function result_map = ff_ipwkbz_fibs_vf_vecsv(varargin)

%% FF_IPWKBZ_VF_VECSV solve infinite horizon exo shock + endo asset problem

% This is a modified version of

% <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbz/solve/html/ff_ipwkbz_vf_vecsv.html

% ff_ipwkbz_vf_vecsv>, to see how this function solves the formal and

% savings risky and safe asset problem with formal and informal choices,

% compare the code here and from

% <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbz/solve/html/ff_ipwkbz_vf_vecsv.html

% ff_ipwkbz_vf_vecsv> side by side.

%

% @param param_map container parameter container

%

% @param support_map container support container

%

% @param armt_map container container with states, choices and shocks

% grids that are inputs for grid based solution algorithm

%

% @param func_map container container with function handles for

% consumption cash-on-hand etc.

%

% @return result_map container contains policy function matrix, value

% function matrix, iteration results, and policy function, value function

% and iteration results tables.

%

% keys included in result_map:

%

% * mt_val matrix states_n by shock_n matrix of converged value function grid

% * mt_pol_a matrix states_n by shock_n matrix of converged policy function grid

% * ar_val_diff_norm array if bl_post = true it_iter_last by 1 val function

% difference between iteration

% * ar_pol_diff_norm array if bl_post = true it_iter_last by 1 policy

% function difference between iterations

% * mt_pol_perc_change matrix if bl_post = true it_iter_last by shock_n the

% proportion of grid points at which policy function changed between

% current and last iteration for each element of shock

%

% @example

%

% @include

%

% * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_ipwkbz/paramfunc/ff_ipwkbz_evf.m ff_ipwkbz_evf>

% * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_ipwkbz/paramfunc/ffs_ipwkbz_set_default_param.m ffs_ipwkbz_set_default_param>

% * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_ipwkbz/paramfunc/ffs_ipwkbz_get_funcgrid.m ffs_ipwkbz_get_funcgrid>

% * <https://github.com/FanWangEcon/CodeDynaAsset/blob/master/m_akz/solvepost/ff_akz_vf_post.m ff_akz_vf_post>

%

%% Default

% * it_param_set = 1: quick test

% * it_param_set = 2: benchmark run

% * it_param_set = 3: benchmark profile

% * it_param_set = 4: press publish button

it_param_set = 3;

[param_map, support_map] = ffs_ipwkbz_fibs_set_default_param(it_param_set);

% parameters can be set inside ffs_ipwkbz_set_default_param or updated here

% param_map('it_w_perc_n') = 50;

% param_map('it_ak_perc_n') = param_map('it_w_perc_n');

% param_map('it_z_n') = 15;

% param_map('fl_coh_interp_grid_gap') = 0.025;

% param_map('it_c_interp_grid_gap') = 0.001;

% param_map('fl_w_interp_grid_gap') = 0.25;

% param_map('it_w_perc_n') = 100;

% param_map('it_ak_perc_n') = param_map('it_w_perc_n');

% param_map('it_z_n') = 11;

% param_map('fl_coh_interp_grid_gap') = 0.1;

% param_map('it_c_interp_grid_gap') = 10^-4;

% param_map('fl_w_interp_grid_gap') = 0.1;

% get armt and func map

params_len = length(varargin);

if params_len <= 2

    [armt_map, func_map] = ffs_ipwkbz_fibs_get_funcgrid(param_map, support_map); % 1 for override

    default_params = {param_map support_map armt_map func_map};

end

%% Parse Parameters 1

% if varargin only has param_map and support_map,

[default_params{1:params_len}] = varargin{:};

param_map = [param_map; default_params{1}];

support_map = [support_map; default_params{2}];

if params_len >= 1 && params_len <= 2

    % If override param_map, re-generate armt and func if they are not

    % provided

    [armt_map, func_map] = ffs_ipwkbz_fibs_get_funcgrid(param_map, support_map);

else

    % Override all

    armt_map = default_params{3};

    func_map = default_params{4};

end

% append function name

st_func_name = 'ff_ipwkbz_fibs_vf_vecsv';

support_map('st_profile_name_main') = [st_func_name support_map('st_profile_name_main')];

support_map('st_mat_name_main') = [st_func_name support_map('st_mat_name_main')];

support_map('st_img_name_main') = [st_func_name support_map('st_img_name_main')];

%% Parse Parameters 2

% armt_map

params_group = values(armt_map, ...

    {'ar_w_perc', 'ar_w_level_full', 'ar_coh_bridge_perc', 'ar_z'});

[ar_w_perc, ar_w_level_full, ar_coh_bridge_perc, ar_z] = params_group{:};

params_group = values(armt_map, {'ar_interp_c_grid', 'ar_interp_coh_grid', ...

