# lsqlin

Solve constrained linear least-squares problems

## Syntax

``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(___)``````

## Description

Linear least-squares solver with bounds or linear constraints.

Solves least-squares curve fitting problems of the form

Note

`lsqlin` applies only to the solver-based approach. For a discussion of the two optimization approaches, see First Choose Problem-Based or Solver-Based Approach.

example

````x = lsqlin(C,d,A,b)` solves the linear system `C*x = d` in the least-squares sense, subject to `A*x` ≤ `b`.```

example

````x = lsqlin(C,d,A,b,Aeq,beq,lb,ub)` adds linear equality constraints `Aeq*x = beq` and bounds `lb` ≤ `x` ≤ `ub`. If you do not need certain constraints such as `Aeq` and `beq`, set them to `[]`. If `x(i)` is unbounded below, set `lb(i) = -Inf`, and if `x(i)` is unbounded above, set `ub(i) = Inf`.```

example

````x = lsqlin(C,d,A,b,Aeq,beq,lb,ub,x0,options)` minimizes with an initial point `x0` and the optimization options specified in `options`. Use `optimoptions` to set these options. If you do not want to include an initial point, set the `x0` argument to `[]`.```
````x = lsqlin(problem)` finds the minimum for `problem`, a structure described in `problem`. Create the `problem` structure using dot notation or the `struct` function. Or create a `problem` structure from an `OptimizationProblem` object by using `prob2struct`.```

example

``````[x,resnorm,residual,exitflag,output,lambda] = lsqlin(___)```, for any input arguments described above, returns:The squared 2-norm of the residual `resnorm = `${‖C\cdot x-d‖}_{2}^{2}$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.```

## Examples

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Find the `x` that minimizes the norm of `C*x - d` for an overdetermined problem with linear inequality constraints.

Specify the problem and constraints.

```C = [0.9501 0.7620 0.6153 0.4057 0.2311 0.4564 0.7919 0.9354 0.6068 0.0185 0.9218 0.9169 0.4859 0.8214 0.7382 0.4102 0.8912 0.4447 0.1762 0.8936]; d = [0.0578 0.3528 0.8131 0.0098 0.1388]; A = [0.2027 0.2721 0.7467 0.4659 0.1987 0.1988 0.4450 0.4186 0.6037 0.0152 0.9318 0.8462]; b = [0.5251 0.2026 0.6721];```

Call `lsqlin` to solve the problem.

`x = lsqlin(C,d,A,b)`
```Minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. ```
```x = 4×1 0.1299 -0.5757 0.4251 0.2438 ```

Find the `x` that minimizes the norm of `C*x - d` for an overdetermined problem with linear equality and inequality constraints and bounds.

Specify the problem and constraints.

```C = [0.9501 0.7620 0.6153 0.4057 0.2311 0.4564 0.7919 0.9354 0.6068 0.0185 0.9218 0.9169 0.4859 0.8214 0.7382 0.4102 0.8912 0.4447 0.1762 0.8936]; d = [0.0578 0.3528 0.8131 0.0098 0.1388]; A =[0.2027 0.2721 0.7467 0.4659 0.1987 0.1988 0.4450 0.4186 0.6037 0.0152 0.9318 0.8462]; b =[0.5251 0.2026 0.6721]; Aeq = [3 5 7 9]; beq = 4; lb = -0.1*ones(4,1); ub = 2*ones(4,1);```

Call `lsqlin` to solve the problem.

`x = lsqlin(C,d,A,b,Aeq,beq,lb,ub)`
```Minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. ```
```x = 4×1 -0.1000 -0.1000 0.1599 0.4090 ```

This example shows how to use nondefault options for linear least squares.

Set options to use the `'interior-point'` algorithm and to give iterative display.

`options = optimoptions('lsqlin','Algorithm','interior-point','Display','iter');`

Set up a linear least-squares problem.

