function result_map = ff_abz_fibs_vf_vec(varargin)

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

% The model could be invoked mainly in sveral ways:

%

% # param_map('bl_default') = true;  param_map('bl_bridge') = false;

% param_map('bl_rollover') = true; Given these, default is possible, bridge

% loans are not needed because rollover is allowed for formal loans (or

% informal loans)

% # we change param_map('bl_bridge') = true, that means

% rollover is still allowed, but only allowed using informal sources,

% formal loans no longer allow for roll-over. Furthermore, if both

% bl_bridge and bl_rollover are false, that means we are not allowing for

% rollover at all, so households can not borrow such that they end up with

% negative cash-on-hand.

%

% Default simulation bl_bridge = false.

%

% @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

%

%    % Get Default Parameters

%    it_param_set = 4;

%    [param_map, support_map] = ffs_abz_fibs_set_default_param(it_param_set);

%    % Chnage param_map keys for borrowing

%    param_map('fl_b_bd') = -20; % borrow bound

%    param_map('bl_default') = false; % true if allow for default

%    param_map('fl_c_min') = 0.0001; % u(c_min) when default

%    % Change Keys in param_map

%    param_map('it_a_n') = 500;

%    param_map('it_z_n') = 11;

%    param_map('fl_a_max') = 100;

%    param_map('fl_w') = 1.3;

%    % Change Keys support_map

%    support_map('bl_display') = false;

%    support_map('bl_post') = true;

%    support_map('bl_display_final') = false;

%    % Call Program with external parameters that override defaults.

%    ff_abz_fibs_vf_vec(param_map, support_map);

%

% @include

%

% * <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/m_abz_paramfunc/html/ffs_abz_fibs_set_default_param.html ffs_abz_fibs_set_default_param>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/m_abz_paramfunc/html/ffs_abz_fibs_get_funcgrid.html ffs_abz_fibs_get_funcgrid>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/paramfunc_fibs/html/ffs_fibs_min_c_cost_bridge.html ffs_fibs_min_c_cost_bridge>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/paramfunc_fibs/html/ffs_fibs_inf_bridge.html ffs_fibs_inf_bridge>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/paramfunc_fibs/html/ffs_fibs_min_c_cost.html ffs_fibs_min_c_cost>

% * <https://fanwangecon.github.io/CodeDynaAsset/m_az/solvepost/html/ff_az_vf_post.html ff_az_vf_post>

%

% @seealso

%

% * for/inf + save + borr loop: <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/m_abz_solve/html/ff_abz_fibs_vf.html ff_abz_fibs_vf>

% * for/inf + borr vectorized: <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/m_abz_solve/html/ff_abz_fibs_vf_vec.html ff_abz_fibs_vf_vec>

% * for/inf + borr optimized-vectorized: <https://fanwangecon.github.io/CodeDynaAsset/m_fibs/m_abz_solve/html/ff_abz_fibs_vf_vecsv.html ff_abz_fibs_vf_vecsv>

%

%% 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;

bl_input_override = true;

[param_map, support_map] = ffs_abz_fibs_set_default_param(it_param_set);

% Note: param_map and support_map can be adjusted here or outside to override defaults

% To generate results as if formal informal do not matter

% param_map('fl_r_fsv') = 0.025;

% param_map('fl_r_inf') = 0.035;

% param_map('fl_r_inf_bridge') = 0.035;

% param_map('fl_r_fbr') = 0.035;

% param_map('bl_b_is_principle') = false;

% param_map('st_forbrblk_type') = 'seg3';

% param_map('fl_forbrblk_brmost') = -19;

% param_map('fl_forbrblk_brleast') = -1;

% param_map('fl_forbrblk_gap') = -1.5;

% param_map('bl_b_is_principle') = false;

% param_map('it_a_n') = 750;

% param_map('it_z_n') = 15;

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

default_params = {param_map support_map armt_map func_map};

%% Parse Parameters 1

% if varargin only has param_map and support_map,

params_len = length(varargin);

[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

    bl_input_override = true;

    [armt_map, func_map] = ffs_abz_fibs_get_funcgrid(param_map, support_map, bl_input_override);

else

    % Override all

    armt_map = [armt_map; default_params{3}];

    func_map = [func_map; default_params{4}];

end

% append function name

st_func_name = 'ff_abz_fibs_vf_vec';

