What are two main measures for the efficiency of an algorithm?
Two main measures for the efficiency of an algorithm are: Processor and Memory. Complexity and Capacity.
How do you determine if one algorithm is better than another?
The standard way of comparing different algorithms is by comparing their complexity using Big O notation. In practice you would of course also benchmark the algorithms. As an example the sorting algorithms bubble sort and heap sort has complexity O(n2) and O(n log n) respective.
Are there problems that Cannot be solved algorithmically?
Explanation: problems cannot be solved by any algorithm are called undecidable problems. problems that can be solved in polynomial time are called tractable problems.
How do you verify an algorithm?
Formal reasoning. The only way to prove the correctness of an algorithm over all possible inputs is by reasoning formally or mathematically about it. One form of reasoning is a “proof by induction”, a technique that’s also used by mathematicians to prove properties of numerical sequences.
When determining the efficiency of algorithm is measured by?
time factor
Explanation: To determine the efficiency of an algorithm the time factor is measured by counting number of key operations.
How is algorithm 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.
How do you compare two algorithms?
Comparing algorithms
- Approach 1: Implement and Test. Alce and Bob could program their algorithms and try them out on some sample inputs.
- Approach 2: Graph and Extrapolate.
- Approach 2: Create a formula.
- Approach 3: Approximate.
- Ignore the Constants.
- Practice with Big-O.
- Going from Pseudocode.
- Going from Java.
How can you compare the performance of different algorithms for solving the problem?
Which one should be chosen to be coded as a program to solve the problem? In order to decide which algorithm to chose over another, they are compared in terms of their efficiency: the time it takes to find the solution and the resources which are consumed in the process.
Can anything be solved algorithmically?
There are some problems that a computer can never solve, even the world’s most powerful computer with infinite time: the undecidable problems. An undecidable problem is one that should give a “yes” or “no” answer, but yet no algorithm exists that can answer correctly on all inputs.
Is NP equal to P?
The “P versus NP problem” asks whether these two classes are actually identical; that is, whether every NP problem is also a P problem. Practical experience overwhelmingly suggests that P does not equal NP. But until someone provides a sound mathematical proof, the validity of the assumption remains open to question.
How do you know if an algorithm is reasonable?
If the number of steps is less than or equal to a polynomial function of the size of the input (including constant, linear, and quadratic functions, etc.), then the algorithm takes polynomial time and is reasonable.