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This is a list of operators in the C and C++ programming languages.. All listed operators are in C++ and lacking indication otherwise, in C as well. Some tables include a "In C" column that indicates whether an operator is also in C. Note that C does not support operator overloading.
C mathematical operations are a group of functions in the standard library of the C programming language implementing basic mathematical functions. [1] [2] All functions use floating-point numbers in one manner or another. Different C standards provide different, albeit backwards-compatible, sets of functions.
An example of a multiplier operator acting on functions on the real line is the Hilbert transform. It can be shown that the Hilbert transform is a multiplier operator whose multiplier is given by the m ( ξ ) = − i sgn ( ξ ) {\displaystyle m(\xi )=-i\operatorname {sgn} (\xi )} , where sgn is the signum function .
In C/C++, it is possible to declare the parameter of a function or method as constant. This is a guarantee that this parameter cannot be inadvertently modified after its initialization by the caller. If the parameter is a pre-defined (built-in) type, it is called by value and cannot be modified.
k 1 = c · (a + b) k 2 = a · (d − c) k 3 = b · (c + d) Real part = k 1 − k 3 Imaginary part = k 1 + k 2. This algorithm uses only three multiplications, rather than four, and five additions or subtractions rather than two. If a multiply is more expensive than three adds or subtracts, as when calculating by hand, then there is a gain in speed.
For example, if A, B and C are matrices of respective sizes 10×30, 30×5, 5×60, computing (AB)C needs 10×30×5 + 10×5×60 = 4,500 multiplications, while computing A(BC) needs 30×5×60 + 10×30×60 = 27,000 multiplications. Algorithms have been designed for choosing the best order of products; see Matrix chain multiplication.
This leads to efficient implementations of add/subtract networks (e.g. multiplication by a constant) in hardwired digital signal processing. [1] Obviously, at most half of the digits are non-zero, which was the reason it was introduced by G.W. Reitweisner [2] for speeding up early multiplication algorithms, much like Booth encoding.
Graphs of functions commonly used in the analysis of algorithms, showing the number of operations versus input size for each function. The following tables list the computational complexity of various algorithms for common mathematical operations.