```C = [0.9501 0.7620 0.6153 0.4057 0.2311 0.4564 0.7919 0.9354 0.6068 0.0185 0.9218 0.9169 0.4859 0.8214 0.7382 0.4102 0.8912 0.4447 0.1762 0.8936]; d = [0.0578 0.3528 0.8131 0.0098 0.1388]; A = [0.2027 0.2721 0.7467 0.4659 0.1987 0.1988 0.4450 0.4186 0.6037 0.0152 0.9318 0.8462]; b = [0.5251 0.2026 0.6721];```

Run the problem.

`x = lsqlin(C,d,A,b,[],[],[],[],[],options)`
``` Iter Fval Primal Infeas Dual Infeas Complementarity 0 -7.687420e-02 1.600492e+00 6.150431e-01 1.000000e+00 1 -7.687419e-02 8.002458e-04 3.075216e-04 2.430833e-01 2 -3.162837e-01 4.001229e-07 1.537608e-07 5.945636e-02 3 -3.760545e-01 2.000615e-10 2.036997e-08 1.370933e-02 4 -3.912129e-01 9.997558e-14 1.006816e-08 2.548273e-03 5 -3.948062e-01 0.000000e+00 2.955102e-09 4.295807e-04 6 -3.953277e-01 1.110223e-16 1.237758e-09 3.102850e-05 7 -3.953581e-01 1.665335e-16 1.645863e-10 1.138719e-07 8 -3.953582e-01 5.551115e-17 2.400025e-13 5.693290e-11 Minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. ```
```x = 4×1 0.1299 -0.5757 0.4251 0.2438 ```

Obtain and interpret all `lsqlin` outputs.

Define a problem with linear inequality constraints and bounds. The problem is overdetermined because there are four columns in the `C` matrix but five rows. This means the problem has four unknowns and five conditions, even before including the linear constraints and bounds.

```C = [0.9501 0.7620 0.6153 0.4057 0.2311 0.4564 0.7919 0.9354 0.6068 0.0185 0.9218 0.9169 0.4859 0.8214 0.7382 0.4102 0.8912 0.4447 0.1762 0.8936]; d = [0.0578 0.3528 0.8131 0.0098 0.1388]; A = [0.2027 0.2721 0.7467 0.4659 0.1987 0.1988 0.4450 0.4186 0.6037 0.0152 0.9318 0.8462]; b = [0.5251 0.2026 0.6721]; lb = -0.1*ones(4,1); ub = 2*ones(4,1);```

Set options to use the `'interior-point'` algorithm.

`options = optimoptions('lsqlin','Algorithm','interior-point');`

The `'interior-point'` algorithm does not use an initial point, so set `x0` to `[]`.

`x0 = [];`

Call `lsqlin` with all outputs.

```[x,resnorm,residual,exitflag,output,lambda] = ... lsqlin(C,d,A,b,[],[],lb,ub,x0,options)```
```Minimum found that satisfies the constraints. Optimization completed because the objective function is non-decreasing in feasible directions, to within the value of the optimality tolerance, and constraints are satisfied to within the value of the constraint tolerance. ```
```x = 4×1 -0.1000 -0.1000 0.2152 0.3502 ```
```resnorm = 0.1672 ```
```residual = 5×1 0.0455 0.0764 -0.3562 0.1620 0.0784 ```
```exitflag = 1 ```
```output = struct with fields: message: '...' algorithm: 'interior-point' firstorderopt: 4.3374e-11 constrviolation: 0 iterations: 6 linearsolver: 'dense' cgiterations: [] ```
```lambda = struct with fields: ineqlin: [3x1 double] eqlin: [0x1 double] lower: [4x1 double] upper: [4x1 double] ```

Examine the nonzero Lagrange multiplier fields in more detail. First examine the Lagrange multipliers for the linear inequality constraint.

`lambda.ineqlin`
```ans = 3×1 0.0000 0.2392 0.0000 ```

Lagrange multipliers are nonzero exactly when the solution is on the corresponding constraint boundary. In other words, Lagrange multipliers are nonzero when the corresponding constraint is active. `lambda.ineqlin(2)` is nonzero. This means that the second element in `A*x` should equal the second element in `b`, because the constraint is active.