    'ar_a_meshk', 'ar_k_mesha', ...

    'mt_interp_coh_grid_mesh_z', 'mt_z_mesh_coh_interp_grid',...

    'mt_interp_coh_grid_mesh_w_perc',...

    'mt_w_level_neg_mesh_coh_bridge_perc', 'mt_coh_bridge_perc_mesh_w_level_neg',...

    'mt_bl_w_by_interp_coh_interp_grid_wneg', ...

    'mt_w_by_interp_coh_interp_grid_wneg', 'mt_w_by_interp_coh_interp_grid_wpos', 'mt_coh_w_perc_ratio_wneg'});

[ar_interp_c_grid, ar_interp_coh_grid, ar_a_meshk, ar_k_mesha, ...

    mt_interp_coh_grid_mesh_z, mt_z_mesh_coh_interp_grid, ...

    mt_interp_coh_grid_mesh_w_perc,...

    mt_w_level_neg_mesh_coh_bridge_perc, mt_coh_bridge_perc_mesh_w_level_neg, ...

    mt_bl_w_by_interp_coh_interp_grid_wneg, ...

    mt_w_by_interp_coh_interp_grid_wneg, mt_w_by_interp_coh_interp_grid_wpos, mt_coh_w_perc_ratio_wneg] ...

        = params_group{:};

params_group = values(armt_map, {'mt_coh_wkb', 'mt_z_mesh_coh_wkb'});

[mt_coh_wkb, mt_z_mesh_coh_wkb] = params_group{:};

% armt_map

% Formal choice Menu/Grid and Interest Rate Menu/Grid

params_group = values(armt_map, {'ar_forbrblk_r', 'ar_forbrblk'});

[ar_forbrblk_r, ar_forbrblk] = params_group{:};

% func_map

params_group = values(func_map, {'f_util_log', 'f_util_crra', 'f_cons'});

[f_util_log, f_util_crra, f_cons] = params_group{:};

% param_map

params_group = values(param_map, {'it_z_n', 'fl_crra', 'fl_beta', ...

    'fl_nan_replace', 'fl_c_min', 'bl_bridge', 'bl_default', 'fl_default_wprime'});

[it_z_n, fl_crra, fl_beta, fl_nan_replace, fl_c_min, bl_bridge, bl_default, fl_default_wprime] = params_group{:};

params_group = values(param_map, {'it_maxiter_val', 'fl_tol_val', 'fl_tol_pol', 'it_tol_pol_nochange'});

[it_maxiter_val, fl_tol_val, fl_tol_pol, it_tol_pol_nochange] = params_group{:};

% support_map

params_group = values(support_map, {'bl_profile', 'st_profile_path', ...

    'st_profile_prefix', 'st_profile_name_main', 'st_profile_suffix',...

    'bl_time', 'bl_display_defparam', 'bl_graph_evf', 'bl_display', 'it_display_every', 'bl_post'});

[bl_profile, st_profile_path, ...

    st_profile_prefix, st_profile_name_main, st_profile_suffix, ...

    bl_time, bl_display_defparam, bl_graph_evf, bl_display, it_display_every, bl_post] = params_group{:};

params_group = values(support_map, {'it_display_summmat_rowmax', 'it_display_summmat_colmax'});

[it_display_summmat_rowmax, it_display_summmat_colmax] = params_group{:};

%% Initialize Output Matrixes

mt_val_cur = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_val = mt_val_cur - 1;

mt_pol_a = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_pol_a_cur = mt_pol_a - 1;

mt_pol_k = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_pol_k_cur = mt_pol_k - 1;

mt_pol_idx = zeros(length(ar_interp_coh_grid),length(ar_z));

% collect optimal borrowing formal and informal choices

% mt_pol_b_with_r: cost to t+1 consumption from borrowing in t

mt_pol_b_with_r = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_pol_b_bridge = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_pol_inf_borr_nobridge = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_pol_for_borr = zeros(length(ar_interp_coh_grid),length(ar_z));

mt_pol_for_save = zeros(length(ar_interp_coh_grid),length(ar_z));

% We did not need these in ff_oz_vf or ff_oz_vf_vec

% see

% <https://fanwangecon.github.io/M4Econ/support/speed/partupdate/fs_u_c_partrepeat_main.html

% fs_u_c_partrepeat_main> for why store using cells.

