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What is rls algorithm?
The Recursive least squares RLS adaptive filter is an algorithm which recursively finds the filter coefficients that minimize a weighted linear least squares cost function relating to the input signals. This is in contrast to other algorithms such as the least mean squares LMS that aim to reduce the mean square error. In the derivation of the RLS, the input signals are considered deterministic, while for the LMS and similar algorithm they are considered stochastic. Compared to most of its competitors, the RLS exhibits extremely fast convergence. However, this benefit comes at the cost of high computational complexity.
What are the factors that influence the performance of algorithm?
The performance of an algorithm depends primarily on two concepts: concurrency and number of operations per input unit. Concurrency occurs when an algorithm can split its data into parts and work on each part in parallel, such as a binary sort algorithm that uses "threads" to break its work into equal loads. JPEG algorithms can operate on 8x8 blocks of pixels, and are well-suited to concurrent algorithms, while solving a long algebraic equation may not be suitable for concurrent operation if the results of each step determine the following step. The second factor of performance is known as the Big-O notation. This is summarized as an algorithm's response to the size of the input, where O(1) is fixed time (and fastest), and O(n) is a linear increase to input size, and larger values, such as O(n^2) representing exponential growth of processing time dependent on input size, or O(n!) representing factorial growth (very poor scaling). Other models that represent scaling also exist, but Big-O appears to be one of the most common models for conveying the cost of an algorithm.
Which of the fastest sorting algorithm?
There is no single algorithm that is ideally suited to every type of sort. If all the data will fit into working memory, then you have a choice of algorithms depending on the size of the set, whether the sort should remain stable or not and how much auxiliary memory you wish to utilise. But if data will not fit into working memory all at once, your choice of algorithm is more limited. Stability relates to elements with equal status. When the sort is stable, equal elements remain in the same order they were originally input while an unstable sort cannot guarantee this. Stable sorts are ideally suited to data that may be sorted by different primary keys, such that the previous sort order is automatically maintained. That is, if data may be sorted by name or by date, sorting by name and then by date keeps the names in the same order (by date). With an unstable sort, even if you keep track of secondary keys there is no guarantee the secondary or tertiary keys will maintain order. For small sets of data that will easily fit into memory, an insertion sort offers the best performance with minimal auxiliary storage. This is a stable sort that can be done in place. For larger sets, a quicksort offers the best performance but is unstable. However, stable versions exist at the cost of performance. Since the algorithm divides the set into smaller and smaller unsorted sets (where each set is in the correct order with respect to the other sets), switching to insertion sort to sort the smaller sets improves overall performance. For disk-based sorting, merge sort is generally the most efficient. It utilises multiple disks and is stable.
How does RIP perform load balancing?
by defining a number of parallel paths to a single destination by sending data across a number of equal cost paths by using the hop count as the metric for path selection
What is the difference between dijkstra and kruskal's algorithm?
Both of these functions solve the single source shortest path problem. The primary difference in the function of the two algorithms is that Dijkstra's algorithm cannont handle negative edge weights. Bellman-Ford's algorithm can handle some edges with negative weight. It must be remembered, however, that if there is a negative cycle there is no shortest path.
What is the optimal way to assign a set of tasks to a set of resources in a linear assignment problem?
In a linear assignment problem, the optimal way to assign tasks to resources is to use a method called the Hungarian algorithm. This algorithm helps find the best assignment by considering the costs or benefits associated with each task-resource combination. By minimizing the total cost or maximizing the total benefit, the Hungarian algorithm can determine the most efficient assignment of tasks to resources.
What is an example of a minimum cost flow problem and how can it be solved efficiently?
An example of a minimum cost flow problem is determining the most cost-effective way to transport goods from multiple sources to multiple destinations while minimizing transportation costs. This problem can be efficiently solved using algorithms such as the Ford-Fulkerson algorithm or the network simplex algorithm, which find the optimal flow through the network with the lowest total cost.
What is the grid techonolgy?
Grid technology is a means of using parallel and distributed computing models in order to achieve high performance, flexability, cost effectiveness and efficiency from an IT system. A good collection of resources are available at Gridipedia
Why is floatation cost included in computing the cost of capital for new stock issues?
yes
How would you identify the optimal cost of capital for an organization?
To identify the optimal cost of capital for an organization the cost of debt and equity is needed. The preferred stock is also needed.
What are the Disadvantages of parallel computing?
Some disadvantages of parallel computing include increased complexity in programming and debugging due to the need for coordinating multiple processes, potential for race conditions and deadlocks which can impact performance and reliability, and the cost of implementing and maintaining parallel computing systems which can be higher compared to traditional serial systems. Additionally, not all algorithms are easily parallelizable, limiting the potential benefits of parallel computing in certain applications.
What is price of cloud computing companies stock when you want one?
We can not exactly say the cost that is supposed to be spent on cloud computing as varies on various factors. but according to a recent survey the cost can be thousand of dollars.
How much security is there in cloud computing?
Private cloud computing systems from IBM and VMware can cost a million dollars. ... computing standards and compliance, SaaS and cloud computing security ... IBM's cloud offering has evolved, helping customers advance their business ..
What criteria determine the admissibility of a heuristic in problem-solving algorithms?
The admissibility of a heuristic in problem-solving algorithms is determined by its ability to provide a lower bound estimate of the cost to reach the goal state without overestimating. A heuristic is considered admissible if it never overestimates the cost to reach the goal, ensuring that the algorithm will find the optimal solution.
What is optimal duration in a project?
lowest cost to complete it
What are the top 10 companies for cloud computing?
Microsoft would be the top company that is currently implementing cloud computing. The primary advantage of cloud computing is its significantly low cost maintenance of data processing.
How soft computing differ from hard computing?
1) Hard computing, i.e., conventional computing, requires a precisely stated analytical model and often a lot of computation time. Soft computingdiffers from conventional (hard) computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.2) Hard computing based on binary logic, crisp systems, numerical analysis and crisp software but soft computingbased on fuzzy logic, neural nets and probabilistic reasoning.3) Hard computing has the characteristics of precision and categoricity and the soft computing, approximation and dispositionality. Although in hard computing, imprecision and uncertainty are undesirable properties, in soft computing the tolerance for imprecision and uncertainty is exploited to achieve tractability, lower cost, high Machine Intelligence Quotient (MIQ) and economy of communication4) Hard computing requires programs to be written; soft computing can evolve its own programs5) Hard computing uses two-valued logic; soft computing can use multivalued or fuzzy logic6) Hard computing is deterministic; soft computingincorporates stochasticity7) Hard computing requires exact input data; soft computing can deal with ambiguous and noisy data8) Hard computing is strictly sequential; soft computing allows parallel computations9) Hard computing produces precise answers; soft computing can yield approximate answers