`[A(2,:)*x,b(2)]`
```ans = 1×2 0.2026 0.2026 ```

Now examine the Lagrange multipliers for the lower and upper bound constraints.

`lambda.lower`
```ans = 4×1 0.0409 0.2784 0.0000 0.0000 ```
`lambda.upper`
```ans = 4×1 0 0 0 0 ```

The first two elements of `lambda.lower` are nonzero. You see that `x(1)` and `x(2)` are at their lower bounds, `-0.1`. All elements of `lambda.upper` are essentially zero, and you see that all components of `x` are less than their upper bound, `2`.

## Input Arguments

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Multiplier matrix, specified as a matrix of doubles. `C` represents the multiplier of the solution `x` in the expression ```C*x - d```. `C` is `M`-by-`N`, where `M` is the number of equations, and `N` is the number of elements of `x`.

Example: `C = [1,4;2,5;7,8]`

Data Types: `double`

Constant vector, specified as a vector of doubles. `d` represents the additive constant term in the expression `C*x - d`. `d` is `M`-by-`1`, where `M` is the number of equations.

Example: `d = [5;0;-12]`

Data Types: `double`

Linear inequality constraints, specified as a real matrix. `A` is an `M`-by-`N` matrix, where `M` is the number of inequalities, and `N` is the number of variables (number of elements in `x0`). For large problems, pass `A` as a sparse matrix.

`A` encodes the `M` linear inequalities

`A*x <= b`,

where `x` is the column vector of `N` variables `x(:)`, and `b` is a column vector with `M` elements.

For example, to specify

x1 + 2x2 ≤ 10
3x1 + 4x2 ≤ 20
5x1 + 6x2 ≤ 30,

enter these constraints:

```A = [1,2;3,4;5,6]; b = [10;20;30];```

Example: To specify that the x components sum to 1 or less, use ```A = ones(1,N)``` and `b = 1`.

Data Types: `double`

Linear inequality constraints, specified as a real vector. `b` is an `M`-element vector related to the `A` matrix. If you pass `b` as a row vector, solvers internally convert `b` to the column vector `b(:)`. For large problems, pass `b` as a sparse vector.

`b` encodes the `M` linear inequalities

`A*x <= b`,

where `x` is the column vector of `N` variables `x(:)`, and `A` is a matrix of size `M`-by-`N`.

For example, consider these inequalities:

x1 + 2x2 ≤ 10
3x1 + 4x2 ≤ 20
5x1 + 6x2 ≤ 30.

Specify the inequalities by entering the following constraints.

```A = [1,2;3,4;5,6]; b = [10;20;30];```

Example: To specify that the x components sum to 1 or less, use ```A = ones(1,N)``` and `b = 1`.

Data Types: `double`

Linear equality constraints, specified as a real matrix. `Aeq` is an `Me`-by-`N` matrix, where `Me` is the number of equalities, and `N` is the number of variables (number of elements in `x0`). For large problems, pass `Aeq` as a sparse matrix.

`Aeq` encodes the `Me` linear equalities

`Aeq*x = beq`,

where `x` is the column vector of `N` variables `x(:)`, and `beq` is a column vector with `Me` elements.

For example, to specify

x1 + 2x2 + 3x3 = 10
2x1 + 4x2 + x3 = 20,

enter these constraints:

```Aeq = [1,2,3;2,4,1]; beq = [10;20];```

Example: To specify that the x components sum to 1, use `Aeq = ones(1,N)` and `beq = 1`.

Data Types: `double`

Linear equality constraints, specified as a real vector. `beq` is an `Me`-element vector related to the `Aeq` matrix. If you pass `beq` as a row vector, solvers internally convert `beq` to the column vector `beq(:)`. For large problems, pass `beq` as a sparse vector.

`beq` encodes the `Me` linear equalities

`Aeq*x = beq`,

where `x` is the column vector of `N` variables `x(:)`, and `Aeq` is a matrix of size `Me`-by-`N`.

