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https://github.com/gnu-octave/pkg-apa

Octave/Matlab arbitrary precision arithmetic
https://github.com/gnu-octave/pkg-apa

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Octave/Matlab arbitrary precision arithmetic

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# APA - Arbitrary Precision Arithmetic via MPFR interface for Octave/Matlab.

## Installation

From the Octave command-line run:
```octave
pkg install apa
pkg load apa
pkg test apa
```

From the Matlab* command-line run:
```octave
urlwrite ('https://github.com/gnu-octave/pkg-apa/archive/refs/heads/main.zip', 'pkg-apa-main.zip');
unzip ('pkg-apa-main.zip');
cd (fullfile ('pkg-apa-main', 'inst'))
install_apa
test_apa
```

> The installation in Matlab requires a proper MEX-compiler setup, please see for details
> [`doc/MEX_INTERFACE.md`](https://github.com/gnu-octave/apa/blob/main/doc/MEX_INTERFACE.md).

## High-level MPFR Interface

The high-level MPFR interface is given through the `@mpfr_t` class.
A variable of that type "behaves" like a "normal" built-in Octave/Matlab
data type.

```octave
op1 = mpfr_t (eye (3) * 4);

rop = op1 + 1
```

rop =

5 1 1
1 5 1
1 1 5

However, you can adjust the binary precision.

The default Octave/Matlab data type **(double)** has a precision of 53 binary digits.
Thus the following calculation exceeds the given precision:

```octave
format long
too_small = (2 ^ (-60))
A = ones (3);
A(3,3) = A(3,3) + too_small
```

too_small = 8.673617379884035e-19
A =

1 1 1
1 1 1
1 1 1

```octave
B = A - ones (3)
```

B =

0 0 0
0 0 0
0 0 0

The same calculation using APA and quadruple precision (113 binary digits):

```octave
A = mpfr_t (ones (3), 113);
A(3,3) = A(3,3) + too_small
```

A =

1 1 1
1 1 1
1 1 1.00000000000000000086736173798840355

```octave
apa ('format.fmt', 'scientific')
apa ('format.base', 2)
B = A - ones (3)
```

B =

0 * 2^(0) 0 * 2^(0) 0 * 2^(0)
0 * 2^(0) 0 * 2^(0) 0 * 2^(0)
0 * 2^(0) 0 * 2^(0) 1 * 2^(-60)

The high-level MPFR interface is the preferred choice for quick numerical
experiments.

However, if performance is more critical, please use the low-level MPFR
interface (explained below) and vectorization wherever possible.

> Please note that an interface from an interpreted high-level programming
> language like Octave/Matlab is most likely slower than a pre-compiled C
> program.
>
> If performance is highly-critical, use this tool for initial experiments
> and translate the developed algorithm to native MPFR C-code.

## Low-level MPFR Interface

> For information how to compile/develop the interface, see
> [`doc/MEX_INTERFACE.md`](https://github.com/gnu-octave/apa/blob/main/doc/MEX_INTERFACE.md).

The low-level MPFR interface permits efficient access to almost all functions
specified by MPFR 4.1.0 .

All supported functions are [listed in the `inst` folder](inst)
and can be called from Octave/Matlab like in the C programming language.

For example, the C function:

```c
int mpfr_add (mpfr_t rop, mpfr_t op1, mpfr_t op2, mpfr_rnd_t rnd)
```

can be called from Octave/Matlab with scalar, vector, or matrix quantities:

```octave
% Reset to default APA output.
clear apa

% Prepare input and output variables.
rop = mpfr_t (zeros (3));
op1 = mpfr_t (eye (3) * 4);
op2 = mpfr_t (2);
rnd = mpfr_get_default_rounding_mode ();

% Call mpfr_add. Note unlike Octave/Matlab the
% left-hand side does NOT contain the result.
ret = mpfr_add (rop, op1, op2, rnd);

rop % Note rop vs. ret!
```

rop =

6 2 2
2 6 2
2 2 6

In the low-level interface the type checks are stricter,
but scalar and matrix quantities can still be mixed.

Another benefit of using the low-level MPFR interface is that **in-place**
operations are permitted, which do not create new (temporary) variables:

```octave
ret = mpfr_add (op1, op1, op1, rnd); % op1 += op1
```

## Presentations

- [NVR workshop (Nov) 2021](https://github.com/siko1056/slides_nvr2021/blob/main/slides.pdf)