cl_u_c_store = cell([it_z_n, 1]);

cl_c_valid_idx = cell([it_z_n, 1]);

cl_w_kstar_interp_z = cell([it_z_n, 1]);

for it_z_i = 1:length(ar_z)

    cl_w_kstar_interp_z{it_z_i} = zeros([length(ar_w_perc), length(ar_interp_coh_grid)]) - 1;

end

%% Initialize Convergence Conditions

bl_vfi_continue = true;

it_iter = 0;

ar_val_diff_norm = zeros([it_maxiter_val, 1]);

ar_pol_diff_norm = zeros([it_maxiter_val, 1]);

mt_pol_perc_change = zeros([it_maxiter_val, it_z_n]);

%% Pre-calculate u(c)

% Interpolation, see

% <https://fanwangecon.github.io/M4Econ/support/speed/partupdate/fs_u_c_partrepeat_main.html

% fs_u_c_partrepeat_main> for why interpolate over u(c)

% Evaluate

if (fl_crra == 1)

    ar_interp_u_of_c_grid = f_util_log(ar_interp_c_grid);

    fl_u_cmin = f_util_log(fl_c_min);

else

    ar_interp_u_of_c_grid = f_util_crra(ar_interp_c_grid);

    fl_u_cmin = f_util_crra(fl_c_min);

end

ar_interp_u_of_c_grid(ar_interp_c_grid <= fl_c_min) = fl_u_cmin;

% Get Interpolant

f_grid_interpolant_spln = griddedInterpolant(ar_interp_c_grid, ar_interp_u_of_c_grid, 'spline', 'nearest');

%% Iterate Value Function

% Loop solution with 4 nested loops

%

% # loop 1: over exogenous states

% # loop 2: over endogenous states

% # loop 3: over choices

% # loop 4: add future utility, integration--loop over future shocks

%

% Start Profile

if (bl_profile)

    close all;

    profile off;

    profile on;

end 

% Start Timer

if (bl_time) 

    tic; 

end 

% Value Function Iteration

while bl_vfi_continue 

    it_iter = it_iter + 1; 

    %% Interpolate (1) reacahble v(coh(k(w,z),b(w,z),z),z) given v(coh, z)

    % This is the same as <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbz/solve/html/ff_ipwkbz_vf_vecsv.html

    % ff_ipwkbz_vf_vecsv>. For the FIBS problem, the cash-on-hand

    % interpolation grid stays the same, and the shock grid stays the same

    % as well. The results will not be the same, for example, the coh_grid

    % max is the max of reachable cash-on-hand levels (min is however just

    % the borrowing bound).

    % Generate Interpolant for v(coh,z)

    f_grid_interpolant_value = griddedInterpolant(... 

        mt_z_mesh_coh_interp_grid', mt_interp_coh_grid_mesh_z', mt_val_cur', 'linear', 'nearest'); 

    % Interpolate for v(coh(k(w,z),b(w,z),z),z)

    mt_val_wkb_interpolated = f_grid_interpolant_value(mt_z_mesh_coh_wkb, mt_coh_wkb); 

    %% Solve Second Stage Problem k*(w,z)

    % This is again the same as <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbz/solve/html/ff_ipwkbz_vf_vecsv.html

    % ff_ipwkbz_vf_vecsv>. But the output matrix sizes are different.

    % Previously, they were (length(ar_w_level)) by (length(ar_z)). Now

    % have this thing which is stored (length(ar_w_level_full)) by

    % (length(ar_z)). _ar_w_level_full_ includes not just different levels

    % of _ar_w_level_, but also repeats the elements of _ar_w_level_ that

    % are < 0 by _it_coh_bridge_perc_n_ times, starting with what

    % corresponds to 100 percent of w should go to cover bridge loan, until

    % 0 percent for w < 0, which then proceeds to w > 0. So the last

    % segment of _ar_w_level_full_ is the same as ar_w_level:

    % ar_w_level_full((end-length(ar_w_level)+1):end) = ar_w_level.

    support_map('bl_graph_evf') = false; 

    if (it_iter == (it_maxiter_val + 1)) 

        support_map('bl_graph_evf') = bl_graph_evf; 

    end 

    bl_input_override = true; 

    [mt_ev_condi_z_max, ~, mt_ev_condi_z_max_kp, ~] = ... 

        ff_ipwkbz_fibs_evf(mt_val_wkb_interpolated, param_map, support_map, armt_map, bl_input_override); 

    %% Solve First Stage Problem w*(z) given k*(w,z)

    % Refer to

    % <https://fanwangecon.github.io/CodeDynaAsset/m_ipwkbz/solve/html/ff_ipwkbz_fibs_vf_vecsv.html

    % ff_ipwkbz_fibs_vf_vecsv> where the problem was solved without formal and

    % informal choices that allow for bridge loans to see line by line how

    % code differ. Some of the comments from that file are not here to save

    % space. Comments here address differences and are specific to formal

    % and informal choices.

