Documentation

## Syntax

``x = quadprog(H,f)``
``x = quadprog(H,f,A,b)``
``x = quadprog(H,f,A,b,Aeq,beq)``
``x = quadprog(H,f,A,b,Aeq,beq,lb,ub)``
``x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0)``
``x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0,options)``
``x = quadprog(problem)``
``````[x,fval] = quadprog(___)``````
``````[x,fval,exitflag,output] = quadprog(___)``````
``````[x,fval,exitflag,output,lambda] = quadprog(___)``````

## Description

Solver for quadratic objective functions with linear constraints.

quadprog finds a minimum for a problem specified by

H, A, and Aeq are matrices, and f, b, beq, lb, ub, and x are vectors.

You can pass f, lb, and ub as vectors or matrices; see Matrix Arguments.

### Note

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

````x = quadprog(H,f)` returns a vector `x` that minimizes `1/2*x'*H*x + f'*x`. The input `H` must be positive definite for the problem to have a finite minimum. If `H` is positive definite, then the solution ```x = H\(-f)```.```

example

````x = quadprog(H,f,A,b)` minimizes `1/2*x'*H*x + f'*x` subject to the restrictions `A*x `≤` b`. The input `A` is a matrix of doubles, and `b` is a vector of doubles.```

example

````x = quadprog(H,f,A,b,Aeq,beq)` solves the preceding problem subject to the additional restrictions `Aeq*x = beq`. `Aeq` is a matrix of doubles, and `beq` is a vector of doubles. If no inequalities exist, set `A = []` and `b = []`.```

example

````x = quadprog(H,f,A,b,Aeq,beq,lb,ub)` solves the preceding problem subject to the additional restrictions `lb `≤` x `≤` ub`. The inputs `lb` and `ub` are vectors of doubles, and the restrictions hold for each `x` component. If no equalities exist, set `Aeq = []` and `beq = []`. NoteIf the specified input bounds for a problem are inconsistent, the output `x` is `x0` and the output `fval` is `[]`.`quadprog` resets components of `x0` that violate the bounds `lb `≤` x `≤` ub` to the interior of the box defined by the bounds. `quadprog` does not change components that respect the bounds. ```
````x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0)` solves the preceding problem starting from the vector `x0`. If no bounds exist, set `lb = []` and `ub = []`. Some `quadprog` algorithms ignore `x0`; see `x0`.```

example

````x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0,options)` solves the preceding problem using the optimization options specified in `options`. Use `optimoptions` to create `options`. If you do not want to give an initial point, set `x0 = []`.```

example

````x = quadprog(problem)` returns the minimum for `problem`, where `problem` is a structure described in Description. Create `problem` by exporting a problem using the Optimization app; see Exporting Your Work. Alternatively, create a `problem` structure from an `OptimizationProblem` object by using `prob2struct`.```

example

``````[x,fval] = quadprog(___)```, for any input variables, also returns `fval`, the value of the objective function at `x`: fval = 0.5*x'*H*x + f'*x ```

example

``````[x,fval,exitflag,output] = quadprog(___)``` also returns `exitflag`, an integer that describes the exit condition of `quadprog`, and `output`, a structure that contains information about the optimization.```

example

``````[x,fval,exitflag,output,lambda] = quadprog(___)``` also returns `lambda`, a structure whose fields contain the Lagrange multipliers at the solution `x`.```

## Examples

collapse all

Find the minimum of

`$f\left(x\right)=\frac{1}{2}{x}_{1}^{2}+{x}_{2}^{2}-{x}_{1}{x}_{2}-2{x}_{1}-6{x}_{2}$`

subject to the constraints

`$\begin{array}{l}{x}_{1}+{x}_{2}\le 2\\ -{x}_{1}+2{x}_{2}\le 2\\ 2{x}_{1}+{x}_{2}\le 3.\end{array}$`

In `quadprog` syntax, this problem is to minimize

$f\left(x\right)=\frac{1}{2}{x}^{T}Hx+{f}^{T}x$,

where

`$\begin{array}{l}\mathit{H}=\left[\begin{array}{cc}1& -1\\ -1& 2\end{array}\right]\\ \mathit{f}=\left[\begin{array}{c}-2\\ -6\end{array}\right],\end{array}$`

subject to the linear constraints.

To solve this problem, first enter the coefficient matrices.

