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Solve constrained linear least-squares problems

Linear least-squares solver with bounds or linear constraints.

Solves least-squares curve fitting problems of the form

$$\underset{x}{\mathrm{min}}\frac{1}{2}{\Vert C\cdot x-d\Vert}_{2}^{2}\text{suchthat}\{\begin{array}{c}A\cdot x\le b,\\ Aeq\cdot x=beq,\\ lb\le x\le ub.\end{array}$$

`x = lsqlin(C,d,A,b)`

`x = lsqlin(C,d,A,b,Aeq,beq,lb,ub)`

`x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,options)`

`x = lsqlin(problem)`

```
[x,resnorm,residual,exitflag,output,lambda]
= lsqlin(___)
```

finds
the minimum for `x`

= lsqlin(`problem`

)`problem`

, where `problem`

is
a structure. Create the `problem`

structure by exporting
a problem from Optimization app, as described in Exporting Your Work.

`[`

, for any input arguments described
above, returns:`x`

,`resnorm`

,`residual`

,`exitflag`

,`output`

,`lambda`

]
= lsqlin(___)

The squared 2-norm of the residual

`resnorm =`

$${\Vert C\cdot x-d\Vert}_{2}^{2}$$The residual

`residual = C*x - d`

A value

`exitflag`

describing the exit conditionA structure

`output`

containing information about the optimization processA structure

`lambda`

containing the Lagrange multipliersThe factor ½ in the definition of the problem affects the values in the

`lambda`

structure.

For problems with no constraints, you can use

`mldivide`

(matrix left division). When you have no constraints,`lsqlin`

returns`x = C\d`

.Because the problem being solved is always convex,

`lsqlin`

finds a global, although not necessarily unique, solution.Better numerical results are likely if you specify equalities explicitly, using

`Aeq`

and`beq`

, instead of implicitly, using`lb`

and`ub`

.The

`trust-region-reflective`

algorithm does not allow equal upper and lower bounds. Use another algorithm for this case.If the specified input bounds for a problem are inconsistent, the output

`x`

is`x0`

and the outputs`resnorm`

and`residual`

are`[]`

.You can solve some large structured problems, including those where the

`C`

matrix is too large to fit in memory, using the`trust-region-reflective`

algorithm with a Jacobian multiply function. For information, see trust-region-reflective Algorithm Options.

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