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
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
Shows how to solve a linear least-squares problem using the problem-based approach.
Shows how to solve a nonnegative linear least-squares problem using the problem-based approach and several solvers.
Solves an optical deblurring problem using the problem-based approach.
Example showing the Optimization app and linear least squares.
This example shows how to use several algorithms to solve a linear least-squares problem with the bound constraint that the solution is nonnegative.
Example showing how to save memory in a large structured linear least-squares problem.
Solves an optical deblurring problem using the solver-based approach.
Syntax rules for problem-based least squares.
How the optimization functions and objects solve optimization problems.
Lists all available mathematical and indexing operations on optimization variables and expressions.