    % loop 1: over exogenous states

    for it_z_i = 1:length(ar_z) 

        %% A. Interpolate FULL to get k*(coh_level, w_perc, z), b*(k,w) based on k*(coh_perc, w_level)

        % additionally, Interpolate FULL EV(k*(coh_level, w_perc, z), w -

        % b*|z) based on EV(k*(coh_perc, w_level))

        %

        % we solved the second period problem in ff_ipwkbz_fibs_fibs_evf.m

        % above. To use results, we need to interpolate in the following

        % way to obtain *mt_w_kstar_interp_z* as well as

        % *mt_ev_condi_z_max_interp_z*:

        %

        % # Interp STG1A: for $w > 0$, 1D interpolate over w level, given z

        % # Interp STG1B: for $w < 0$, 2D interpolate over w level and coh

        % perceng, given z

        %

        % 1. Negative Elements of w grid expanded by w percentages for bridge

        ar_bl_w_level_full_neg = (ar_w_level_full < 0); 

        % 2. Current Positve w and negative w optimal k choices

        it_wneg_mt_row = sum(ar_bl_w_level_full_neg)/length(ar_coh_bridge_perc); 

        % for mt_ev_condi_z_max_kp

        ar_ev_condi_z_max_kp_wpos = mt_ev_condi_z_max_kp(~ar_bl_w_level_full_neg, it_z_i)'; 

        ar_ev_condi_z_max_kp_wneg = mt_ev_condi_z_max_kp(ar_bl_w_level_full_neg, it_z_i)'; 

        mt_ev_condi_z_max_kp_wneg = reshape(ar_ev_condi_z_max_kp_wneg, [it_wneg_mt_row, length(ar_coh_bridge_perc)]); 

        % for mt_ev_condi_z_max

        ar_ev_condi_z_max_wpos = mt_ev_condi_z_max(~ar_bl_w_level_full_neg, it_z_i)'; 

        ar_ev_condi_z_max_wneg = mt_ev_condi_z_max(ar_bl_w_level_full_neg, it_z_i)'; 

        mt_ev_condi_z_max_wneg = reshape(ar_ev_condi_z_max_wneg, [it_wneg_mt_row, length(ar_coh_bridge_perc)]); 

        % 2. Interp STG1A for w > 0

        ar_w_level_full_pos = ar_w_level_full(~ar_bl_w_level_full_neg); 

        % Interpolation for mt_ev_condi_z_max_kp

        f_interpolante_w_level_pos_kstar_z = griddedInterpolant(ar_w_level_full_pos, ar_ev_condi_z_max_kp_wpos, 'linear', 'nearest'); 

        mt_w_kstar_interp_z_wpos = f_interpolante_w_level_pos_kstar_z(mt_w_by_interp_coh_interp_grid_wpos(:)); 

        mt_w_astar_interp_z_wpos = mt_w_by_interp_coh_interp_grid_wpos(:) - mt_w_kstar_interp_z_wpos; 

        % Interpolation for mt_ev_condi_z_max

        f_interpolante_w_level_pos_ev_z = griddedInterpolant(ar_w_level_full_pos, ar_ev_condi_z_max_wpos, 'linear', 'nearest'); 

        mt_w_ev_interp_z_wpos = f_interpolante_w_level_pos_ev_z(mt_w_by_interp_coh_interp_grid_wpos(:)); 

        % 3. Interp STG1B for w <= 0

        if (bl_bridge) 

            % Interpolation for mt_ev_condi_z_max_kp

            f_interpolante_w_level_neg_kstar_z = griddedInterpolant(... 

                mt_coh_bridge_perc_mesh_w_level_neg', mt_w_level_neg_mesh_coh_bridge_perc', ... 

                mt_ev_condi_z_max_kp_wneg', 'linear', 'nearest'); 

            mt_w_kstar_interp_z_wneg = f_interpolante_w_level_neg_kstar_z(mt_coh_w_perc_ratio_wneg(:), mt_w_by_interp_coh_interp_grid_wneg(:)); 

            mt_w_astar_interp_z_wneg = mt_w_by_interp_coh_interp_grid_wneg(:) - mt_w_kstar_interp_z_wneg; 

            % Interpolation for mt_ev_condi_z_max

            f_interpolante_w_level_neg_ev_z = griddedInterpolant(... 

