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  2. Multiplication algorithm - Wikipedia

    en.wikipedia.org/wiki/Multiplication_algorithm

    In arbitrary-precision arithmetic, it is common to use long multiplication with the base set to 2 w, where w is the number of bits in a word, for multiplying relatively small numbers. To multiply two numbers with n digits using this method, one needs about n 2 operations.

  3. Schönhage–Strassen algorithm - Wikipedia

    en.wikipedia.org/wiki/Schönhage–Strassen...

    The run-time bit complexity to multiply two n-digit numbers using the algorithm is (⁡ ⁡ ⁡) in big O notation. The Schönhage–Strassen algorithm was the asymptotically fastest multiplication method known from 1971 until 2007.

  4. Arbitrary-precision arithmetic - Wikipedia

    en.wikipedia.org/wiki/Arbitrary-precision_arithmetic

    For multiplication, the most straightforward algorithms used for multiplying numbers by hand (as taught in primary school) require (N 2) operations, but multiplication algorithms that achieve O(N log(N) log(log(N))) complexity have been devised, such as the Schönhage–Strassen algorithm, based on fast Fourier transforms, and there are also ...

  5. Computational complexity of mathematical operations - Wikipedia

    en.wikipedia.org/wiki/Computational_complexity...

    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.

  6. Strassen algorithm - Wikipedia

    en.wikipedia.org/wiki/Strassen_algorithm

    The number of additions and multiplications required in the Strassen algorithm can be calculated as follows: let () be the number of operations for a matrix. Then by recursive application of the Strassen algorithm, we see that f ( n ) = 7 f ( n − 1 ) + l 4 n {\displaystyle f(n)=7f(n-1)+l4^{n}} , for some constant l {\displaystyle l} that ...

  7. Hadamard product (matrices) - Wikipedia

    en.wikipedia.org/wiki/Hadamard_product_(matrices)

    With the SymPy symbolic library, multiplication of array objects as either a*b or a@b will produce the matrix product. The Hadamard product can be obtained with the method call a.multiply_elementwise(b). [22] Some Python packages include support for Hadamard powers using methods like np.power(a, b), or the Pandas method a.pow(b).

  8. Booth's multiplication algorithm - Wikipedia

    en.wikipedia.org/wiki/Booth's_multiplication...

    Let m and r be the multiplicand and multiplier, respectively; and let x and y represent the number of bits in m and r. Determine the values of A and S, and the initial value of P. All of these numbers should have a length equal to (x + y + 1). A: Fill the most significant (leftmost) bits with the value of m. Fill the remaining (y + 1) bits with ...

  9. Karatsuba algorithm - Wikipedia

    en.wikipedia.org/wiki/Karatsuba_algorithm

    Karatsuba multiplication of az+b and cz+d (boxed), and 1234 and 567 with z=100. Magenta arrows denote multiplication, amber denotes addition, silver denotes subtraction and cyan denotes left shift. (A), (B) and (C) show recursion with z=10 to obtain intermediate values. The Karatsuba algorithm is a fast multiplication algorithm.