For example, consider these equalities:

x1 + 2x2 + 3x3 = 10
2x1 + 4x2 + x3 = 20.

Specify the equalities by entering the following constraints.

```Aeq = [1,2,3;2,4,1]; beq = [10;20];```

Example: To specify that the x components sum to 1, use `Aeq = ones(1,N)` and `beq = 1`.

Data Types: `double`

Lower bounds, specified as a vector or array of doubles. `lb` represents the lower bounds elementwise in `lb `` x `` ub`.

Internally, `lsqlin` converts an array `lb` to the vector `lb(:)`.

Example: `lb = [0;-Inf;4]` means ```x(1) ≥ 0```, `x(3) ≥ 4`.

Data Types: `double`

Upper bounds, specified as a vector or array of doubles. `ub` represents the upper bounds elementwise in `lb `` x `` ub`.

Internally, `lsqlin` converts an array `ub` to the vector `ub(:)`.

Example: `ub = [Inf;4;10]` means ```x(2) ≤ 4```, `x(3) ≤ 10`.

Data Types: `double`

Initial point for the solution process, specified as a real vector or array. The `'trust-region-reflective'` and `'active-set'` algorithms use `x0` (optional).

If you do not specify `x0` for the `'trust-region-reflective'` or `'active-set'` algorithm, `lsqlin` sets `x0` to the zero vector. If any component of this zero vector `x0` violates the bounds, `lsqlin` sets `x0` to a point in the interior of the box defined by the bounds.

Example: `x0 = [4;-3]`

Data Types: `double`

Options for `lsqlin`, specified as the output of the `optimoptions` function or as a structure such as created by `optimset`.

Some options are absent from the `optimoptions` display. These options appear in italics in the following table. For details, see View Options.

All Algorithms

 `Algorithm` Choose the algorithm:`'interior-point'` (default)`'trust-region-reflective'``'active-set'`The `'trust-region-reflective'` algorithm allows only upper and lower bounds, no linear inequalities or equalities. If you specify both the `'trust-region-reflective'` algorithm and linear constraints, `lsqlin` uses the `'interior-point'` algorithm.The `'trust-region-reflective'` algorithm does not allow equal upper and lower bounds.When the problem has no constraints, `lsqlin` calls `mldivide` internally.If you have a large number of linear constraints and not a large number of variables, try the `'active-set'` algorithm.For more information on choosing the algorithm, see Choosing the Algorithm. Diagnostics Display diagnostic information about the function to be minimized or solved. The choices are `'on'` or the default `'off'`. `Display` Level of display returned to the command line.`'off'` or `'none'` displays no output.`'final'` displays just the final output (default).The `'interior-point'` algorithm allows additional values:`'iter'` gives iterative display.`'iter-detailed'` gives iterative display with a detailed exit message.`'final-detailed'` displays just the final output, with a detailed exit message. `MaxIterations` Maximum number of iterations allowed, a positive integer. The default value is `2000` for the `'active-set'` algorithm, and `200` for the other algorithms.For `optimset`, the option name is `MaxIter`. See Current and Legacy Option Names.

`trust-region-reflective` Algorithm Options

 `FunctionTolerance` Termination tolerance on the function value, a positive scalar. The default is `100*eps`, about `2.2204e-14`.For `optimset`, the option name is `TolFun`. See Current and Legacy Option Names. `JacobianMultiplyFcn` Jacobian multiply function, specified as a function handle. For large-scale structured problems, this function should compute the Jacobian matrix product `C*Y`, `C'*Y`, or `C'*(C*Y)` without actually forming `C`. Write the function in the form`W = jmfun(Jinfo,Y,flag)`where `Jinfo` contains a matrix used to compute `C*Y` (or `C'*Y`, or `C'*(C*Y)`).