Solve linear least-squares problems with bounds or linear
constraints

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.