In general an algorithm that has time complexity o n

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In general, an algorithm that has time complexity O (n 2 ) will run as a program in time O (n 2 ), no matter what language or machine is chosen for the implementation of the algorithm. To analyze an algorithm, we make some simplifying assumption: (1) First assumption: each statement , except perhaps for procedure and function calls, takes the same unit amount of time . This will not affect the timing analysis since we are only interested in big-O estimates and so are ignoring constant multiples. As an example, if multiplication takes five times as long as addition when implemented on a computer, we are off in our timing estimate by no more than a factor of five if we treat them as equal in the algorithm, and the multiple of five will be absorbed in our big-O estimate anyway. The instructions not obeying to this constraint are: IF instruction. Repetitive instruction sequences (loops). Functions and procedures calls. Ignoring for the moment the function and procedure calls, there are two more simplifying assumptions we will make in the timing analysis of an algorithm: (2) Unless there is compelling reason to the contrary, we will always assume that an if statement takes time equal to the larger of its two branches (in order to obtain worst-case timing estimates). (3) Unless there is compelling reason to the contrary, we will always assume that the statements within a loop will be executed as many times as the maximum permitted by the loop control.
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------------------------------------------------------------ Example 2.4.a. Consider the following routine: (1) Searches an array with n elements for any element with value equal to key. (2) Returns the last location where key is found in variable where, or zero if not found. [2.4.a]. ------------------------------------------------------------ PROCEDURE LinearSearch (n,key: integer, a: NumberArray; VAR where: integer); {Searches an array with n elements for any element with value equal to key. Returns the last location where key is found in variable where, or zero if not found.} VAR index: integer; BEGIN where: = 0 FOR index:= 1 TO n DO IF a[index]= key THEN [2.4.a] where:= index END ; { LinearSearch } ------------------------------------------------------------ Entering procedure and initializing O (1) variable where FOR loop ( n iterations) O (1+n+1) = O (n) IF statement and O (1) O (n*1) = O (n) its branch Return from procedure O (1) Fig. 2.4.a. Temporal scheme of the algorithm [2.4.a] The figure 2.4.a shows the temporal scheme of the algorithm and the corresponding O-big estimation. We have included timing for operations, such as entering and leaving the procedure , that are not explicitly written as part of the routine. In the future we will omit these from our timing analysis, since they contribute only a constant amount of additional time, and so will not appear in the big-O estimate. Notice that we have made use of the rules for evaluating big-O expressions.
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Example 2.4.b .
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