Sequential Search Algorithms

Sequential Search Algorithms

In the computation field, sequential search algorithms are employed to locate the specific value of interest. To execute the process, the organization of the data should permit the processors to implement the algorithms appropriately. Information is always arrangedin sets, some shorts while other long enough, to consume the better part of the available space. In sequential search algorithms, data should be organized in a list, which would be easier to find a particular element of interest. The process randomly checks all the values that are sequentially stored in the machine. This means that the algorithm works in a coordinated way. As it was alluded in the prior sentence, the input in the processors varies in size and the content that is carried in each node. The search process happens in a group format while retaining the quality of the final output.

According to the information released by Chan &Tian (2015), he noted that sequential search makes at most n searches, where n denotes the length of the sequence. The longer the path, the more time it takes for the results to be displayed. If every element has equally to be searched, the average searches are equivalent to n/2. This search is rarely practical because other methods are more efficient and fast. Some of them are binary algorithms search and hash tables, which are attributedto their quick execution. However, the named alternatives are only applicable where the sequence is short. Based on the research conducted by Astrachan et al. (2015), he noted that combination of all the available methods could offer solutions in specific instances. Linear search (sequential search) is practically simple to use in the cases where the data is unordered. It’s used to sort the more massive array into specific subsets that are easy to be processed using other methods such as binary. Although the efficiency of the hash table and binary are not comparable to linear, regarding speed, the sequential search is still one of the best in rearranging the information into the required formats.

Depth-First Search Algorithms

It applies to the search spaces that are trees. The process begins by expanding the node, into its successors. According to this strategy, the initial packets that store the primary information are split into several small sections thatcarry unique data. If there is no success of possible successors, backtracking happens, where the original node is evaluated for the alternatives (Chakraborty & Satti, 2017). Often, the newly generated nodes are arranged dependingon the likelihood of reaching the final solution. The arrangement is made automatically, with the machine utilizing some form of coded information. The search algorithm can predict the nodes that carry pertinent information and aligns them in the same order. Similarly, those that have little probability of providing the correct answer are placed last in the list. Some of the broad first search algorithms are simple backtracking, which performs the search until it finds the appropriate and feasible answer to an individual problem. Moreover, it is not guaranteed to find the path that is cost-effective(Chen & Tian,2015). This is because it works in searching for solutions randomly, without considering other peripheral costs that are associated with the transition of data from one node to another. Also, the method uses the heuristic information to arrange the outputs of the expanded nodes. The second model of DFS algorithm is depth-first branch-and-bound, which works by updating the current solutions. Astrachan et al. (2015) comments that this kind of method relies on the previous results that are generated in past solved problems. In cases where the machine has saved the results of similar problems, the algorithm scrolls through and gives the resembling output. The process is always faster than comparing the data and arranging them for processing. Furthermore, just like backtracking, the shortest and appropriate route through which data can arrive at the final destination is not established.

At each level of the DFS algorithm, the unsolved alternatives must be stored for backtracking. If m is the amount of space required to store an element, and d is the depth, then, the maximum storage required is denoted as O (md). The functioning of the depth-first search algorithms can be represented by the charts shown below.

From the figures above, (a) represent the DFS tree, with the dashed lines representing the successors that have been explored. On graph (b) the boxes represent the untested state that contains original forms of the data.  The next chart (c) shows the untested stacks along with their parents. The shaded boxes represent the original states while the blocks on the right side illustrate the successors.

Best-First Search (BFS) Algorithms

It uses a heuristic to guide the search process. The core source of data is open source that comprises of multiple nodes. The combination of all types of nodes increases the task of the algorithm to select the most probable element that would generate an appropriate answer. The best node is chosen and expanded to give successors, which are used to solve a particular problem. One of the challenges that are experienced in this form is sophistication and data redundancy (Chen &Tian, 2015). As it is well known, colossal information consumes time to sort out. This is because it lacks a definite path through which it can be evaluated, which results to use of randomization as the only alternative. Due to this reason, a best first search algorithm is time-consuming and does not save on cost. However, once the best state is selected, the processing takes place efficiently.BFS algorithms of graphs have to be slightly modified to cater for the different paths that a single node can take. Thisis performed by invoking computational equations that apply to the problems under study. Again, closed list stores the information of the nodes that were processed previously. It enables the searching process to take a short route, incase coinciding information is found while expanding the nodes. In that case, the available data is utilized to provide a workable solution.

Unfolding a Graph into a Tree

Data can be structured into different formats, depending on the architecture of the processors. The design that is adopted is critical in deciding on the right algorithm to invoke. In search algorithms, one aspect which is considered essential is the accessibility of the data in the storage spaces (Astrachan et al., 2015). The more complicated it is, the more it would take to receive the desired output. Nodes are arranged in graphical formats that have to be unfolded to reduce the communication latency. For it to happen, one node has to support the subsequent successors. What is observed is one node being expanded, into several states, and the chain continues downwards to form a tree-like structure. The expansion is done to locate the most appropriate node that would increase the output rate (Chakraborty&Satti, 2017). Unfolding comes along with some overhead costs such as increased time of retrieving the most appropriate is not applicable in situations where rapid processing is needed. Conclusively, sequential search algorithms are highly required in areas where the arrangement of large data is necessary. The method allows classifications to be done, based on finite characteristics which determine the path that the problems would take, before generating correct answers.

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