Least Squares Method

The Least Squares Method is a mathematical optimization technique that finds the best functional match for data by minimizing the sum of squared errors. It provides a simple approach to estimate unknown parameters by ensuring the minimal squared difference between computed values and observed data. This method is widely used for curve fitting and can also express other optimization problems through energy minimization or entropy maximization. In implementation, it typically involves solving linear equations using matrix operations like numpy.linalg.lstsq() in Python or the backslash operator in MATLAB.