`jmfun` must compute one of three different products, depending on the value of `flag` that `lsqlin` passes:If `flag == 0` then `W = C'*(C*Y)`. If `flag > 0` then `W = C*Y`. If `flag < 0` then `W = C'*Y`. In each case, `jmfun` need not form `C` explicitly. `lsqlin` uses `Jinfo` to compute the preconditioner. See Passing Extra Parameters for information on how to supply extra parameters if necessary.See Jacobian Multiply Function with Linear Least Squares for an example.For `optimset`, the option name is `JacobMult`. See Current and Legacy Option Names. MaxPCGIter Maximum number of PCG (preconditioned conjugate gradient) iterations, a positive scalar. The default is `max(1,floor(numberOfVariables/2))`. For more information, see Trust-Region-Reflective Algorithm. `OptimalityTolerance` Termination tolerance on the first-order optimality, a positive scalar. The default is `100*eps`, about `2.2204e-14`. See First-Order Optimality Measure.For `optimset`, the option name is `TolFun`. See Current and Legacy Option Names. PrecondBandWidth Upper bandwidth of preconditioner for PCG (preconditioned conjugate gradient). By default, diagonal preconditioning is used (upper bandwidth of 0). For some problems, increasing the bandwidth reduces the number of PCG iterations. Setting `PrecondBandWidth` to `Inf` uses a direct factorization (Cholesky) rather than the conjugate gradients (CG). The direct factorization is computationally more expensive than CG, but produces a better quality step toward the solution. For more information, see Trust-Region-Reflective Algorithm. `SubproblemAlgorithm` Determines how the iteration step is calculated. The default, `'cg'`, takes a faster but less accurate step than `'factorization'`. See Trust-Region-Reflective Least Squares. TolPCG Termination tolerance on the PCG (preconditioned conjugate gradient) iteration, a positive scalar. The default is `0.1`. `TypicalX` Typical `x` values. The number of elements in `TypicalX` is equal to the number of variables. The default value is `ones(numberofvariables,1)`. `lsqlin` uses `TypicalX` internally for scaling. `TypicalX` has an effect only when `x` has unbounded components, and when a `TypicalX` value for an unbounded component is larger than `1`.

`interior-point` Algorithm Options

 `ConstraintTolerance` Tolerance on the constraint violation, a positive scalar. The default is `1e-8`.For `optimset`, the option name is `TolCon`. See Current and Legacy Option Names. `LinearSolver` Type of internal linear solver in algorithm:`'auto'` (default) — Use `'sparse'` if the `C` matrix is sparse, `'dense'` otherwise.`'sparse'` — Use sparse linear algebra. See Sparse Matrices.`'dense'` — Use dense linear algebra. `OptimalityTolerance` Termination tolerance on the first-order optimality, a positive scalar. The default is `1e-8`. See First-Order Optimality Measure.For `optimset`, the option name is `TolFun`. See Current and Legacy Option Names. `StepTolerance` Termination tolerance on `x`, a positive scalar. The default is `1e-12`.For `optimset`, the option name is `TolX`. See Current and Legacy Option Names.

`'active-set'` Algorithm Options

 `ConstraintTolerance` Tolerance on the constraint violation, a positive scalar. The default value is `1e-8`.For `optimset`, the option name is `TolCon`. See Current and Legacy Option Names. `ObjectiveLimit` A tolerance (stopping criterion) that is a scalar. If the objective function value goes below `ObjectiveLimit` and the current point is feasible, the iterations halt because the problem is unbounded, presumably. The default value is `-1e20`. `OptimalityTolerance` Termination tolerance on the first-order optimality, a positive scalar. The default value is `1e-8`. See First-Order Optimality Measure.For `optimset`, the name is `TolFun`. See Current and Legacy Option Names. `StepTolerance` Termination tolerance on `x`, a positive scalar. The default value is `1e-8`.For `optimset`, the option name is `TolX`. See Current and Legacy Option Names.

Optimization problem, specified as a structure with the following fields.

 `C` Matrix multiplier in the term ```C*x - d``` `d` Additive constant in the term ```C*x - d``` `Aineq` Matrix for linear inequality constraints `bineq` Vector for linear inequality constraints `Aeq` Matrix for linear equality constraints `beq` Vector for linear equality constraints `lb` Vector of lower bounds `ub` Vector of upper bounds `x0` Initial point for `x` `solver` `'lsqlin'` `options` Options created with `optimoptions`