```H = [1 -1; -1 2]; f = [-2; -6]; A = [1 1; -1 2; 2 1]; b = [2; 2; 3];```

Call `quadprog`.

```[x,fval,exitflag,output,lambda] = ... quadprog(H,f,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. ```

Examine the final point, function value, and exit flag.

`x,fval,exitflag`
```x = 2×1 0.6667 1.3333 ```
```fval = -8.2222 ```
```exitflag = 1 ```

An exit flag of `1` means the result is a local minimum. Because `H` is a positive definite matrix, this problem is convex, so the minimum is a global minimum.

Confirm that `H` is positive definite by checking its eigenvalues.

`eig(H)`
```ans = 2×1 0.3820 2.6180 ```

Find the minimum of

`$f\left(x\right)=\frac{1}{2}{x}_{1}^{2}+{x}_{2}^{2}-{x}_{1}{x}_{2}-2{x}_{1}-6{x}_{2}$`

subject to the constraint

`${x}_{1}+{x}_{2}=0.$`

In `quadprog` syntax, this problem is to minimize

$f\left(x\right)=\frac{1}{2}{x}^{T}Hx+{f}^{T}x$,

where

`$\begin{array}{l}\mathit{H}=\left[\begin{array}{cc}1& -1\\ -1& 2\end{array}\right]\\ \mathit{f}=\left[\begin{array}{c}-2\\ -6\end{array}\right],\end{array}$`

subject to the linear constraint.

To solve this problem, first enter the coefficient matrices.

```H = [1 -1; -1 2]; f = [-2; -6]; Aeq = [1 1]; beq = 0;```

Call `quadprog`, entering `[]` for the inputs `A` and `b`.

```[x,fval,exitflag,output,lambda] = ... quadprog(H,f,[],[],Aeq,beq);```
```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. ```

Examine the final point, function value, and exit flag.

`x,fval,exitflag`
```x = 2×1 -0.8000 0.8000 ```
```fval = -1.6000 ```
```exitflag = 1 ```

An exit flag of `1` means the result is a local minimum. Because `H` is a positive definite matrix, this problem is convex, so the minimum is a global minimum.

Confirm that `H` is positive definite by checking its eigenvalues.

`eig(H)`
```ans = 2×1 0.3820 2.6180 ```

Find the x that minimizes the quadratic expression

`$\frac{1}{2}{x}^{T}Hx+{f}^{T}x$`

where

$\mathit{H}=\left[\begin{array}{ccc}1& -1& 1\\ -1& 2& -2\\ 1& -2& 4\end{array}\right]$, $\mathit{f}=\left[\begin{array}{c}2\\ -3\\ 1\end{array}\right]$,

subject to the constraints

$0\le x\le 1$, $\sum x=1/2$.

To solve this problem, first enter the coefficients.

```H = [1,-1,1 -1,2,-2 1,-2,4]; f = [2;-3;1]; lb = zeros(3,1); ub = ones(size(lb)); Aeq = ones(1,3); beq = 1/2;```

Call `quadprog`, entering `[]` for the inputs `A` and `b`.

`x = quadprog(H,f,[],[],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 = 3×1 0.0000 0.5000 0.0000 ```

Set options to monitor the progress of `quadprog`.

`options = optimoptions('quadprog','Display','iter');`

Define a problem with a quadratic objective and linear inequality constraints.

