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Prove by mathematical induction that the complexity of binary search algorithm is log n?
Wiki User
∙ 14y ago
The complexity of the binary search algorithm is log(n)...
If you have n items to search, you iteratively pick the middle item and compare it to the search term. Based on that comparision, you then halve the search space and try again. The number of times that you can halve the search space is the same as log2n. This is why we say that binary search is complexity log(n).
We drop the base 2, on the assumption that all methods will have a similar base, and we are really just comparing on the same basis, i.e. apples against apples, so to speak.
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∙ 14y ago
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Which search algorithm requires that the arrays contents be sorted?
Binary Search Algorithm
Complexity of an algorithm in data structure?
* search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1) * search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1)
How do you calculate space complexity?
Calculate the amount of additional memory used by the algorithm relative to the number of its inputs. Typically the number of inputs is defined by a container object or data sequence of some type, such as an array. If the amount of memory consumed remains the same regardless of the number of inputs, then the space complexity is constant, denoted O(1) in Big-Omega notation (Big-O). If the amount of memory consumed increases linearly as n increases, then the space complexity is O(n). For example, the algorithm that sums a data sequence has O(1) space complexity because the number of inputs does not affect the amount of additional memory consumed by the accumulator. However, the algorithm which copies a data sequence of n elements has a space complexity of O(n) because the algorithm must allocate n elements to store the copy. Other commonly used complexities include O(n*n) to denote quadratic complexity and O(log n) to denote (binary) logarithmic complexity. Combinations of the two are also permitted, such as O(n log n).
Which are the searching algorithm always compare the middle element with the searching elements in the given array?
binary search system
How do you print all data in a Binary Search Tree?
By using Depth First Search or Breadth First search Tree traversal algorithm we can print data in Binary search tree.
Which search algorithm requires that the arrays contents be sorted?
Binary Search Algorithm
what is the complexity of binary search?
Deezzzz Nutzzzz
What is complexity of binary search tree?
The complexity of binary search tree : Search , Insertion and Deletion is O(h) . and the Height can be of O(n) ( if the tree is a skew tree). For Balanced Binary Trees , the Order is O(log n).
Any mathematical B words?
binary
Complexity of an algorithm in data structure?
* search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1) * search array => O(1) linked list=> O(n) binary tree=> O(log n) hash=>O(1)
Give you the algorithm of creating a new binary search tree using c?
i want to know how to give the algorithm password in a computer ?
How is a pso program in matlab..how is it created from algorithm..please help?
The PSO or Particle Swarm Optimization Program algorithm in MatLab is created by first creating a binary genetic algorithm.
How do you calculate space complexity?
Calculate the amount of additional memory used by the algorithm relative to the number of its inputs. Typically the number of inputs is defined by a container object or data sequence of some type, such as an array. If the amount of memory consumed remains the same regardless of the number of inputs, then the space complexity is constant, denoted O(1) in Big-Omega notation (Big-O). If the amount of memory consumed increases linearly as n increases, then the space complexity is O(n). For example, the algorithm that sums a data sequence has O(1) space complexity because the number of inputs does not affect the amount of additional memory consumed by the accumulator. However, the algorithm which copies a data sequence of n elements has a space complexity of O(n) because the algorithm must allocate n elements to store the copy. Other commonly used complexities include O(n*n) to denote quadratic complexity and O(log n) to denote (binary) logarithmic complexity. Combinations of the two are also permitted, such as O(n log n).
The order of binary search algorithm is?
n log n - G.Parthiban, SRM
What is the difference between best worst and average case complexity of an algorithm?
These are terms given to the various scenarios which can be encountered by an algorithm. The best case scenario for an algorithm is the arrangement of data for which this algorithm performs best. Take a binary search for example. The best case scenario for this search is that the target value is at the very center of the data you're searching. So the best case time complexity for this would be O(1). The worst case scenario, on the other hand, describes the absolute worst set of input for a given algorithm. Let's look at a quicksort, which can perform terribly if you always choose the smallest or largest element of a sublist for the pivot value. This will cause quicksort to degenerate to O(n2). Discounting the best and worst cases, we usually want to look at the average performance of an algorithm. These are the cases for which the algorithm performs "normally."
The truncated binary exponential back off algorithm?
This is the Algorithm use by CSMA/CD as a wait period to allow other devices on the network to access the media.
Why is booth algorithm fast for multiplication two binary numbers?
Shifting in easily accomplished in hardware.
