Linear Least Squares

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.

Functions

expand all

evaluateEvaluate optimization expression or objectives and constraints in problem
infeasibilityConstraint violation at a point
optimproblemCreate optimization problem
optimvarCreate optimization variables
solveSolve optimization problem or equation problem
lsqlinSolve constrained linear least-squares problems
lsqnonnegSolve nonnegative linear least-squares problem
optim.coder.infboundInfinite bound support for code generation (Since R2022b)
mldivide, \Solve systems of linear equations Ax = B for x
optimwarmstartCreate warm start object (Since R2021a)

Live Editor Tasks

OptimizeOptimize or solve equations in the Live Editor (Since R2020b)

Topics

Problem-Based Linear Least Squares

Solver-Based Linear Least Squares

Code Generation

Problem-Based Algorithms

Algorithms and Options