Numerical Optimization

Numerical optimization is a cornerstone of modern engineering and scientific computing. It involves finding the best solution to a problem from a set of possible solutions, often under certain constraints. In SepalSolver, we implement various optimization algorithms to tackle problems ranging from simple function minimization to complex constrained optimization tasks.

Optimization techniques are widely used in fields such as machine learning, operations research, and control systems. They help in tuning model parameters, optimizing resource allocation, and improving system performance.

In this chapter, we will explore different optimization algorithms, their implementations in SepalSolver, and practical examples demonstrating their applications.

• Linear Programming
• Mixed Integer Linear Programming
• NonLinear Optimization
• Curve Fitting
• Constrained Curve Fitting
• Exercise On Numerical Optimization