nonlinear-programming

What problem does it solve?

Nonlinear programming helps solve optimization problems where the objective function or constraints are nonlinear, making linear-programming approaches unsuitable. It covers functions like x^2, xy, sin(x), exp(x), and sqrt(x), and handles both unconstrained and constrained scenarios.

Core Features & Use Cases

• Handles nonlinear objectives and constraints with gradient-based or derivative-free solvers.
• Supports common problem types: engineering design, parameter fitting, optimal control, and facility/location decisions.
• Works with standard tools (e.g., SciPy) and allows multiple starting points to mitigate local optima.

Quick Start

Define your nonlinear objective and constraints, provide an initial guess, choose a solver (e.g., SLSQP), and run the optimizer. Example: set up objective function f(x), constraints g(x) <= 0 and h(x) = 0, initialize x0, call minimize with method='SLSQP', check result.