Multiplications in a sentence

An advanced Kenyan curriculum meant that while my classmates were grappling with their 2 x table, I had already mastered multiplications up to 12 by heart.

It’s an extension of what we see now in graphical processing units (GPUs) where large scale graphics are created using concurrent calculations called matrix multiplications.

A faster solution is to calculate both powers simultaneously: :((a × b) 2 × a) 2 × a × b which needs only 6 multiplications in total.

All multiplications in G op are thus "turned around".

Although using more and more parts can reduce the time spent on recursive multiplications further, the overhead from additions and digit management also grows.

Because of its matrix operations, APL was for some time quite popular for computer graphics programming, where graphic transformations could be encoded as matrix multiplications.

By applying several matrix multiplications in succession, any vector can be expressed in any basis so long as the set of direction cosines is known relating the successive bases.

Computer algorithms main The classical method of multiplying two n-digit numbers requires n 2 simple multiplications.

Efficiency Evaluation using the monomial form of a degree-n polynomial requires at most n additions and (n 2 + n)/2 multiplications, if powers are calculated by repeated multiplication and each monomial is evaluated individually.

Examples For the following expressions the count of multiplications is shown for calculating each power separately, calculating them simultaneously without transformation and calculating them simultaneously after transformation.

Fast multiplication algorithms for large inputs Gauss's complex multiplication algorithm Complex multiplication normally involves four multiplications and two additions.

For example, when computing x 2 k −1 the binary method requires k−1 multiplications and k−1 squarings.

In 1971, L. B. Smith published similar algorithms for all conic sections and proved them to have good properties. citation These algorithms need only a few multiplications and additions to calculate each vector.

It is possible to avoid the multiplications in a software implementation by using two accumulators.

Its essence is the calculation of the simple multiplications separately, with all addition being left to the final gathering-up stage.

Many simple multiplications by small constants (besides powers of two, for which shifts can be used) can be done much faster using dedicated short subroutines.

More generally, if one allows any previously computed exponents to be summed (by multiplying those powers of x), one can sometimes perform the exponentiation using fewer multiplications (but typically using more memory).

More precisely, the number of multiplications is one less than the number of ones present in the binary expansion of n. For n greater than about 4 this is computationally more efficient than naively multiplying the base with itself repeatedly.

Namely, an attacker observing the sequence of squarings and multiplications can (partially) recover the exponent involved in the computation.

Regardless, the number of multiplications never grows more slowly than (log n), so these algorithms only improve asymptotically upon exponentiation by squaring by a constant factor at best.