Online algorithms are algorithms that make decisions based on a sequence of inputs, without knowing the entire sequence in advance. In other words, they receive input data piece-by-piece and are required to produce results or make decisions while receiving the inputs. This differs from offline algorithms, which assume that all input data is available before the algorithm begins to operate.
An example of an online algorithm is the Least Recently Used (LRU) Cache algorithm. This algorithm is used in computer memory management systems to determine which items to remove when the cache limit is reached. The algorithm keeps track of the most recently used items and removes the least recently used items when the cache limit is reached. The LRU Cache algorithm works by first storing items in a linked list in the order they are accessed. When the cache limit is reached, the least recently used item is removed by deleting the first element of the linked list. The LRU Cache algorithm operates online because it makes decisions based on incoming data (memory use) as it comes in, without requiring knowledge of the entire sequence of memory use beforehand.
Online algorithms are designed to process incoming data in real-time.
They make decisions based only on the information available at the time each decision is made.
They operate in a variety of applications such as packet routing, job scheduling and stock trading.
They have to balance the need for making quick decisions with the need for making accurate decisions.
They typically have lower memory requirements and better scalability than their offline counterparts.
They often use heuristics and approximation algorithms to make decisions in time constraints.
They require careful analysis and modelling to ensure their performance meets the requirements of the application.
They are subject to algorithmic complexities such as competitive analysis and regret minimization.
They are a rapidly evolving area of research, with many open problems and challenges yet to be solved.
What is the basic difference between online algorithms and offline algorithms?
Answer: Online algorithms process input data as soon as they arrive, whereas offline algorithms process the entire input data before initiating program execution.
Can an online algorithm be deterministic?
Answer: Yes, an online algorithm can be deterministic if it always takes the same action for a given set of input data.
What are the advantages of using an online algorithm?
Answer: The advantages of using an online algorithm include improved responsiveness, reduced storage requirements, and adaptability to changing input data.
Why are greedy algorithms popularly used in online algorithms?
Answer: Greedy algorithms are popularly used in online algorithms because they are simple, efficient, and offer provable bounds on performance.
What is the competitive analysis of an online algorithm?
Answer: Competitive analysis is a way to compare the performance of an online algorithm against an offline algorithm. It measures the online algorithm’s performance as a percentage of the performance of the offline algorithm, and the ratio between them is known as the competitive ratio.