Version:

Last Modified: January 12, 2018

Finds a local minimum of a given *n*-dimension function with constraints.

This node uses the sequential quadratic programming method to determine the local minimum.

Arbitrary values passed to the strictly typed VI reference.

Strictly typed reference to the VI that implements the objective function(s), the equality constraints function, and the inequality constraints function as separate outputs.

Values of the variables at which the optimization starts.

Upper and lower numeric limits for the parameters being optimized and the inequality constraints.

Smallest allowed values of the parameters being optimized.

The number of elements in **x minimum** can be 0 or the same as **start**. **x minimum** must contain the same number of elements as **x maximum**. This input does not allow exceptional values, such as Inf, -Inf, or NaN.

Greatest allowed values of the parameters being optimized.

The number of elements in **x maximum** can be 0 or the same as **start**. **x maximum** must contain the same number of elements as **x minimum**. This input does not allow exceptional values, such as Inf, -Inf, or NaN.

Smallest allowed values of the inequality constraints.

The length of this input must match the length of the inequality constraints returned from **objective and constraint function**. This input does not allow exceptional values, such as Inf, -Inf, or NaN.

Greatest allowed values of the inequality constraints.

The length of this input must match the length of the inequality constraints returned from **objective and constraint function**. This input does not allow exceptional values, such as Inf, -Inf, or NaN.

Initial values of the inequality constraint function, the Lagrangian multipliers, and the Hessian.

**beginning state** is typically the **ending state** of a previous call to this node and allows a warm start of the optimization.

Initial values of the inequality constraint functions.

Initial values of the Lagrangian multipliers.

Initial estimate values of the Hessian.

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an **error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

**Default: **No error

Tolerance and termination settings.

Relative weighting of the constraints as compared to the objective function and distance that the optimization search travels outside the feasible range to seek an optimal solution.

A value greater than 1 forces the search to stay close to the known feasible range. A value equal to or smaller than 1 allows a broader range of searching.

**Default: **1

Upper bound on the number of iterations allowed to solve the quadratic problem.

If the constraint functions in the quadratic problem are highly nonlinear, you can force the node to update the linear approximation more often by limiting the number of maximum minor iterations so that the linearized constraints are more accurate.

**Default: **10

Conditions that terminate the optimization.

This node terminates the optimization if this node reaches all the tolerance thresholds or passes any of the maximum thresholds.

Minimum relative change in function values between two internal iterations.

Definition of Relative Change in Function Values

The relative change in function values between two internal iterations is defined as follows:

$\frac{\mathrm{abs}({f}_{n}-{f}_{n-1})}{\mathrm{abs}\left({f}_{n}\right)+\epsilon}$

where

*f*_{n}is the function value of the current iteration*f*_{n - 1}is the function value of the previous iteration- ε is the machine epsilon

**Default: **1E-06

Minimum relative change in parameter values between two internal iterations.

Definition of Relative Change in Parameter Values

The relative change in parameter values between two internal iterations is defined as follows:

$\frac{\mathrm{abs}({P}_{n}-{P}_{n-1})}{\mathrm{abs}\left({P}_{n}\right)+\epsilon}$

where

*P*_{n}is the parameter value of the current iteration*P*_{n - 1}is the parameter value of the previous iteration- ε is the machine epsilon

**Default: **1E-06

Minimum 2-norm of the gradient.

**Default: **1E-06

Maximum number of iterations that the node runs in the optimization.

**Default: **100

Maximum number of calls to the objective function allowed in the optimization.

**Default: **1000

Maximum amount of time in seconds allowed for the optimization.

**Default: ** -1 — The optimization never times out.

Values of the variables where the objective function has the local minimum.

Value of the objective function at **minimum**.

Final values of the inequality constraint function, the Lagrange multipliers, and the Hessian.

Values of the inequality constraint functions at the end of the optimization.

Values of the Lagrangian multipliers at the end of the optimization.

Estimated values of the Hessian at the end of the optimization.

Number of times that this node called the objective function(s) in the optimization.

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

**error in** input and an **error out** output so that the node can respond to and communicate errors that occur while code is running. The value of **error in** specifies whether an error occurred before the node runs. Most nodes respond to values of **error in** in a standard, predictable way.

**Where This Node Can Run: **

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application