```H = [1 -1; -1 2]; f = [-2; -6]; A = [1 1; -1 2; 2 1]; b = [2; 2; 3];```

To help write the `quadprog` function call, set the unnecessary inputs to `[]`.

```Aeq = []; beq = []; lb = []; ub = []; x0 = [];```

Call `quadprog` to solve the problem.

`x = quadprog(H,f,A,b,Aeq,beq,lb,ub,x0,options)`
``` Iter Fval Primal Infeas Dual Infeas Complementarity 0 -8.884885e+00 3.214286e+00 1.071429e-01 1.000000e+00 1 -8.331868e+00 1.321041e-01 4.403472e-03 1.910489e-01 2 -8.212804e+00 1.676295e-03 5.587652e-05 1.009601e-02 3 -8.222204e+00 8.381476e-07 2.793826e-08 1.809485e-05 4 -8.222222e+00 3.064216e-14 1.352696e-12 7.525735e-13 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 = 2×1 0.6667 1.3333 ```

Create a `problem` structure using a Problem-Based Optimization Workflow. Create an optimization problem equivalent to Quadratic Program with Linear Constraints.

```x = optimvar('x',2); objec = x(1)^2/2 + x(2)^2 - x(1)*x(2) - 2*x(1) - 6*x(2); prob = optimproblem('Objective',objec); prob.Constraints.cons1 = sum(x) <= 2; prob.Constraints.cons2 = -x(1) + 2*x(2) <= 2; prob.Constraints.cons3 = 2*x(1) + x(2) <= 3;```

Convert `prob` to a `problem` structure.

`problem = prob2struct(prob);`

Solve the problem using `quadprog`.

`[x,fval] = quadprog(problem)`
```Warning: Your Hessian is not symmetric. Resetting H=(H+H')/2. ```
```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 = 2×1 0.6667 1.3333 ```
```fval = -8.2222 ```

Solve a quadratic program and return both the solution and the objective function value.

```H = [1,-1,1 -1,2,-2 1,-2,4]; f = [-7;-12;-15]; A = [1,1,1]; b = 3; [x,fval] = quadprog(H,f,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 = 3×1 -3.5714 2.9286 3.6429 ```
```fval = -47.1786 ```

Check that the returned objective function value matches the value computed from the `quadprog` objective function definition.

`fval2 = 1/2*x'*H*x + f'*x`
```fval2 = -47.1786 ```

To see the optimization process for `quadprog`, set options to show an iterative display and return four outputs. The problem is to minimize

`$\frac{1}{2}{x}^{T}Hx+{f}^{T}x$`

subject to

$0\le x\le 1$,

where

$\mathit{H}=\left[\begin{array}{ccc}2& 1& -1\\ 1& 3& \frac{1}{2}\\ -1& \frac{1}{2}& 5\end{array}\right]$, $\mathit{f}=\left[\begin{array}{c}4\\ -7\\ 12\end{array}\right]$.

Enter the problem coefficients.

```H = [2 1 -1 1 3 1/2 -1 1/2 5]; f = [4;-7;12]; lb = zeros(3,1); ub = ones(3,1);```

Set the options to display iterative progress of the solver.

`options = optimoptions('quadprog','Display','iter');`

Call `quadprog` with four outputs.

`[x fval,exitflag,output] = quadprog(H,f,[],[],[],[],lb,ub,[],options)`
``` Iter Fval Primal Infeas Dual Infeas Complementarity 0 2.691769e+01 1.582123e+00 1.712849e+01 1.680447e+00 1 -3.889430e+00 0.000000e+00 8.564246e-03 9.971731e-01 2 -5.451769e+00 0.000000e+00 4.282123e-06 2.710131e-02 3 -5.499997e+00 0.000000e+00 1.221903e-10 6.939689e-07 4 -5.500000e+00 0.000000e+00 5.842173e-14 3.469847e-10 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 = 3×1 0.0000 1.0000 0.0000 ```
```fval = -5.5000 ```
```exitflag = 1 ```
```output = struct with fields: message: '...' algorithm: 'interior-point-convex' firstorderopt: 1.5921e-09 constrviolation: 0 iterations: 4 linearsolver: 'dense' cgiterations: [] ```

