Sequential Unconstrained Minimization Technique¶
Table of contents
Description¶
For a general problem
The Sequential Unconstrained Minimization Technique solves:
The algorithm stops when the error is less than err_tol
, or the total number of ‘generations’ exceeds a desired (or default) value.
Definitions¶
-
bool sumt(ColVec_t &init_out_vals, std::function<fp_t(const ColVec_t &vals_inp, ColVec_t *grad_out, void *opt_data)> opt_objfn, void *opt_data, std::function<ColVec_t(const ColVec_t &vals_inp, Mat_t *jacob_out, void *constr_data)> constr_fn, void *constr_data)¶
Sequential Unconstrained Minimization Technique.
- Parameters
init_out_vals – a column vector of initial values, which will be replaced by the solution upon successful completion of the optimization algorithm.
opt_objfn – the function to be minimized, taking three arguments:
vals_inp
a vector of inputs;grad_out
a vector to store the gradient; andopt_data
additional data passed to the user-provided function.
opt_data – additional data passed to the user-provided function.
constr_fn – the constraint functions, in vector form, taking three arguments.
constr_data – additional data passed to the constraints functions.
- Returns
a boolean value indicating successful completion of the optimization algorithm.
-
bool sumt(ColVec_t &init_out_vals, std::function<fp_t(const ColVec_t &vals_inp, ColVec_t *grad_out, void *opt_data)> opt_objfn, void *opt_data, std::function<ColVec_t(const ColVec_t &vals_inp, Mat_t *jacob_out, void *constr_data)> constr_fn, void *constr_data, algo_settings_t &settings)¶
Sequential Unconstrained Minimization Technique.
- Parameters
init_out_vals – a column vector of initial values, which will be replaced by the solution upon successful completion of the optimization algorithm.
opt_objfn – the function to be minimized, taking three arguments:
vals_inp
a vector of inputs;grad_out
a vector to store the gradient; andopt_data
additional data passed to the user-provided function.
opt_data – additional data passed to the user-provided function.
constr_fn – the constraint functions, in vector form, taking three arguments.
constr_data – additional data passed to the constraints functions.
settings – parameters controlling the optimization routine.
- Returns
a boolean value indicating successful completion of the optimization algorithm.