optimization.sensitivities

optimization.sensitivities.location_post_process(p, b, flux, BW, storm, depths, locs, most_common_wave, X_opts, obj_opts, flags)

Function location_post_process()

Parameters:

• p – Parameter struct

• b – Design variable bounds struct

• flux – flux

• BW – BW

• storm – storm

• depths – depths

• locs – locs

• most_common_wave – most_common_wave

• X_opts – X_opts

• obj_opts – obj_opts

• flags – Optimization output flags

Returns:

tab

Returns:

pct_diff

Returns:

location_flags

Returns:

tablatex

optimization.sensitivities.local_sens_both_obj_all_param(x0s, J0, p, params, p_val, param_idxs, lambdas, grads, hesses, num_constr)

Function local_sens_both_obj_all_param()

Parameters:

• x0s – x0s

• J0 – J0

• p – Parameter struct

• params – params

• p_val – p_val

• param_idxs – param_idxs

• lambdas – lambdas

• grads – grads

• hesses – hesses

• num_constr – num_constr

Returns:

par_x_star_par_p_norm

Returns:

dJstar_dp_norm

Returns:

dJdp_norm

Returns:

par_J_par_p_norm

Returns:

$\delta$ p norm

optimization.sensitivities.gradient_mult_x0(p, b)

Function gradient_mult_x0()

Parameters:

• p – Parameter struct

• b – Design variable bounds struct

Returns:

Optimal design variables

Returns:

objs

Returns:

Optimization output flags

Returns:

x0s

Returns:

num_runs

optimization.sensitivities.random_x0(b)

Function random_x0()

Parameters:

b – Design variable bounds struct

Returns:

Initial design variable vector

Returns:

x0_struct

optimization.sensitivities.param_sweep(filename_uuid)

Function param_sweep()

Parameters:

filename_uuid – filename_uuid

Returns:

ratios

Returns:

Levelized cost of energy ($/kWh)

Returns:

LCOE_nom

Returns:

P_var

Returns:

P_var_nom

Returns:

param_names

Returns:

num_DVs

Returns:

X_LCOE

Returns:

X_LCOE_nom

Returns:

dvar_names

Returns:

X_Pvar

Returns:

X_Pvar_nom

Returns:

slope_LCOE_norm

Returns:

slope_Pvar_norm

Returns:

slope_X_LCOE_norm

Returns:

slope_X_Pvar_norm

Returns:

wave energy period (s)

Returns:

par_J_par_p_post_optim

Returns:

dJ_star_dp_quad_post_optim

Returns:

dJ_star_dp_lin_post_optim

Returns:

dJstar_dp_re_optim

Returns:

par_x_star_par_p_post_optim

Returns:

par_x_star_par_p_re_optim

Returns:

$\delta$ p change activity post optim

Returns:

$\delta$ p change activity re optim

Returns:

runtime_post_optim

Returns:

runtime_re_optim

optimization.sensitivities.delta_x(X, grad, hess, J, p, b, which_obj)

Function delta_x()

param X:

Design variable vector

param grad:

grad

param hess:

hess

param J:

Objective function value

param p:

Parameter struct

param b:

Design variable bounds struct

param which_obj:

which_obj

returns:

f

DELTA_X Use grad to estimate delta J for given delta x

optimization.sensitivities.model_sens

[X_opt, ~, ~, ~, ~, ~, ~, val] = :func:`gradient_optim`(x0_input,p,b,1);

optimization.sensitivities.location_sensitivity(p, b)

Function location_sensitivity()

Parameters:

• p – Parameter struct

• b – Design variable bounds struct

Returns:

flux

Returns:

BW

Returns:

storm

Returns:

depths

Returns:

locs

Returns:

most_common_wave

Returns:

X_opts

Returns:

obj_opts

Returns:

Optimization output flags

Returns:

Figure handles