Despite its concrete-sounding name,
a floating point is something that
technically doesn’t exist. People
can’t prove its existence, yet it is
used millions of times a day in
computer operations. How and why
this happens is fascinating to many
people.
number. In technical terms, it is a
digital representation of a number,
an approximation of an actual
number. It doesn’t exist on number
lines or on the pages of mathematics
textbooks, however. Floating points
form the basis of computer
calculations.
Usually, these numbers are a
combination of integers and their
various multipliers. In computer
terms, the number two is usually the
base in such an operation. Using
such a base and various exponents,
the computer will perform operations
by the millions. The vast majority of
these operations are powered by
floating point numbers.
The idea behind floating point
numbers is to generate enough
random numbers to power the often
complex data interactions that make
up a computer’s most basic and
more complicated functions.
Showing the date and time, for
example, could take a few or
perhaps a large handful of
calculations, depending on a number
of variables. Displaying options and
results for graphic-intensive
software programs, however, might
require calculations numbering in
the millions.
A sometimes interesting byproduct
of these calculations is that numbers
that would be equal on a number line
or in numerical equations can co-
exist. For example, both 0.01 x 10(1)
and 1.00 x 10(-1) are equal to 0.1 if
we write them as parts of an
equation, but floating point
calculations allow both simply
because they are written differently.
Equations, which tend to want to
simplify things as much as possible,
are not floating point calculations,
and vice versa.
One issue surrounding such
calculations that is quite unpopular
with makers of financial software,
the users of which require exact
calculations down into the smaller
sides of the decimal, is that the
numbers are not at all definite. It’s
all well and good to tell the time and
date using this type of calculation,
but determining a multinational
company’s net worth for a given
fiscal year needs a much more
definite numerical accounting than
the inherent random result that a
floating point calculation will
provide. The very words suggest that
the numbers are not at all stable,
and that kind of insecurity makes
financial experts uncomfortable.
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Also like my facebook page @ TechnoTeen
a floating point is something that
technically doesn’t exist. People
can’t prove its existence, yet it is
used millions of times a day in
computer operations. How and why
this happens is fascinating to many
people.
A floating point
is, at its heart, a
digital representation of a number,
an approximation of an actual
number. It doesn’t exist on number
lines or on the pages of mathematics
textbooks, however. Floating points
form the basis of computer
calculations.
Usually, these numbers are a
combination of integers and their
various multipliers. In computer
terms, the number two is usually the
base in such an operation. Using
such a base and various exponents,
the computer will perform operations
by the millions. The vast majority of
these operations are powered by
floating point numbers.
The idea behind floating point
numbers is to generate enough
random numbers to power the often
complex data interactions that make
up a computer’s most basic and
more complicated functions.
Showing the date and time, for
example, could take a few or
perhaps a large handful of
calculations, depending on a number
of variables. Displaying options and
results for graphic-intensive
software programs, however, might
require calculations numbering in
the millions.
A sometimes interesting byproduct
of these calculations is that numbers
that would be equal on a number line
or in numerical equations can co-
exist. For example, both 0.01 x 10(1)
and 1.00 x 10(-1) are equal to 0.1 if
we write them as parts of an
equation, but floating point
calculations allow both simply
because they are written differently.
Equations, which tend to want to
simplify things as much as possible,
are not floating point calculations,
and vice versa.
One issue surrounding such
calculations that is quite unpopular
with makers of financial software,
the users of which require exact
calculations down into the smaller
sides of the decimal, is that the
numbers are not at all definite. It’s
all well and good to tell the time and
date using this type of calculation,
but determining a multinational
company’s net worth for a given
fiscal year needs a much more
definite numerical accounting than
the inherent random result that a
floating point calculation will
provide. The very words suggest that
the numbers are not at all stable,
and that kind of insecurity makes
financial experts uncomfortable.
Thank you for visiting TechnoTeen
Also like my facebook page @ TechnoTeen
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