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**Owning Palette:** Optimization VIs

**Requires:** Full Development System

Solves a general nonlinear optimization problem with nonlinear equality constraint and nonlinear inequality constraint bounds using a sequential quadratic programming method.

function data contains static data that the user-defined function needs at run time. | |||||||||||||

objective and constraint function is a reference to the VI that implements the nonlinear function to minimize, the nonlinear equality constraints function, and the nonlinear inequality constraints function as separate outputs. The objective function output must not be empty. The constraint functions are optional. Therefore, the equality constraints output, the inequality constraints output, or both can be empty. Create this VI by starting from the VI template located in labview\vi.lib\gmath\NumericalOptimization\cno_objective function template.vit. | |||||||||||||

start are the points in n dimension at which the optimization process starts. | |||||||||||||

bounds is a cluster that contains the upper and lower numeric limits for the parameters being optimized and the inequality constraints.
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beginning state contains the initial values of the inequality constraint function, the Lagrangian multipliers, and the Hessian. beginning state is typically the ending state of a previous optimization and allows a warm start of the optimization.
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error in describes error conditions that occur before this node runs. This input provides standard error in functionality. | |||||||||||||

cno settings contains additional tolerance and termination settings that are specific to this algorithm.
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stopping criteria is the collection of conditions that terminate the optimization. If (function tolerance AND parameter tolerance AND gradient tolerance) OR max iterations OR max function calls then optimization terminates.
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number of function calls is the number of times the objective function calls in the optimization process. If max function calls is -1, number of function calls equals population size * (max iterations + 1).
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minimum is the determined local minimum in n dimension. | |||||||||||||

f(minimum) is the function value of objective function at the determined minimum. | |||||||||||||

ending state contains the final values of the inequality constraint function, the Lagrange multipliers, and the Hessian.
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error out contains error information. This output provides standard error out functionality. |

Refer to the Nonlinear Spring Constant fit VI in the labview\examples\Mathematics\Fitting directory for an example of using the Constrained Nonlinear Optimization VI.

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