Solve a quadratic programming problem and return the Lagrange multipliers.

```H = [1,-1,1 -1,2,-2 1,-2,4]; f = [-7;-12;-15]; A = [1,1,1]; b = 3; lb = zeros(3,1); [x,fval,exitflag,output,lambda] = quadprog(H,f,A,b,[],[],lb);```
```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. ```

Examine the Lagrange multiplier structure `lambda`.

`disp(lambda)`
``` ineqlin: 12.0000 eqlin: [0x1 double] lower: [3x1 double] upper: [3x1 double] ```

The linear inequality constraint has an associated Lagrange multiplier of `12`.

Display the multipliers associated with the lower bound.

`disp(lambda.lower)`
``` 5.0000 0.0000 0.0000 ```

Only the first component of `lambda.lower` has a nonzero multiplier. This generally means that only the first component of `x` is at the lower bound of zero. Confirm by displaying the components of `x`.

`disp(x)`
``` 0.0000 1.5000 1.5000 ```

## Input Arguments

collapse all

Quadratic objective term, specified as a symmetric real matrix. `H` represents the quadratic in the expression `1/2*x'*H*x + f'*x`. If `H` is not symmetric, `quadprog` issues a warning and uses the symmetrized version `(H + H')/2` instead.

If the quadratic matrix `H` is sparse, then by default, the `'interior-point-convex'` algorithm uses a slightly different algorithm than when `H` is dense. Generally, the sparse algorithm is faster on large, sparse problems, and the dense algorithm is faster on dense or small problems. For more information, see the `LinearSolver` option description and interior-point-convex quadprog Algorithm.

Example: `[2,1;1,3]`

Data Types: `double`

Linear objective term, specified as a real vector. `f` represents the linear term in the expression `1/2*x'*H*x + f'*x`.

Example: `[1;3;2]`

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, 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 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, 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`

Lower bounds, specified as a real vector or real array. If the number of elements in `x0` is equal to the number of elements in `lb`, then `lb` specifies that

`x(i) >= lb(i)` for all `i`.

If `numel(lb) < numel(x0)`, then `lb` specifies that

`x(i) >= lb(i)` for ```1 <= i <= numel(lb)```.

If there are fewer elements in `lb` than in `x0`, solvers issue a warning.

Example: To specify that all x components are positive, use ```lb = zeros(size(x0))```.

Data Types: `double`

Upper bounds, specified as a real vector or real array. If the number of elements in `x0` is equal to the number of elements in `ub`, then `ub` specifies that

`x(i) <= ub(i)` for all `i`.

If `numel(ub) < numel(x0)`, then `ub` specifies that

`x(i) <= ub(i)` for ```1 <= i <= numel(ub)```.

If there are fewer elements in `ub` than in `x0`, solvers issue a warning.

Example: To specify that all x components are less than 1, use ```ub = ones(size(x0))```.

Data Types: `double`

Initial point, specified as a real vector. This input is optional. `x0` applies only to the `'trust-region-reflective'` algorithm when there are only bound constraints.

If you do not specify `x0`, `quadprog` sets all components of `x0` to a point in the interior of the box defined by the bounds. `quadprog` ignores `x0` for the `'interior-point-convex'` algorithm and for the `'trust-region-reflective'` algorithm with equality constraints.

Example: `[1;2;1]`

Data Types: `double`

Optimization options, specified as the output of `optimoptions` or a structure such as `optimset` returns.