                mt_coh_bridge_perc_mesh_w_level_neg', mt_w_level_neg_mesh_coh_bridge_perc', ... 

                mt_ev_condi_z_max_wneg', 'linear', 'nearest'); 

            mt_w_ev_interp_z_wneg = f_interpolante_w_level_neg_ev_z(mt_coh_w_perc_ratio_wneg(:), mt_w_by_interp_coh_interp_grid_wneg(:)); 

        else

            ar_w_level_full_neg = ar_w_level_full(ar_bl_w_level_full_neg);

            % Interpolation for mt_ev_condi_z_max_kp

            f_interpolante_w_level_neg_kstar_z = griddedInterpolant(ar_w_level_full_neg, ar_ev_condi_z_max_kp_wneg, 'linear', 'nearest');

            mt_w_kstar_interp_z_wneg = f_interpolante_w_level_neg_kstar_z(mt_w_by_interp_coh_interp_grid_wneg(:));

            mt_w_astar_interp_z_wneg = mt_w_by_interp_coh_interp_grid_wneg(:) - mt_w_kstar_interp_z_wneg;

            % Interpolation for mt_ev_condi_z_max

            f_interpolante_w_level_neg_ev_z = griddedInterpolant(ar_w_level_full_neg, ar_ev_condi_z_max_wneg, 'linear', 'nearest');

            mt_w_ev_interp_z_wneg = f_interpolante_w_level_neg_ev_z(mt_w_by_interp_coh_interp_grid_wneg(:));

        end 

        % 4. Combine positive and negative aggregate savings matrix

        % check: mt_w_by_interp_coh_interp_grid vs mt_w_astar_interp_z + mt_w_kstar_interp_z

        % combine for mt_ev_condi_z_max_kp

        mt_w_kstar_interp_z = zeros(size(mt_bl_w_by_interp_coh_interp_grid_wneg)); 

        mt_w_kstar_interp_z(~mt_bl_w_by_interp_coh_interp_grid_wneg) = mt_w_kstar_interp_z_wpos; 

        mt_w_kstar_interp_z(mt_bl_w_by_interp_coh_interp_grid_wneg)  = mt_w_kstar_interp_z_wneg; 

        mt_w_astar_interp_z = zeros(size(mt_bl_w_by_interp_coh_interp_grid_wneg)); 

        mt_w_astar_interp_z(~mt_bl_w_by_interp_coh_interp_grid_wneg) = mt_w_astar_interp_z_wpos; 

        mt_w_astar_interp_z(mt_bl_w_by_interp_coh_interp_grid_wneg)  = mt_w_astar_interp_z_wneg; 

        % combine for mt_ev_condi_z_max

        mt_ev_condi_z_max_interp_z = zeros(size(mt_bl_w_by_interp_coh_interp_grid_wneg)); 

        mt_ev_condi_z_max_interp_z(~mt_bl_w_by_interp_coh_interp_grid_wneg) = mt_w_ev_interp_z_wpos; 

        mt_ev_condi_z_max_interp_z(mt_bl_w_by_interp_coh_interp_grid_wneg)  = mt_w_ev_interp_z_wneg; 

        % 5. changes in w_perc kstar choices

        mt_w_kstar_diff_idx = (cl_w_kstar_interp_z{it_z_i} ~= mt_w_kstar_interp_z); 

        %% B. Calculate UPDATE u(c) Update: u(c(coh_level, w_perc)) given k*_interp, b*_interp

        ar_c = f_cons(mt_interp_coh_grid_mesh_w_perc(mt_w_kstar_diff_idx), ... 

                      mt_w_astar_interp_z(mt_w_kstar_diff_idx), ... 

                      mt_w_kstar_interp_z(mt_w_kstar_diff_idx)); 

        ar_it_c_valid_idx = (ar_c <= fl_c_min); 

        % EVAL current utility: N by N, f_util defined earlier

        ar_utility_update = f_grid_interpolant_spln(ar_c); 

        % Update Storage

        if (it_iter == 1) 

            cl_u_c_store{it_z_i} = reshape(ar_utility_update, [length(ar_w_perc), length(ar_interp_coh_grid)]); 

            cl_c_valid_idx{it_z_i} = reshape(ar_it_c_valid_idx, [length(ar_w_perc), length(ar_interp_coh_grid)]); 

        else 

            cl_u_c_store{it_z_i}(mt_w_kstar_diff_idx) = ar_utility_update; 

            cl_c_valid_idx{it_z_i}(mt_w_kstar_diff_idx) = ar_it_c_valid_idx; 

        end 

        cl_w_kstar_interp_z{it_z_i} = mt_w_kstar_interp_z; 

