slsqp_module Module

number of optimization variables ($n > 0$)

number of constraints ($m \ge 0$)

number of equality constraints ($m \ge m_{eq} \ge 0$)

maximum number of iterations

accuracy tolerance

accuracy tolerance over f: if $|f| < tolf$ then stop

accuracy tolerance over df: if $|f_{n+1} - f_n| < toldf$ then stop. It's different from acc in the case of positive derivative

accuracy tolerance over dx: if $|x_{n+1} - x_n| < toldx$ then stop

how the gradients are computed:

perturbation step size to approximate gradients by finite differences (gradient_mode 1-3).

min $\alpha$ for line search $0 < \alpha_{min} < \alpha_{max} \le 1$

max $\alpha$ for line search $0 < \alpha_{min} < \alpha_{max} \le 1$

unit number of status printing (0 for no printing)

lower bound on x

upper bound on x

size of w

real work array

problem function subroutine

gradient subroutine

for reporting an iteration

linesearch mode:

data formerly within linmin. Only used when linesearch_mode=2

data formerly within slsqpb.

Which NNLS method to use:

max iterations in the least squares problem. if <=0, defaults to 3*n. (use by either nnls or bvls)

if the abort method has been called to stop the iterations

"infinity" for the upper and lower bounds. if xl<=-infinite_bound or xu>=infinite_bound then these bounds are considered nonexistant.

for reporting messages to the user

the number of variables, $n \ge 1$

total number of constraints, $m \ge 0$

number of equality constraints, $m_{eq} \ge 0$

maximum number of iterations

accuracy

problem function

function to compute gradients (must be associated if gradient_mode=0)

lower bounds on x. xl(i)=NaN (or xl(i)<=-infinite_bound) indicates to ignore ith bound

upper bounds on x. xu(i)=NaN (or xu(i)>=infinite_bound) indicates to ignore ith bound

will be false if there were errors

1 = inexact (default), 2 = exact

unit number of status messages (default=output_unit)

user-defined procedure that will be called once per iteration

minimum alpha for linesearch [default 0.1]

maximum alpha for linesearch [default 1.0]

how the gradients are to be computed:

perturbation step size (>epsilon) to compute the approximated gradient by finite differences (gradient_mode 1-3). note that this is an absolute step that does not respect the xl or xu variable bounds.

stopping criterion if $|f| < tolf$ then stop.

stopping criterion if $|f_{n+1} - f_n| < toldf$ then stop

stopping criterion if $||x_{n+1} - x_n|| < toldx$ then stop

maximum number of iterations in the nnls problem

Which NNLS method to use:

"infinity" for the upper and lower bounds. if xl<=-infinite_bound or xu>=infinite_bound then these bounds are considered nonexistant. If not present then huge() is used for this.

in: initial optimization variables, out: solution.

status code (see mode in slsqp).

number of iterations

string status message corresponding to istat

the message to report.

optional integer to print after the message.

optional real to print after the message.

if True, then the program is stopped (default=False).