PyMathCompute

PyMathCompute is a Python tool designed to handle mathematical variables, create and evaluate mathematical expressions, and perform various mathematical optimizations. This library is ideal for those working in applied mathematics, optimization, and related fields.

Features

• Variable: Defines mathematical variables with lower and upper bounds.
• MathExpression: Creates and evaluates mathematical expressions using defined variables.
• Mathematical Operations: Supports addition, subtraction, multiplication, and exponentiation of variables and expressions.
• Special mathematical operations: Also allow us to interact with special operators such as e, sin, cos and others.

Installation

You can install PyMathCompute using pip:

pip install pymath_compute

Basic Usage

Defining Variables and Mathematical Expressions

from pymath_compute import Variable

# Create a variable
x = Variable(name="x", lower_bound=0, upper_bound=10)

# Create a mathematical expression
expr = x + 2 * x - 5

# Evaluate the expression
values = {"x": 5}
result = expr.evaluate(values)
print(f"Result of the expression: {result}")

Mathematical Operations

PyMathCompute allows various mathematical operations with variables and expressions:

from pymath_compute import Variable

# Create variables
x = Variable(name="x", lower_bound=0, upper_bound=10)
y = Variable(name="y", lower_bound=0, upper_bound=10)

# Create expressions
expr1 = x + y
expr2 = x * 2 + y ** 2

# Evaluate expressions
values = {"x": 3, "y": 4}
result1 = expr1.evaluate(values)
result2 = expr2.evaluate(values)

print(f"Result of expr1: {result1}")
print(f"Result of expr2: {result2}")

Mathematical Operators

PyMathCompute also allow connection with operators, as using sin, cos, e, and others. For this, we use the MathFunction parameter

from numpy import np
from pymath_compute import Variable, MathFunction

# Create the variables
x = Variable(name="x", lower_bound=0, upper_bound=np.pi)

# Add a function to calculate the sin of x
sin = MathFunction(np.sin, x)

Usage

Here is a basic example of how to use the OptSolver and its methods:

from pymath_compute import Variable
from pymath_compute.solvers.opt_solver import OptSolver
from pymath_compute.methods import OptMethods

# Define a simple objective function
def objective_function(variables: list[Variable]) -> float:
    return sum(var.value for var in variables)

# Create some variables
variables = [Variable(name=f"Var_{i}", value=i) for i in range(10)]

# Initialize the solver
solver = OptSolver()

# Set the variables
solver.set_variables(variables)

# Set the objective function
solver.set_objective_function(objective_function)

# Configure the solver
solver.set_solver_config({
    "solver_time": 30,
    "solver_method": OptMethods.GRADIENT_DESCENT
})

# Solve the optimization problem
solver.solve()

# Check the status and get the results
print(f"Solver status: {solver.status}")
results = solver.vars_results()
for var in results:
    print(f"{var.name}: {var.value}")

You can see more in the examples section of this package.

Future Plans

In future versions, we plan to add:

• Mathematical optimization methods like Newton's method.
• Molecular dynamics solvers.
• Computational derivatives and other advanced calculus tools.

Contributions

Contributions are welcome. If you have suggestions, bug reports, or improvements to propose, please open an issue or a pull request on our GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.