        %% D. Compute FULL U(coh_level, w_perc, z) over all w_perc

        mt_utility = cl_u_c_store{it_z_i} + fl_beta*mt_ev_condi_z_max_interp_z; 

        % Index update

        % using the method below is much faster than index replace

        % see <https://fanwangecon.github.io/M4Econ/support/speed/index/fs_subscript.html fs_subscript>

        mt_it_c_valid_idx = cl_c_valid_idx{it_z_i}; 

        % Default or Not Utility Handling

        if (bl_default) 

            % if default: only today u(cmin), transition out next period, debt wiped out

            fl_v_default = fl_u_cmin + fl_beta*f_interpolante_w_level_pos_ev_z(fl_default_wprime); 

            mt_utility = mt_utility.*(~mt_it_c_valid_idx) + fl_v_default*(mt_it_c_valid_idx); 

        else

            % if default is not allowed: v = u(cmin)

            mt_utility = mt_utility.*(~mt_it_c_valid_idx) + fl_nan_replace*(mt_it_c_valid_idx);

        end 

        % percentage algorithm does not have invalid (check to make sure

        % min percent is not 0 in ffs_ipwkbz_fibs_get_funcgrid.m)

        % mt_utility = mt_utility.*(~mt_it_c_valid_idx) + fl_u_neg_c*(mt_it_c_valid_idx);

        %% E. Optimize Over Choices: max_{w_perc} U(coh_level, w_perc, z)

        % Optimization: remember matlab is column major, rows must be

        % choices, columns must be states

        % <https://en.wikipedia.org/wiki/Row-_and_column-major_order COLUMN-MAJOR>

        [ar_opti_val_z, ar_opti_idx_z] = max(mt_utility); 

        % Generate Linear Opti Index

        [it_choies_n, it_states_n] = size(mt_utility); 

        ar_add_grid = linspace(0, it_choies_n*(it_states_n-1), it_states_n); 

        ar_opti_linear_idx_z = ar_opti_idx_z + ar_add_grid; 

        ar_opti_aprime_z = mt_w_astar_interp_z(ar_opti_linear_idx_z); 

        ar_opti_kprime_z = mt_w_kstar_interp_z(ar_opti_linear_idx_z); 

        ar_opti_c_z = f_cons(ar_interp_coh_grid, ar_opti_aprime_z, ar_opti_kprime_z); 

        % Handle Default is optimal or not

        if (bl_default) 

            % if defaulting is optimal choice, at these states, not required

            % to default, non-default possible, but default could be optimal

            fl_default_opti_kprime = f_interpolante_w_level_pos_kstar_z(fl_default_wprime); 

            ar_opti_aprime_z(ar_opti_c_z <= fl_c_min) = fl_default_wprime - fl_default_opti_kprime; 

            ar_opti_kprime_z(ar_opti_c_z <= fl_c_min) = fl_default_opti_kprime; 

        else

            % if default is not allowed, then next period same state as now

            % this is absorbing state, this is the limiting case, single

            % state space point, lowest a and lowest shock has this.

            ar_opti_aprime_z(ar_opti_c_z <= fl_c_min) = min(ar_a_meshk);

            ar_opti_kprime_z(ar_opti_c_z <= fl_c_min) = min(ar_k_mesha);

        end 

        %% F. Store Results

        mt_val(:,it_z_i) = ar_opti_val_z; 

        mt_pol_a(:,it_z_i) = ar_opti_aprime_z; 

        mt_pol_k(:,it_z_i) = ar_opti_kprime_z; 

        if (it_iter == (it_maxiter_val + 1)) 

            mt_pol_idx(:,it_z_i) = ar_opti_linear_idx_z; 

        end 

    end 

    %% Check Tolerance and Continuation

    % Difference across iterations

    ar_val_diff_norm(it_iter) = norm(mt_val - mt_val_cur); 

    ar_pol_diff_norm(it_iter) = norm(mt_pol_a - mt_pol_a_cur) + norm(mt_pol_k - mt_pol_k_cur); 

    ar_pol_a_perc_change = sum((mt_pol_a ~= mt_pol_a_cur))/(length(ar_interp_coh_grid)); 

    ar_pol_k_perc_change = sum((mt_pol_k ~= mt_pol_k_cur))/(length(ar_interp_coh_grid)); 

    mt_pol_perc_change(it_iter, :) = mean([ar_pol_a_perc_change;ar_pol_k_perc_change]); 

    % Update

    mt_val_cur = mt_val; 

    mt_pol_a_cur = mt_pol_a; 

    mt_pol_k_cur = mt_pol_k; 

    % Print Iteration Results

    if (bl_display && (rem(it_iter, it_display_every)==0)) 

        fprintf('VAL it_iter:%d, fl_diff:%d, fl_diff_pol:%d\n', ...

            it_iter, ar_val_diff_norm(it_iter), ar_pol_diff_norm(it_iter));

        tb_valpol_iter = array2table([mean(mt_val_cur,1);...

