GRADS – Gradient search optimization
Block SymbolLicensing group: ADVANCED
Function Description
The GRADS block performs one-dimensional optimization of the
function by gradient
method, where is the
optimized variable and
is an arbitrary vector variable. It is assumed that the value of the function
for given x at time
is enumerated and fed
to the f input at time ,
where is the
execution period of the GRADS block. This means that the individual optimization iterations have a
period of .
The length of step of the gradient method is given by
where stands for -th iteration. The step size is restricted to lie within the interval . The value of the optimized variable for the next iteration is given by
Inputs
f | Value of the optimized for given variable x | double |
x0 | Optimization starting point | double |
START | Starting signal (rising edge) | bool |
BRK | Termination signal | bool |
Outputs
x | Current value of the optimized variable | double |
xopt | Resulting optimal value of the x variable | double |
fopt | Resulting optimal value of the function | double |
BSY | Indicator of running optimization | bool |
iter | Number of current iteration | long |
E | Error flag | bool |
iE | Error code | long |
|
|
|
Parameters
xmin | Lower limit for the x variable | double |
xmax | Upper limit for the x variable 10.0 | double |
gamma | Coefficient for determining the step size of the gradient optimization method 0.3 | double |
d0 | Initial step size 0.05 | double |
dmin | Minimum step size 0.01 | double |
dmax | Maximum step size 1.0 | double |
n | Iteration period (in sampling periods ) 100 | long |
itermax | Maximum number of iterations 20 | long |
[Previous] [Back to top] [Up] [Next]