Data Types: `struct`

## Output Arguments

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Solution, returned as a vector that minimizes the norm of `C*x-d` subject to all bounds and linear constraints.

Objective value, returned as the scalar value `norm(C*x-d)^2`.

Solution residuals, returned as the vector `C*x-d`.

Algorithm stopping condition, returned as an integer identifying the reason the algorithm stopped. The following lists the values of `exitflag` and the corresponding reasons `lsqlin` stopped.

 `3` Change in the residual was smaller than the specified tolerance `options.FunctionTolerance`. (`trust-region-reflective` algorithm) `2` Step size smaller than `options.StepTolerance`, constraints satisfied. (`interior-point` algorithm) `1` Function converged to a solution `x`. `0` Number of iterations exceeded `options.MaxIterations`. `-2` The problem is infeasible. Or, for the `interior-point` algorithm, step size smaller than `options.StepTolerance`, but constraints are not satisfied. `-3` The problem is unbounded. `-4` Ill-conditioning prevents further optimization. `-8` Unable to compute a step direction.

The exit message for the `interior-point` algorithm can give more details on the reason `lsqlin` stopped, such as exceeding a tolerance. See Exit Flags and Exit Messages.

Solution process summary, returned as a structure containing information about the optimization process.

 `iterations` Number of iterations the solver took. `algorithm` One of these algorithms:`'interior-point'``'trust-region-reflective'``'mldivide'` for an unconstrained problemFor an unconstrained problem, `iterations` = 0, and the remaining entries in the `output` structure are empty. `constrviolation` Constraint violation that is positive for violated constraints (not returned for the `'trust-region-reflective'` algorithm).```constrviolation = max([0;norm(Aeq*x-beq, inf);(lb-x);(x-ub);(A*x-b)])``` `message` Exit message. `firstorderopt` First-order optimality at the solution. See First-Order Optimality Measure. `linearsolver` Type of internal linear solver, `'dense'` or `'sparse'` (`'interior-point'` algorithm only) `cgiterations` Number of conjugate gradient iterations the solver performed. Nonempty only for the `'trust-region-reflective'` algorithm.

Lagrange multipliers, returned as a structure with the following fields.

 `lower` Lower bounds `lb` `upper` Upper bounds `ub` `ineqlin` Linear inequalities `eqlin` Linear equalities

## Tips

• 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.

• If your problem has many linear constraints and few variables, try using the `'active-set'` algorithm. See Quadratic Programming with Many Linear Constraints.

• 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.

## Algorithms

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### Trust-Region-Reflective Algorithm

This method is a subspace trust-region method based on the interior-reflective Newton method described in [1]. Each iteration involves the approximate solution of a large linear system using the method of preconditioned conjugate gradients (PCG). See Trust-Region-Reflective Least Squares, and in particular Large Scale Linear Least Squares.

### Interior-Point Algorithm

The `'interior-point'` algorithm is based on the `quadprog` `'interior-point-convex'` algorithm. See Linear Least Squares: Interior-Point or Active-Set.

### Active-Set Algorithm

The `'active-set'` algorithm is based on the `quadprog` `'active-set'` algorithm. For more information, see Linear Least Squares: Interior-Point or Active-Set and active-set quadprog Algorithm.

## References

[1] Coleman, T. F. and Y. Li. “A Reflective Newton Method for Minimizing a Quadratic Function Subject to Bounds on Some of the Variables,” SIAM Journal on Optimization, Vol. 6, Number 4, pp. 1040–1058, 1996.

[2] Gill, P. E., W. Murray, and M. H. Wright. Practical Optimization, Academic Press, London, UK, 1981.

## Alternative Functionality

### App

The Optimize Live Editor task provides a visual interface for `lsqlin`.

Introduced before R2006a