Before you begin to solve an optimization problem, you must choose the appropriate approach: problem-based or solver-based. For details, see First Choose Problem-Based or Solver-Based Approach.

Nonlinear least-squares solves min(∑||F(x_i) - y_i||²), where F(x_i) is a nonlinear function and y_i is data. The problem can have bounds, linear constraints, or nonlinear constraints.

For the problem-based approach, create problem variables, and then represent the objective function and constraints in terms of these symbolic variables. For the problem-based steps to take, see Problem-Based Optimization Workflow. To solve the resulting problem, use solve.

For the solver-based steps to take, including defining the objective function and constraints, and choosing the appropriate solver, see Solver-Based Optimization Problem Setup. To solve the resulting problem, use lsqcurvefit or lsqnonlin.