Table of Contents
- 1 What is the difference between an approximation algorithm and heuristic algorithm?
- 2 What is heuristics in computer science and how is it used in algorithms?
- 3 What is the need of approximation algorithms?
- 4 What are approximation algorithms What are the situations in which approximation algorithms are useful?
- 5 What is a heuristic in research?
- 6 What is the theory behind approximation algorithms for polynomial time?
What is the difference between an approximation algorithm and heuristic algorithm?
While approximation algorithms provide a worst-case performance guarantee in both computational time and solution quality, research on heuristics typically focuses on the average empirical behaviour of the algorithms.
What is meant by approximation algorithms?
In computer science and operations research, approximation algorithms are efficient algorithms that find approximate solutions to optimization problems (in particular NP-hard problems) with provable guarantees on the distance of the returned solution to the optimal one.
What is heuristics in computer science and how is it used in algorithms?
Heuristics in computer science and artificial intelligence are “rules of thumb” used in algorithms to assist in finding approximate solutions to complex problems. Heuristics aim to produce solutions in a reasonable time frame that are good enough for solving the problem at hand.
What is the opposite of heuristics?
a commonsense rule (or set of rules) intended to increase the probability of solving some problem. Antonyms: algorithmic, recursive.
What is the need of approximation algorithms?
Approximation algorithms are typically used when finding an optimal solution is intractable, but can also be used in some situations where a near-optimal solution can be found quickly and an exact solution is not needed. Many problems that are NP-hard are also non-approximable assuming P≠NP.
Why do we need approximation algorithms How do we characterize approximation algorithms?
An approximation algorithm is a way of dealing with NP-completeness for an optimization problem. The goal of the approximation algorithm is to come close as much as possible to the optimal solution in polynomial time.
What are approximation algorithms What are the situations in which approximation algorithms are useful?
What is the difference between a heuristic and an approximation algorithm?
A heuristic is typically a bunch of intuitive steps that may or may not lead you an optimal solution. An approximation algorithm, on the other hand, is equipped with a formal promise of being reasonably close to an optimal solution. A canonical example that illustrates the difference is the following.
What is a heuristic in research?
” a heuristic is a technique designed for solving a problem more quickly when classic methods are too slow. The objective of a heuristic is to produce quickly enough a solution that is good enough for solving the problem at hand.
What is a k-approximation algorithm?
An algorithm with approximation ratio k is called a k-approximation algorithm; both algorithms above would be called 2-approximation algorithms. When the approximation ratio is close to 1, it is often more useful to look at the approximation error, which is defined as the approximation ratio minus 1.
What is the theory behind approximation algorithms for polynomial time?
As for your last question, there is no separate theory for approximation algorithms for problems that are solvable in polynomial time. In fact, it might be that P = N P. Some examples of approximation algorithms for problems in P include algorithms for numerical linear algebra and computational geometry.