Submitted by:
Mirza Saifullah Baig

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Searching Algorithms
A search algorithm is an algorithm that evaluates a number of possible solutions to a given problem and returns the best one.
Searching algorithms are used in constraint satisfaction problem, where the goal is to find a set of value assignments to certain variables that will satisfy specific mathematical equations and inequations.
They are also used when the goal is to find a variable assignment that will maximize or minimize a certain function of those variables.
Algorithms for these problems include the basic brute-force search (also called "naïve" or "uninformed" search.
The various search algorithms also include tree search algorithms, that view the elements as vertices of a tree, and traverse that tree in some special order.
Examples of the latter include the exhaustive methods such as depth-first search and  breadth-first search, as well as various heuristic-based search tree pruning methods such as backtracking and branch and bound.
Another important sub-class consists of algorithms for exploring the game tree of multiple-player games, such as chess or backgammon, whose nodes consist of all possible game situations that could result from the current situation.
The goal in these problems is to find the move that provides the best chance of a win, taking into account all possible moves of the opponent(s).
Similar problems occur when humans or machines have to make successive decisions whose outcomes are not entirely under one's control, such as in robot guidance or in marketing, financial or military strategy planning.
This kind of problems has been extensively studied in the context of artificial intelligence.
Examples of algorithms for this class are the minimax algorithm, alpha-beta pruning, and the A* algorithm.
Searching algorithms:
Sequential Search
A procedure for searching a table that consists of starting at some table position (usually the beginning) and comparing the file-record key in hand with each table-record key, one at a time, until either a match is found or all sequential positions have been searched.
Linear Search
Linear Search (aka Sequential Search) is the most fundamental and important of all algorithms. It is simple to understand and implement.
The input to linear search is a sequence (e.g. an array, a collection, a string, an iterator, etc.) plus a target item. The output is true if the target item is in the sequence, and false otherwise
Binary Search
This algorithm locates the position of an item in a sorted array.
Binary search works by comparing an input value to the middle element of the array.
The comparison determines whether the element equals the input, less than the input or greater.
When the element being compared to equals the input the search stops and typically returns the position of the element.
If the element is not equal to the input then a comparison is made to determine whether the input is less than or greater than the element.
Depending on which it is the algorithm then starts over but only searching the top or bottom subset of the array's elements.
Binary search algorithms typically halve the number of items to check with each successive iteration, thus locating the given item (or determining its absence) in logarithmic time.
A binary search is a dichotomic and conquer search algorithm
APPLICATIONS
Enterprise search is much more difficult problem, and one of the most challenging problems in IT.
Consumer search can be vague and still be successful. It can bring up a list of nearly relevant sites or documents, and usually that is all that is needed. But in the enterprise, search is usually needed to find something very specific, a contract, a purchase order, a memo.
And there are all sorts of conditions associated with access to data, some security based, others are regulatory. Search quickly becomes quite a complex process and one that can lead to other things
Enterprises use a lot of structured data, but there is also a massive amount of unstructured data too. Search in the enterprise could potentially bring the two data world's together.
You can also create enterprise applications by using modified search algorithms.
This type of scenario gets very interesting: enterprise applications by algorithm. This is already happening in business intelligence, I wonder how far, in theory, such an approach could be taken