Table Of Contents
Quadratic Programming (Active Set) (G Dataflow)
Solves the quadratic programming problem using the active set method.
warm start
Boolean value that specifies whether to allow a warm start of the optimization.
| True | Uses the indexes of the active constraints from the previous solution as the initial set of active constraints for the current problem. |
| False | Does not use the indexes of the active constraints from the previous solution as the initial set of active constraints for the current problem. |
Default: False
objective function
Coefficients of the quadratic and linear terms of the objective function 0.5x * Q * x + c * x.
Q
Quadratic term, in the form of a matrix, of the objective function.
c
Linear term, in the form of a vector, of the objective function.
start
Values of the variables at which the optimization starts.
parameter bounds
Upper and lower numeric limits for the parameters being optimized.
minimum
Smallest allowed values of the parameters being optimized.
This input must be empty or the same size as start. This input must be the same size as maximum. This input does not allow exceptional values, such as Inf, -Inf, or NaN.
maximum
Greatest allowed values of the parameters being optimized.
This input must be empty or the same size as start. This input must be the same size as minimum. This input does not allow exceptional values, such as Inf, -Inf, or NaN.
equality constraints
Components of the linear equality constraint equation A * x = b.
A
Matrix term of the linear equality constraint equation.
b
Vector term of the linear equality constraint equation.
inequality constraints
Components of the linear inequality constraint minimum ≤ D * x ≤ maximum.
D
Matrix term of the linear inequality constraint.
minimum
Smallest allowed value of the linear inequality constraint.
maximum
Greatest allowed value of the linear inequality constraint.
error in
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
stopping criteria
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.
function tolerance
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:
where
- fn is the function value of the current iteration
- fn - 1 is the function value of the previous iteration
- ε is the machine epsilon
Default: 1E-08
parameter tolerance
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:
where
- Pn is the parameter value of the current iteration
- Pn - 1 is the parameter value of the previous iteration
- ε is the machine epsilon
Default: 1E+06
gradient tolerance
Minimum 2-norm of the gradient.
Default: 1E+06
maximum iterations
Maximum number of iterations that the node runs in the optimization.
Default: 10000
maximum function calls
Maximum number of calls to the objective function allowed in the optimization.
Default: 10000
maximum time
Maximum amount of time in seconds allowed for the optimization.
Default: -1 — The optimization never times out.
minimum
Values of the variables where the objective function has the local minimum.
f(minimum)
Value of the objective function at minimum.
Lagrangian multipliers
Coefficients of the Lagrangian function that correspond to the equality and inequality constraints.
If the objective function has three equality constraints and two inequality constraints, the first three Lagrangian multipliers correspond to the equality constraints, and the last two Lagrangian multipliers correspond to the inequality constraints.
error out
Error information.
The node produces this output 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.
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported
Web Server: Not supported in VIs that run in a web application