides derivative (A) dot A + A dot derivative(A) =0 2(derivative (A) dot A)=0 (derivative (A) dot A)=0 A * derivative (A) * cos (theta) =0 => theta =90 A and derivative (A) are perpendicular
