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.
Linear least-squares solves min||C*x - d||2, possibly with bounds or linear 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 lsqlin or, for
nonnegative least squares, you can also use lsqnonneg.
Shows how to solve a linear least-squares problem using the problem-based approach.
Nonnegative Linear Least Squares, Problem-Based
Shows how to solve a nonnegative linear least-squares problem using the problem-based approach and several solvers.
Large-Scale Constrained Linear Least-Squares, Problem-Based
Solves an optical deblurring problem using the problem-based approach.
Optimization App with the lsqlin Solver
Example showing the Optimization app and linear least squares.
Nonnegative Linear Least Squares, Solver-Based
This example shows how to use several algorithms to solve a linear least-squares problem with the bound constraint that the solution is nonnegative.
Jacobian Multiply Function with Linear Least Squares
Example showing how to save memory in a large structured linear least-squares problem.
Large-Scale Constrained Linear Least-Squares, Solver-Based
Solves an optical deblurring problem using the solver-based approach.
Write Objective Function for Problem-Based Least Squares
Syntax rules for problem-based least squares.
Problem-Based Optimization Algorithms
How the optimization functions and objects solve optimization problems.
Supported Operations on Optimization Variables and Expressions
Lists all available mathematical and indexing operations on optimization variables and expressions.
Least-Squares (Model Fitting) Algorithms
Minimizing a sum of squares in n dimensions with only bound or linear constraints.
Optimization Options Reference
Explore optimization options.