nonop 0.0.1

Nonlinear Optimization Programming (NoNOP)

NoNOP: nonlinear optimization programming

Project description

Author: Kaike Sa Teles Rocha Alves

NoNOP (Nonlinear Optimization Programming) is a package that contains new machine learning models developed by Kaike Alves to optimize nonlinear functions. The approach employes genetic algorithms (GA) and an expanding net multilayer perceptron (MLP) model.

Author: Kaike Sa Teles Rocha Alves (PhD)
Email: [e-mail] or [e-mail]

Description:

NoNOP: A new optimization library for nonlinear functions.

NoNOP (Nonlinear Optimization Programming) is a groundbreaking Python library available on PyPI (https://pypi.org/project/nonop/) that introduces a novel optimization technique that implements GA and MLP. This library is specifically designed to tackle the complexities of nonlinear functions by offering machine learning models that prioritize accuracy.

Instructions

To install the library use the command:

pip install nonop

The library provides the following class:

optimize_function

optimize_function is a class for the optimization of nonlinear functions that applies meta-heuristic techniques

To import it, simply type the command:

from nonop.optimization import optimize_function as optim_func

Hyperparameters:

objective_func : callable - The core mathematical model you want to solve. By passing this as an argument, you decouple the 'what' (the math) from the 'how' (the optimization algorithm).

constraints_func : callable, default=None - A function defining the 'boundaries' of your problem. If None, the optimizer assumes an unconstrained search space.

variables_type : str or type, default=float - The data type of the variables being optimized. This determines if the search space is continuous (float) or discrete (int), impacting how mutations and crossovers are calculated.

variables_lower_limit : float or array-like - The minimum allowable value for the variables. It acts as a boundary to ensure the optimizer stays within a feasible or physical range.

variables_upper_limit : float or array-like - The maximum allowable value for the variables. Together with the lower limit, it defines the search space volume.

rho : float, default=0.9 - The coefficient used for moving averages (often in optimizers like RMSProp). It determines how much weight is given to recent gradients versus past history.

lr : float, default=0.01 - The learning rate for the neural network. This scales the step size during gradient descent; too high can cause instability, while too low can result in painfully slow convergence.

lr_decay_rate : float, default=0.95 - The multiplier applied to the learning rate after each epoch. A value of 0.95 means the learning rate shrinks by 5% periodically to help the model settle into a global minimum.

epochs : int, default=10 - The number of complete passes through the training dataset. More epochs allow for more learning but increase the risk of overfitting.

num_generations : int, default=50 - The number of iterations for the Genetic Algorithm. Think of this as the "timeline" of evolution—how many cycles of selection and reproduction will occur.

sol_per_pop : int, default=20 - The population size (number of candidate solutions) in each generation. A larger population increases diversity but requires more computational resources per generation.

maximize : bool, default=True - Determines the direction of optimization. If True, the algorithm seeks the highest possible fitness score; if False, it minimizes a cost function.

n_min_neurons : int, default=1 - The lower bound for the number of neurons in a hidden layer if the architecture is being searched.

n_max_neurons : int, default=128 - The upper bound for the number of neurons in a hidden layer. This prevents the model from becoming too computationally expensive.

Example of optimize_function:

from nonop.optimization import optimize_function as optim_func
model = optim_func(rules = 4, fuzzy_operator = "min")
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Example I

import torch
from nonop.optimization import optimize_function as optim_func

def original_objective(x):
    return 2 * x[0] - x[0]**2 + x[1]

def original_constraints(x):
    return (torch.clamp(x[0]**2 + x[1]**2 - 4, min=0)**2 + 
            torch.clamp(x[1] - 1.8, min=0)**2 + 
            torch.clamp(x[0], max=0)**2 + 
            torch.clamp(x[1], max=0)**2)

params = {"objective_func":original_objective, "constraints_func":original_constraints, "epochs":1000, "rho":1e9, "print_information":False, "maximize":True}
model = optim_func(**params)
model.optimize_GA()

Some comments:

• the function "original_objective(x)" express the equation of the objective function
• The function "original_constraints(x)" express the constraints of the nonlinear programming as a sum. The expression torch.clamp(x[0]**2 + x[1]**2 - 4, min=0)**2 means $x_{0}^{2} + x_{1}^{2} \leq 4$. Whenever the inequality is <=, use min=0, on the other hand, inequality >=, use max=0. The quadratic operator is suitable in mathematical optimization problems
• The default is maximize, on the other hand, set the hyperparameter maximize=False

Example II

See below a more complete example:

import torch
from nonop.optimization import optimize_function as optim_func

def original_objective(x):
    return (x[0] - 2)**2 + (x[1] - 2)**2

def original_constraints(x):
    return (torch.clamp(x[0]**2 + x[1]**2 - 4, min=0)**2 + 
torch.clamp(x[0]**2 + x[1]**2 - 1, max=0)**2)

params = {"objective_func":original_objective, 
        "constraints_func":original_constraints, 
        "variables_lower_limit":[-3,-3], 
        "variables_upper_limit":[3,3], 
        "epochs":10000, 
        "print_information":False, 
        "maximize":False,
        "num_generations":10, 
        "num_parents_mating":5,
        "sol_per_pop":10,
        "n_min_neurons":50, 
        "n_max_neurons":300, 
        "n_hidden_layers":5, 
        "max_patience":5}
model = optimize_function(**params)
model.optimize_GA()

Extra information

Code of Conduct:

NoNOP is a library developed by Kaike Alves. Please read the Code of Conduct for guidance.

Call for Contributions:

The project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact by email first.