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_a', 'mt_z_trans', 'ar_z'});

[ar_a, mt_z_trans, ar_z] = params_group{:};

% 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_coh', 'f_cons_coh_fbis', 'f_cons_coh_save'});

[f_util_log, f_util_crra, f_coh, f_cons_coh_fbis, f_cons_coh_save] = params_group{:};

% param_map

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

    'fl_nan_replace', 'bl_default', 'bl_bridge', 'bl_rollover', 'fl_default_aprime'});

[it_a_n, it_z_n, fl_crra, fl_beta, fl_c_min, ...

    fl_nan_replace, bl_default, bl_bridge, bl_rollover, fl_default_aprime] = 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{:};

% param_map, Formal informal

params_group = values(param_map, {'fl_r_inf', 'fl_r_fsv', 'bl_b_is_principle'});

[fl_r_inf, fl_r_fsv, bl_b_is_principle] = 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_display_minccost', 'bl_display_infbridge', ...

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

[bl_profile, st_profile_path, ...

    st_profile_prefix, st_profile_name_main, st_profile_suffix, ...

    bl_display_minccost, bl_display_infbridge, ...

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

%% Initialize Output Matrixes

% include mt_pol_idx which we did not have in looped code

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

mt_val = mt_val_cur - 1;

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

mt_pol_a_cur = mt_pol_a - 1;

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

mt_pol_cons = zeros(length(ar_a),length(ar_z));

% collect optimal borrowing formal and informal choices

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

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

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

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

%% 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]);

%% 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; 

    %% Solve Optimization Problem Current Iteration

    % loop 1: over exogenous states

    for it_z_i = 1:length(ar_z) 

        %% Solve the Formal Informal Problem for each a' and coh: c_forinf(a')

        % find the today's consumption maximizing formal and informal

        % choices given a' and coh. The formal and informal choices need to

        % generate exactly a', but depending on which formal and informal

        % joint choice is used, the consumption cost today a' is different.

        % Note here, a is principle + interests. Three areas:

        %

        % * *CASE A* a' > 0: savings, do not need to optimize over formal and

        % informal choices

        % * *CASE B* a' < 0 & coh < 0: need bridge loan to pay for unpaid debt, and

        % borrowing over-all, need to first pick bridge loan to pay for

        % debt, if bridge loan is insufficient, go into default. After

        % bridge loan, optimize over formal+informal, borrow+save joint

        % choices.

        % * *CASE C* a' < 0 & coh > 0: do not need to get informal bridge loans,

        % optimize over for+inf save, for+save+borr, inf+borr only, for

        % borrow only.

        %

        % 1. Current Shock

        fl_z = ar_z(it_z_i); 

        % 2. cash-on-hand

        ar_coh = f_coh(fl_z, ar_a); 

        % 3. *CASE A* initiate consumption matrix as if all save

        mt_c = f_cons_coh_save(ar_coh, ar_a'); 

        % 3. if Bridge Loan is Needed

        % 4. *CASE B+C* get negative coh index and get borrowing choices index

        ar_coh_neg_idx = (ar_coh <= 0); 

        ar_a_neg_idx = (ar_a < 0); 

        ar_coh_neg = ar_coh(ar_coh_neg_idx); 

        ar_a_neg = ar_a(ar_a_neg_idx); 

        % 5. if coh > 0 and ap < 0, can allow same for+inf result to all coh.

        % The procedure below works regardless of how ar_coh is sorted. get

        % the index of all negative coh elements as well as first

        % non-negative element. We solve the formal and informal problem at

        % these points, note that we only need to solve the formal and

        % informal problem for positive coh level once.

        ar_coh_first_pos_idx = (cumsum(ar_coh_neg_idx == 0) == 1); 

        ar_coh_forinfsolve_idx = (ar_coh_first_pos_idx | ar_coh_neg_idx); 

        ar_coh_forinfsolve_a_neg_idx = (ar_coh(ar_coh_forinfsolve_idx) <= 0); 

        % 6. *CASE B + C* Negative asset choices (borrowing), 1 col Case C

        % negp1: negative coh + 1, 1 meaning 1 positive coh, first positive

        % coh column index element grabbed.