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-convex'` (default)`'trust-region-reflective'`The `'interior-point-convex'` algorithm handles only convex problems. The `'trust-region-reflective'` algorithm handles problems with only bounds or only linear equality constraints, but not both. For details, see Choosing the Algorithm. Diagnostics Display diagnostic information about the function to be minimized or solved. The choices are `'on'` or `'off'` (default). `Display` Level of display (see Iterative Display):`'off'` or `'none'` displays no output.`'final'` displays only the final output (default).The `'interior-point-convex'` algorithm allows additional values:`'iter'` specifies an iterative display.`'iter-detailed'` specifies an iterative display with a detailed exit message.`'final-detailed'` displays only the final output with a detailed exit message. `MaxIterations` Maximum number of iterations allowed; a positive integer. For a `'trust-region-reflective'` equality-constrained problem, the default value is ```2*(numberOfVariables - numberOfEqualities)```.For all other algorithms and problems, the default value is `200`.For `optimset`, the option name is `MaxIter`. See Current and Legacy Option Name Tables. `OptimalityTolerance` Termination tolerance on the first-order optimality; a positive scalar.For a `'trust-region-reflective'` equality-constrained problem, the default value is `1e-6`.For a `'trust-region-reflective'` bound-constrained problem, the default value is `100*eps`, about `2.2204e-14`.For `'interior-point-convex'` algorithms, the default value is `1e-8`.For `optimset`, the option name is `TolFun`. See Current and Legacy Option Name Tables. `StepTolerance` Termination tolerance on `x`; a positive scalar.For `'trust-region-reflective'`, the default value is `100*eps`, about `2.2204e-14`.For `'interior-point-convex'`, the default value is `1e-12`.For `optimset`, the option name is `TolX`. See Current and Legacy Option Name Tables.

`'trust-region-reflective'` Algorithm Only

 `FunctionTolerance` Termination tolerance on the function value; a positive scalar. The default value depends on the problem type: bound-constrained problems use `100*eps`, and linear equality-constrained problems use `1e-6`. See Tolerances and Stopping Criteria.For `optimset`, the option name is `TolFun`. See Current and Legacy Option Name Tables. `HessianMultiplyFcn` Hessian multiply function, specified as a function handle. For large-scale structured problems, this function computes the Hessian matrix product `H*Y` without actually forming `H`. The function has the form`W = hmfun(Hinfo,Y)`where `Hinfo` (and potentially some additional parameters) contain the matrices used to compute `H*Y`.See Quadratic Minimization with Dense, Structured Hessian for an example that uses this option.For `optimset`, the option name is `HessMult`. See Current and Legacy Option Name Tables. MaxPCGIter Maximum number of PCG (preconditioned conjugate gradient) iterations; a positive scalar. The default is `max(1,floor(numberOfVariables/2))` for bound-constrained problems. For equality-constrained problems, `quadprog` ignores `MaxPCGIter` and uses `MaxIterations` to limit the number of PCG iterations. For more information, see Preconditioned Conjugate Gradient Method. PrecondBandWidth Upper bandwidth of the preconditioner for PCG; a nonnegative integer. By default, `quadprog` uses diagonal preconditioning (upper bandwidth `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. `SubproblemAlgorithm` Determines how the iteration step is calculated. The default, `'cg'`, takes a faster but less accurate step than `'factorization'`. See trust-region-reflective quadprog Algorithm. TolPCG Termination tolerance on the PCG iteration; a positive scalar. The default is `0.1`. `TypicalX` Typical `x` values. The number of elements in `TypicalX` equals the number of elements in `x0`, the starting point. The default value is `ones(numberOfVariables,1)`. `quadprog` uses `TypicalX` internally for scaling. `TypicalX` has an effect only when `x` has unbounded components, and when a `TypicalX` value for an unbounded component exceeds `1`.

`'interior-point-convex'` Algorithm Only

 `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 Name Tables. `LinearSolver` Type of internal linear solver in the algorithm:`'auto'` (default) — Use `'sparse'` if the `H` matrix is sparse and `'dense'` otherwise.`'sparse'` — Use sparse linear algebra. See Sparse Matrices (MATLAB).`'dense'` — Use dense linear algebra.

Problem structure, specified as a structure with these fields:

 `H` Symmetric matrix in `1/2*x'*H*x` `f` Vector in linear term `f'*x` `Aineq` Matrix in linear inequality constraints `Aineq*x `≤` bineq` `bineq` Vector in linear inequality constraints `Aineq*x `≤` bineq` `Aeq` Matrix in linear equality constraints `Aeq*x = beq` `beq` Vector in linear equality constraints `Aeq*x = beq` `lb` Vector of lower bounds `ub` Vector of upper bounds `x0` Initial point for `x` `solver` `'quadprog'` `options` Options created using `optimoptions` or the Optimization app

The required fields are `H`, `f`, `solver`, and `options`. When solving, `quadprog` ignores any fields in `problem` other than those listed.

Data Types: `struct`

## Output Arguments

collapse all

Solution, returned as a real vector. `x` is the vector that minimizes `1/2*x'*H*x + f'*x` subject to all bounds and linear constraints. `x` can be a local minimum for nonconvex problems. For convex problems, `x` is a global minimum. For more information, see Local vs. Global Optima.

Objective function value at the solution, returned as a real scalar. `fval` is the value of `1/2*x'*H*x + f'*x` at the solution `x`.

Reason `quadprog` stopped, returned as an integer described in this table.

 All Algorithms `1` Function converged to the solution `x`. `0` Number of iterations exceeded `options.MaxIterations`. `-2` Problem is infeasible. Or, for `'interior-point-convex'`, the step size was smaller than `options.StepTolerance`, but constraints were not satisfied. `-3` Problem is unbounded. `'interior-point-convex'` Algorithm `2` Step size was smaller than `options.StepTolerance`, constraints were satisfied. `-6` Nonconvex problem detected. `-8` Unable to compute a step direction. `'trust-region-reflective'` Algorithm `4` Local minimum found; minimum is not unique. `3` Change in the objective function value was smaller than `options.FunctionTolerance`. `-4` Current search direction was not a direction of descent. No further progress could be made.

Information about the optimization process, returned as a structure with these fields:

 `iterations` Number of iterations taken `algorithm` Optimization algorithm used `cgiterations` Total number of PCG iterations (`'trust-region-reflective'` algorithm only) `constrviolation` Maximum of constraint functions `firstorderopt` Measure of first-order optimality `linearsolver` Type of internal linear solver, `'dense'` or `'sparse'` (`'interior-point-convex'` algorithm only) `message` Exit message

Lagrange multipliers at the solution, returned as a structure with these fields:

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

For details, see Lagrange Multiplier Structures.

## Algorithms

collapse all

### 'interior-point-convex'

The `'interior-point-convex'` algorithm attempts to follow a path that is strictly inside the constraints. It uses a presolve module to remove redundancies and to simplify the problem by solving for components that are straightforward.

The algorithm has different implementations for a sparse Hessian matrix `H` and for a dense matrix. Generally, the sparse implementation is faster on large, sparse problems, and the dense implementation is faster on dense or small problems. For more information, see interior-point-convex quadprog Algorithm.

### 'trust-region-reflective'

The `'trust-region-reflective'` algorithm 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). For more information, see trust-region-reflective quadprog Algorithm.

## Alternative Functionality

### App

You can use the Optimization app for quadratic programming. Enter `optimtool` at the MATLAB® command line, and choose the ```quadprog - Quadratic programming``` solver. For more information, see Optimization App.

### Problem-Based Approach

You can solve quadratic programming problems using the Problem-Based Optimization Setup. For examples, see Quadratic Programming.

## 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, 1996, pp. 1040–1058.

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

[3] Gould, N., and P. L. Toint. “Preprocessing for quadratic programming.” Mathematical Programming. Series B, Vol. 100, 2004, pp. 95–132.