What makes an algorithm slow?

What makes an algorithm slow?

An algorithm is not its implementation. Even relatively simple and efficient algorithms can be badly implemented, which may result in slower performance. Unnecessary function calls are one common case. Use of recursion rather than loops can impact speed.

Which is the faster algorithm?

The time complexity of Quicksort is O(n log n) in the best case, O(n log n) in the average case, and O(n^2) in the worst case. But because it has the best performance in the average case for most inputs, Quicksort is generally considered the “fastest” sorting algorithm.

How much faster would the algorithm run on a machine that is ten times faster than the one we have *?

The first two equations are both linear; only the value of the constant factor has changed. In both cases, the machine that is ten times faster gives an increase in problem size by a factor of ten.

How do you make an algorithm faster?

Most Common Ways To Speed up an algorithm

1. Replace a nested loop by first building a hash and then looping.
2. Remove unnecessary accumulations.
3. Cache intermediate or previous results.
4. Zip merge.

How is an algorithm time efficiency measured?

One way to measure the efficiency of an algorithm is to count how many operations it needs in order to find the answer across different input sizes. The algorithm looks through each item in the list, checking each one to see if it equals the target value. If it finds the value, it immediately returns the index.

Which is true about the relationship between algorithms and problems?

Which is true about the relationship between algorithms and problems? A problem is a mapping from inputs to outputs, and there might be many algorithms that can accomplish this mapping. A problem is a recipe, and an algorithm is a solution to a problem.

Which algorithm is better algorithm a or algorithm B?

Algorithm B is (n^2). You expect algorithm A to work better. However when you run a specific example of the same machine, Algorithm B runs quicker. Give the reasons to explain how such a thing happen?

What is the O(n) time of the algorithm?

Algorithm A, for example, can run in time 10000000*n which is O (n). If algorithm B, is running in n*n which is O (n^2), A will be slower for every n < 10000000. O (n), O (n^2) are asymptotic runtimes that describe the behavior when n->infinity Suppose you have the two following functions:

What is the difference between algorithm complexity and running time?

For simplicity, sometime instead of “algorithms complexity” or just “complexity” we use the term “running time”. Algorithm complexity is a rough approximation of the number of steps, which will be executed depending on the size of the input data. Complexity gives the order of steps count, not their exact count.

Is there a way to speed up a slow algorithm?

For many “slow” algorithms there is some input that it runs remarkably quickly. For instance, insertion sort is linear if the input is already sorted! So take insertion sort ( O (n)) and merge sort ( O (nlog (n))) and notice that the insertion sort will run faster if you pass in a sorted array! Boom, proof done.