table of contents
| mth(3) | AFNIX Module | mth(3) |
NAME¶
mth - standard math module
STANDARD MATH MODULE¶
The Standard Mathematical module is an original implementation of various mathematical facilities. The module can be divided into several catgeories which include convenient functions, linear algebra and real analysis.
Random number
The math module provides various functions that generate random numbers in
different formats.
| Function | Description |
| get-random-integer | return a random integer number |
| get-random-real | return a random real number between 0.0 and 1.0 |
| get-random-relatif | return a random relatif number |
| get-random-prime | return a random probable prime relatif number |
The numbers are generated with the help of the system random generator. Such generator is machine dependant and results can vary from one machine to another.
Primality testing
The math module provides various predicates that test a number for a primality
condition. Most of these predicates are intricate and are normally not used
except the prime-probable-p predicate.
| Predicate | Description |
| fermat-p | Fermat test predicate |
| miller-rabin-p | Miller-Rabin test predicate |
| prime-probable-p | general purpose prime probable test |
| get-random-prime | return a random probable prime relatif number |
The fermat-p and miller-rabin-p predicates return true if the primality condition is verified. These predicate operate with a base number. The prime number to test is the second argument.
Fermat primality testing
The fermat-p predicate is a simple primality test based on the "little
Fermat theorem". A base number greater than 1 and less than the number
to test must be given to run the test.
afnix:mth:fermat-p 2 7
In the preceeding example, the number 7 is tested, and the fermat-p predicate returns true. If a number is prime, it is guaranted to pass the test. The oppositte is not true. For example, 561 is a composite number, but the Fermat test will succeed with the base 2. Numbers that successfully pass the Fermat test but which are composite are called Carmichael numbers. For those numbers, a better test needs to be employed, such like the Miller-Rabin test.
Miller-Rabin primality testing
The miller-rabin-p predicate is a complex primality test that is more
efficient in detecting prime number at the cost of a longer computation. A
base number greater than 1 and less than the number to test must be given to
run the test.
afnix:mth:miller-rabin-p 2 561
In the preceeding example, the number 561, which is a Carmichael number, is tested, and the miller-rabin-p predicate returns false. The probability that a number is prime depends on the number of times the test is ran. Numerous studies have been made to determine the optimal number of passes that are needed to declare that a number is prime with a good probability. The prime-probable-p predicate takes care to run the optimal number of passes.
General primality testing
The prime-probable-p predicate is a complex primality test that incorporates
various primality tests. To make the story short, the prime candidate is
first tested with a series of small prime numbers. Then a fast Fermat test
is executed. Finally, a series of Miller-Rabin tests are executed. Unlike
the other primality tests, this predicate operates with a number only and
optionally, the number of test passes. This predicate is the recommended
test for the folks who want to test their numbers.
afnix:mth:prime-probable-p 17863
Linear algebra
The math module provides an original and extensive support for linear and non
linear algebra. This includes vector, matrix and solvers. Complex methods
for non linear operations are also integrated tightly in this module.
Real vector
The math module provides the Rvector object which implements the real vector
interface Rvi. Such interface provides numerous operators and methods for
manipulating vectors as traditionnaly found in linear algebra packages.
| Operator | Description |
| == | compare two vectors for equality |
| != | compare two vectors for difference |
| ?= | compare two vectors upto a precision |
| += | add a scalar or vector to the vector |
| -= | substract a scalar or vector to the vector |
| *= | multiply a scalar or vector to the vector |
| /= | divide a vector by a scalar |
| Method | Description |
| set | set a vector component by index |
| get | get a vector component by index |
| clear | clear a vector |
| reset | reset a vector |
| get-size | get the vector dimension |
| dot | compute the dot product with another vector |
| norm | compute the vector norm |
Creating a vector
A vector is always created by size. A null size is perfectly valid. When a
vector is created, it can be filled by setting the components by index.
# create a simple vector const rv (afnix:mth:Rvector 3) # set the components by index rv:set 0 0.0 rv:set 1 3.0 rv:set 2 4.0
Real matrix
The math module provides the Rmatrix object which implements the real matrix
interface Rmi. This interface is designed to operate with the vector
interface and can handle sparse or full matrix.
| Operator | Description |
| == | compare two matrices for equality |
| != | compare two matrices for difference |
| ?= | compare two matrices upto a precision |
| Method | Description |
| set | set a matrix component by index |
| get | get a matrix component by index |
| clear | clear a vector |
| get-row-size | get the matrix row dimension |
| get-col-size | get the matrix column dimension |
| norm | compute the matrix norm |
STANDARD MATH REFERENCE¶
Rvi
The Rvi class an abstract class that models the behavior of a real based
vector. The class defines the vector length as well as the accessor and
mutator methods.
Predicate
Inheritance
Operators
The == operator returns true if the calling object is equal to the vector argument.
The == operator returns true if the calling object is not equal to the vector argument.
The ?= operator returns true if the calling object is equal to the vector argument upto a certain precision.
The += operator returns the calling vector by adding the argument object. In the first form, the real argument is added to all vector components. In the second form, the vector components are added one by one.
The -= operator returns the calling vector by substracting the argument object. In the first form, the real argument is substracted to all vector components. In the second form, the vector components are substracted one by one.
The *= operator returns the calling vector by multiplying the argument object. In the first form, the real argument is multiplied to all vector components. In the second form, the vector components are multiplied one by one.
The /= operator returns the calling vector by dividing the argument object. The vector components are divided by the real argument.
Methods
The set method sets a vector component by index.
The get method gets a vector component by index.
The clear method clears a vector. The dimension is not changed.
The reset method resets a vector. The size is set to 0.
The get-size method returns the vector dimension.
The dot method computes the dot product with the vector argument.
The norm method computes the vector norm.
The permutate method permutates the vector components with the help of a combinatoric permutation object.
The reverse method reverse (permutate) the vector components with the help of a combinatoric permutation object.
Rvector
The Rvector class is the default implementation of the real vector
interface.
Predicate
Inheritance
Constructors
The Rvector constructor creates a default null real vector.
The Rvector constructor creates a real vector those dimension is given as the calling argument.
Functions
The get-random-integer function returns a random integer number. Without argument, the integer range is machine dependent. With one integer argument, the resulting integer number is less than the specified maximum bound.
The get-random-real function returns a random real number between 0.0 and 1.0. In the first form, without argument, the random number is between 0.0 and 1.0 with 1.0 included. In the second form, the boolean flag controls whether or not the 1.0 is included in the result. If the argument is false, the 1.0 value is guaranted to be excluded from the result. If the argument is true, the 1.0 is a possible random real value. Calling this function with the argument set to true is equivalent to the first form without argument.
The get-random-relatif function returns a n bits random positive relatif number. In the first form, the argument is the number of bits. In the second form, the first argument is the number of bits and the second argument, when true produce an odd number, or an even number when false.
The get-random-prime function returns a n bits random positive relatif probable prime number. The argument is the number of bits. The prime number is generated by using the Miller-Rabin primality test. As such, the returned number is declared probable prime. The more bits needed, the longer it takes to generate such number.
The get-random-bitset function returns a n bits random bitset. The argument is the number of bits.
The fermat-p predicate returns true if the little fermat theorem is validated. The first argument is the base number and the second argument is the prime number to validate.
The miller-rabin-p predicate returns true if the Miller-Rabin test is validated. The first argument is the base number and the second argument is the prime number to validate.
The prime-probable-p predicate returns true if the argument is a probable prime. In the first form, only an integer or relatif number is required. In the second form, the number of iterations is specified as the second argument. By default, the number of iterations is specified to 56.
| 2020-12-26 | AFNIX |