Online Algorithm with A Competitive Ratio : The Studies
This time, we will examine Online Algorithm with A Competitive Ratio research from various subtopics.
''Online Algorithms for Market Clearing: A Comparative Study''
A journal about online algorithms for market clearing has been conducted. It has been found that a (randomized) online algorithm with a competitive ratio of ln(p max ? p min) + 1 is the best possible. When bids are in a range [p min, p max], this algorithm can be used even if expiration times are not known.
New On-Line Scheduling Algorithms for Metrical Task Systems
An article about optimal on-line scheduling algorithms for metrical task systems has been performed. It is shown that the optimal algorithm is w ( S, d) = 2|S|1 for every task system in which d is symmetric, and w ( S, d) = O (| S | 2) for every task system. Finally, randomized on-line scheduling are introduced.
On-Line Algorithms: Quality Enhancement through Optimization
An article about the quality of on-line algorithms is presented. The study finds that a more accommodating function, which is a generalization of both the competitive ratio and the on accommodating sequences, helps to improve the quality of on-line procedures.
The Algorithmic Solution to the Scheduling Problem
A study about the scheduling problem, where we present an algorithm that is more consistent and efficient than the ones in use.
A Lower Bound on the Competitive Ratio of Global-control Algorithms
A study about lower bounds on the competitive ratio of global-control algorithms has revealed that there exist a lower bound of ?(max{k, (1/D) ·(log n/log log n)) at infinity.
Offline and Online Interactive Software: A Comparison
An inquiry about the relative worst-order ratio showed that the algorithms used by online interactive software are not always as quality as those used traditionally. This study was conducted to compare and contrast the two types of algorithms, those used in traditional ladles and those used in online interactive software.
The Worst Order Ratio of Online algorithms vs. Offline algorithms
An article about the relative worst order ratio found that the algorithms were more efficient when compared to those used offline. Botanical identification was successfully carried out with the use of online scheduling algorithms.
On-demand greedy algorithm for online multidimensional knapsack problem
A study about online multidimensional knapsack problem has shown that different algorithms can be used to solve it. The algorithms studied were a greedy algorithm and a global Insertion and Removal algorithm. The greedy algorithm was found to be the most effective in solving the online multidimensional knapsack problem.
Closing the Gap in Parallel Algorithm Performance
A paper about the best possible online algorithms for parallel-machine scheduling to minimize the maximum weighted completion time was conducted. In this study, two algorithms were created and evaluated to find the best-possible one. The objective of these studies was to provide a potential lower bound on the maximum weighted completion time, and also to identify an algorithm with a better performance than any other existing one.
The Best Scheduling Algorithms for Unplanned Jobs
An evaluation about online scheduling algorithms found that the best algorithms vary depending on the relative severity of workload differences. Unexpectedjob or no job available can lead to a decreased makespan for a scheduling algorithm.