        mt_coh_negp1_mesh_neg_aprime = zeros(size(ar_a_neg')) + ar_coh(ar_coh_forinfsolve_idx); 

        mt_neg_aprime_mesh_coh_negp1 = zeros(size(mt_coh_negp1_mesh_neg_aprime)) + ar_a_neg'; 

        if (bl_bridge) 

            %         mt_neg_aprime_mesh_coh_1col4poscoh = zeros([length(ar_a_neg), (length(ar_coh_neg)+1)]) + ar_a_neg';

            %         ar_coh_neg_idx_1col4poscoh = ar_coh_neg_idx(1:(length(ar_coh_neg)+1));

            % 6. *CASE B* Solve for: if (fl_ap < 0) and if (fl_coh < 0)

            [mt_aprime_nobridge_negcoh, ~, mt_c_bridge_negcoh] = ffs_fibs_inf_bridge(... 

                bl_b_is_principle, fl_r_inf, ... 

                mt_neg_aprime_mesh_coh_negp1(:,ar_coh_forinfsolve_a_neg_idx), ... 

                mt_coh_negp1_mesh_neg_aprime(:,ar_coh_forinfsolve_a_neg_idx), ... 

                bl_display_infbridge, bl_input_override); 

            % generate mt_aprime_nobridge

            mt_neg_aprime_mesh_coh_negp1(:, ar_coh_forinfsolve_a_neg_idx) = mt_aprime_nobridge_negcoh; 

        else

            % no bridge loan needed means roll over is allowed.

            mt_neg_aprime_mesh_coh_negp1 = ar_a_neg';

        end 

        % 7. *CASE B + C* formal and informal joint choices, 1 col Case C

        bl_input_override = true; 

        [ar_max_c_nobridge, ~, ~, ~] = ... 

            ffs_fibs_min_c_cost(...

            bl_b_is_principle, fl_r_inf, fl_r_fsv, ... 

            ar_forbrblk_r, ar_forbrblk, ... 

            mt_neg_aprime_mesh_coh_negp1(:), ... 

            bl_display_minccost, bl_input_override); 

        %% Update Consumption Matrix *CASE A + B + C* Consumptions

        % Current mt_c is assuming all to be case A

        %

        % * Update Columns for case B (negative coh)

        % * Update Columns for case C (1 column): ar_coh_first_pos_idx,

        % included in ar_coh_forinfsolve_idx

        % * Update Columns for all case C: ~ar_coh_neg_idx using 1 column

        % result

        %

        % 1. Initalize all Neg Aprime consumption cost of aprime inputs

        % Initialize

        mt_max_c_nobridge_a_neg = zeros([length(ar_a_neg), length(ar_coh)]) + 0; 

        mt_c_bridge_coh_a_neg = zeros(size(mt_max_c_nobridge_a_neg)) + 0; 

        % 2. Fill in *Case B* and *Case C* (one column) Other C-cost

        mt_max_c_nobridge_negcohp1 = reshape(ar_max_c_nobridge, [size(mt_neg_aprime_mesh_coh_negp1)]); 

        if (bl_bridge) 

            % 2. Fill in *Case B* Bridge C-cost

            mt_c_bridge_coh_a_neg(:, ar_coh_neg_idx) = mt_c_bridge_negcoh; 

            % 2. Fill in *Case B* and *Case C* (one column) Other C-cost

            mt_max_c_nobridge_a_neg(:, ar_coh_forinfsolve_idx) = mt_max_c_nobridge_negcohp1; 

            mt_max_c_nobridge_a_neg(:, ~ar_coh_forinfsolve_idx) = ... 

                zeros(size(mt_c(ar_a_neg_idx, ~ar_coh_forinfsolve_idx))) ... 

                + mt_max_c_nobridge_negcohp1(:, ~ar_coh_forinfsolve_a_neg_idx); 

        else

            mt_max_c_nobridge_a_neg = zeros([length(ar_a_neg), length(ar_coh)]) + mt_max_c_nobridge_negcohp1;

        end 

        % 3. Consumption for B + C Cases

        % note, the c cost of aprime is the same for all coh > 0, but mt_c

        % is different still for each coh and aprime.

        mt_c_forinfsolve = f_cons_coh_fbis(ar_coh, mt_c_bridge_coh_a_neg + mt_max_c_nobridge_a_neg); 

        % 4. Update with Case B and C

        mt_c(ar_a_neg_idx, :) = mt_c_forinfsolve; 