                                      mean(mt_pol_a_cur,1); ...

                                      mean(mt_pol_k_cur,1); ...

                                      mt_val_cur(length(ar_interp_coh_grid),:); ...

                                      mt_pol_a_cur(length(ar_interp_coh_grid),:); ...

                                      mt_pol_k_cur(length(ar_interp_coh_grid),:)]);

        tb_valpol_iter.Properties.VariableNames = strcat('z', string((1:size(mt_val_cur,2))));

        tb_valpol_iter.Properties.RowNames = {'mval', 'map', 'mak', 'Hval', 'Hap', 'Hak'};

        disp('mval = mean(mt_val_cur,1), average value over a')

        disp('map  = mean(mt_pol_a_cur,1), average choice over a')

        disp('mkp  = mean(mt_pol_k_cur,1), average choice over k')

        disp('Hval = mt_val_cur(it_ameshk_n,:), highest a state val')

        disp('Hap = mt_pol_a_cur(it_ameshk_n,:), highest a state choice')

        disp('mak = mt_pol_k_cur(it_ameshk_n,:), highest k state choice')

        disp(tb_valpol_iter);

    end

    % Continuation Conditions:

    % 1. if value function convergence criteria reached

    % 2. if policy function variation over iterations is less than

    % threshold

    if (it_iter == (it_maxiter_val + 1)) 

        bl_vfi_continue = false; 

    elseif ((it_iter == it_maxiter_val) || ... 

            (ar_val_diff_norm(it_iter) < fl_tol_val) || ... 

            (sum(ar_pol_diff_norm(max(1, it_iter-it_tol_pol_nochange):it_iter)) < fl_tol_pol)) 

        % Fix to max, run again to save results if needed

        it_iter_last = it_iter; 

        it_iter = it_maxiter_val; 

    end 

end 

%% Process Optimal Choices 1: Formal and Informal Choices

result_map = containers.Map('KeyType','char', 'ValueType','any'); 

result_map('mt_val') = mt_val; 

result_map('mt_pol_idx') = mt_pol_idx; 

% Find optimal Formal Informal Choices. Could have saved earlier, but was

% wasteful of resources

for it_z_i = 1:length(ar_z) 

    for it_coh_interp_j = 1:length(ar_interp_coh_grid) 

        fl_coh = mt_interp_coh_grid_mesh_z(it_coh_interp_j, it_z_i); 

        fl_a_opti = mt_pol_a(it_coh_interp_j, it_z_i); 

        % call formal and informal function.

        [fl_max_c, fl_opti_b_bridge, fl_opti_inf_borr_nobridge, fl_opti_for_borr, fl_opti_for_save] = ... 

            ffs_fibs_min_c_cost_bridge(fl_a_opti, fl_coh, param_map, support_map, armt_map, func_map); 

        % store savings and borrowing formal and inf optimal choices

        mt_pol_b_with_r(it_coh_interp_j,it_z_i) = fl_max_c; 

        mt_pol_b_bridge(it_coh_interp_j,it_z_i) = fl_opti_b_bridge; 

        mt_pol_inf_borr_nobridge(it_coh_interp_j,it_z_i) = fl_opti_inf_borr_nobridge; 

        mt_pol_for_borr(it_coh_interp_j,it_z_i) = fl_opti_for_borr; 

        mt_pol_for_save(it_coh_interp_j,it_z_i) = fl_opti_for_save; 

    end 

end 

%% Process Optimal Choices 2: Store a, k, c, coh Results

%

% # *mt_interp_coh_grid_mesh_z*: Cash-on-hand period _t_.

% # *mt_pol_a*: Safe asset choice, principles only for ipwkbz_fibs

% # *cl_mt_pol_a_principleonly*: mt_pol_a is stored in

% cl_mt_pol_a_principle only. This is a shortcut because we need to keep

% cl_mt_pol_a for mt_pol_b_with_r for the _ds_ code.

% # *cl_mt_pol_a*: stores _mt_pol_b_with_r_ which has principles and

% interest rates, to be used with _ds_ code.

