Importance of Randomized Algorithms
Last Updated :
02 Nov, 2023
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Introduction:
- Randomization is an important concept, and hence randomization algorithms are used in a variety of fields, such as number theory, computational geometry, graph theory, and distributed computing.
- The inputs for a randomized algorithm are similar to those of deterministic algorithms, along with a sequence of random bits that can be used by the algorithm for making random choices.
- In other words, a randomized algorithm is one whose behavior depends on the inputs, similar to a deterministic algorithm, and the random choices are made as part of its logic.
- As a result, the algorithm gives different outputs even for the same input.
- In other words, the algorithm exhibits randomness; hence its run-time is often explained in terms of a random variable.
Advantages:
- Randomized algorithms are known for their simplicity.
- Any deterministic algorithm can easily be converted to a randomized algorithm. These algorithms are very simple to understand and implement.
- Randomized algorithms are very efficient.
- They utilize little execution time and space compared to any deterministic algorithms.
- Randomized algorithms exhibit superior asymptotic bounds compared to deterministic algorithms.
- In other words, the algorithm complexity of randomized algorithms is better than that of most deterministic algorithms.
- Reliability is an important issue in many critical applications, as not all randomized algorithms always give correct answers.
- In addition, many randomized algorithms may not terminate.
- Hence, reliability is an important concern that needs to be dealt with.
- The quality of randomized algorithms is dependent on the quality of the random number generator used as part of the algorithm.
- Unlike other design paradigms, a randomized algorithm does not use a single design principle.
- Instead, one should view randomized algorithms as those designed using a set of principles.
- Instead, one should view randomized algorithms as those designed using a set of principles.
Some design principles are listed in the following subsections:
Concept of Witness:
- This principle involves the question of checking whether a given input possesses a property X or not.
- It is established by finding a certain object called a witness or a certificate.
- The witness is identified to prove the fact that the input indeed has the desired property X.
- By conducting fewer trials, it can be found out whether the property was really present.
- The presence of a witness is strong proof of property X based on the absence of witnesses. This principle is illustrated using the example of primality testing.
Fingerprinting:
- By definition, a fingerprint is a shorter message that is representative of a larger object.
- Fingerprinting is a technique wherein one makes a comparison of two large objects, A and B, only by comparing their respective short fingerprints.
- If two fingerprints do not match, then objects A and B are different.
- However, if the fingerprints match, then there is strong circumstantial evidence that both objects are the same.
Checking Identities:
- Let us assume that an algebraic expression is given, and the problem is to check whether the expression evaluates to zero or not.
- The principle of checking identities is to plug the random variables of a given algebraic equation and check whether the expression evaluates to zero.
- If it is not zero, then the given expression is not an identity.
- Otherwise, there is strong circumstantial evidence that the expression is identically zero.
Random Sampling and Ordering:
- The performance of an algorithm sometimes improves by randomizing the input distribution or order.
- It can be observed that for certain ordering of the input, the performance of the algorithm can be higher or just acceptable.
- Here, randomization leads to randomized ordering, partitioning, and sampling.
- In addition, randomized algorithms gather information about input distributions using random samples. This is illustrated through the hiring problem.
Foiling the Adversary:
- A randomized algorithm can be viewed as a game between a person and an adversary, that is, a person proposing an algorithm and an adversary who tries to foil the algorithm by designing suitable inputs so that the algorithm takes a longer time.
- In other words, a randomized algorithm can be viewed as a selection of an algorithm from a large set of deterministic algorithms, and this selection can be considered a scenario where things are made difficult by giving random input, thus making the task more difficult.
