Linear Single-Objective

Given a set of input-output pairs, the task is to optimize the parameters of a linear function of the form:

\[ y = m_1 x_1 + m_2 x_2 + \cdots + m_n x_n \]

where:

• \( x_1, x_2, \dots, x_n \) are the input features.
• \( m_1, m_2, \dots, m_n \) are the unknown parameters (coefficients) of the linear function that need to be optimized.
• \( y \) is the output corresponding to the given inputs \( x_1, x_2, \dots, x_n \).

The goal is to optimize \( w_1, w_2, \dots, w_n \) so that the output is as close as possible to the objective target output \( y \).