% # *mt_pol_a*: Safe asset choice, principles only for ipwkbz_fibs

% # *cl_mt_pol_k*: Risky asset choice, principles only for ipwkbz_fibs

% # *cl_mt_pol_c*: Consumption in _t_ given choices.

% # *cl_pol_b_with_r*: Consumption cost of _mt_pol_a_ in _t+1_, given the

% formal and informal choices that are optimal to minimize this consumption

% cost.

%

result_map('cl_mt_coh') = {mt_interp_coh_grid_mesh_z, zeros(1)}; 

result_map('cl_mt_pol_k') = {mt_pol_k, zeros(1)}; 

result_map('cl_mt_pol_c') = {f_cons(mt_interp_coh_grid_mesh_z, mt_pol_a, mt_pol_k), zeros(1)}; 

result_map('cl_mt_pol_a') = {mt_pol_b_with_r, zeros(1)}; 

result_map('cl_mt_pol_a_principleonly') = {mt_pol_a, zeros(1)}; 

%% Process Optimal Choices 3: Store Formal and Informal Choices

result_map('cl_mt_pol_b_bridge') = {mt_pol_b_bridge, zeros(1)}; 

result_map('cl_mt_pol_inf_borr_nobridge') = {mt_pol_inf_borr_nobridge, zeros(1)}; 

result_map('cl_mt_pol_for_borr') = {mt_pol_for_borr, zeros(1)}; 

result_map('cl_mt_pol_for_save') = {mt_pol_for_save, zeros(1)}; 

%% Process Optimal Choices 4: List of Variable Names to be processed by distributional codes

% this list is needed for the ds codes to generate distribution,

% distributional statistcs will be computed for elements in the list here.

result_map('ar_st_pol_names') = ... 

    ["cl_mt_coh", "cl_mt_pol_a", "cl_mt_pol_k", "cl_mt_pol_c", "cl_mt_pol_a_principleonly", ...

    "cl_mt_pol_b_bridge", "cl_mt_pol_inf_borr_nobridge", "cl_mt_pol_for_borr", "cl_mt_pol_for_save"];

% Get Discrete Choice Outcomes

result_map = ffs_fibs_identify_discrete(result_map, bl_input_override); 

%% End Timer and Profile

% End Timer

if (bl_time) 

    toc; 

end 

% End Profile

if (bl_profile) 

    profile off 

    profile viewer

    st_file_name = [st_profile_prefix st_profile_name_main st_profile_suffix];

    profsave(profile('info'), strcat(st_profile_path, st_file_name));

end

%% Post Solution Graph and Table Generation

if (bl_post)

    bl_input_override = true;

    result_map('ar_val_diff_norm') = ar_val_diff_norm(1:it_iter_last);

    result_map('ar_pol_diff_norm') = ar_pol_diff_norm(1:it_iter_last);

    result_map('mt_pol_perc_change') = mt_pol_perc_change(1:it_iter_last, :);

    armt_map('mt_coh_wkb_ori') = mt_coh_wkb;

    armt_map('ar_a_meshk_ori') = ar_a_meshk;

    armt_map('ar_k_mesha_ori') = ar_k_mesha;

    armt_map('mt_coh_wkb') = mt_interp_coh_grid_mesh_z;

    armt_map('it_ameshk_n') = length(ar_interp_coh_grid);

    armt_map('ar_a_meshk') = mt_interp_coh_grid_mesh_z(:,1);

    armt_map('ar_k_mesha') = zeros(size(mt_interp_coh_grid_mesh_z(:,1)) + 0);

    % Standard AZ graphs

    result_map = ff_akz_vf_post(param_map, support_map, armt_map, func_map, result_map, bl_input_override);

    % Graphs for results_map with FIBS contents

    armt_map('ar_a') = ar_interp_coh_grid;

    result_map = ff_az_fibs_vf_post(param_map, support_map, armt_map, func_map, result_map, bl_input_override);

end

%% Display Various Containers

if (bl_display_defparam)

    %% Display 1 support_map    

    fft_container_map_display(support_map, it_display_summmat_rowmax, it_display_summmat_colmax);

    %% Display 2 armt_map

    fft_container_map_display(armt_map, it_display_summmat_rowmax, it_display_summmat_colmax);

    %% Display 3 param_map

    fft_container_map_display(param_map, it_display_summmat_rowmax, it_display_summmat_colmax);

    %% Display 4 func_map

    fft_container_map_display(func_map, it_display_summmat_rowmax, it_display_summmat_colmax);

    %% Display 5 result_map

    fft_container_map_display(result_map, it_display_summmat_rowmax, it_display_summmat_colmax);

end

end