        %% Solve Optimization Problem: max_{a'} (u(c_forinf(a')) + EV(a',z'))

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

        if (fl_crra == 1) 

            mt_utility = f_util_log(mt_c);

            fl_u_cmin = f_util_log(fl_c_min);

        else 

            mt_utility = f_util_crra(mt_c); 

            fl_u_cmin = f_util_crra(fl_c_min); 

        end 

        % 2. f(z'|z)

        ar_z_trans_condi = mt_z_trans(it_z_i,:); 

        % 3. EVAL EV((A',K'),Z'|Z) = V((A',K'),Z') x p(z'|z)', (N by Z) x (Z by 1) = N by 1

        % Note: transpose ar_z_trans_condi from 1 by Z to Z by 1

        % Note: matrix multiply not dot multiply

        mt_evzp_condi_z = mt_val_cur * ar_z_trans_condi'; 

        % 4. EVAL add on future utility, N by N + N by 1, broadcast again

        mt_utility = mt_utility + fl_beta*mt_evzp_condi_z; 

        if (bl_default) 

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

            mt_utility(mt_c <= fl_c_min) = fl_u_cmin + fl_beta*mt_evzp_condi_z(ar_a == fl_default_aprime); 

        else

            % if default is not allowed: v = fl_nan_replace

            mt_utility(mt_c <= fl_c_min) = fl_nan_replace;

        end 

        % Set below threshold c to c_min

        mt_c(mt_c < fl_c_min) = fl_c_min; 

        % 5. no bridge and no rollover allowed

        if( ~bl_rollover && ~bl_bridge) 

            if (bl_default)

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

                mt_utility(:, ar_coh_neg_idx) = fl_u_cmin + fl_beta*mt_evzp_condi_z(ar_a == fl_default_aprime);

            else

                % if default is not allowed: v = fl_nan_replace

                mt_utility(:, ar_coh_neg_idx) = fl_nan_replace;

            end

        end

        % 5. 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>

        % mt_utility is N by N, rows are choices, cols are states.

        [ar_opti_val_z, ar_opti_idx_z] = max(mt_utility); 

        [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 = ar_a(ar_opti_idx_z); 

        ar_opti_c_z = mt_c(ar_opti_linear_idx_z); 

        % 6. 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

            ar_opti_aprime_z(ar_opti_c_z <= fl_c_min) = fl_default_aprime; 

            ar_opti_idx_z(ar_opti_c_z <= fl_c_min) = find(ar_a == fl_default_aprime); 

        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);

        end 

        % 6. no bridge and no rollover allowed

        if( ~bl_rollover && ~bl_bridge) 

            if (bl_default)

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

                ar_opti_aprime_z(ar_coh_neg_idx) = fl_default_aprime;

            else

                % if default is not allowed: v = fl_nan_replace

                ar_opti_aprime_z(ar_coh_neg_idx) = ar_a(fl_nan_replace);

            end

        end

        %% Store Optimal Choices Current Iteration

        mt_val(:,it_z_i) = ar_opti_val_z; 

        mt_pol_a(:,it_z_i) = ar_opti_aprime_z; 

        mt_pol_cons(:,it_z_i) = ar_opti_c_z; 

        if (it_iter == (it_maxiter_val + 1)) 

            mt_pol_idx(:,it_z_i) = ar_opti_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); 

    mt_pol_perc_change(it_iter, :) = sum((mt_pol_a ~= mt_pol_a_cur))/(it_a_n); 

    % Update

    mt_val_cur = mt_val; 

    mt_pol_a_cur = mt_pol_a; 

    % 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); ...

            mt_val_cur(it_a_n,:); mt_pol_a_cur(it_a_n,:)]);

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

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

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

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

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

        disp('Hap = mt_pol_a_cur(it_a_n,:), highest a 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 

% 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

%% Process Optimal 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_a_j = 1:length(ar_a)

        fl_z = ar_z(it_z_i);

        fl_a = ar_a(it_a_j);

        fl_coh = f_coh(fl_z, fl_a);

        fl_a_opti = mt_pol_a(it_a_j, it_z_i);

        % call formal and informal function.

        [~, 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, bl_input_override);

        % store savings and borrowing formal and inf optimal choices

        mt_pol_b_bridge(it_a_j,it_z_i) = fl_opti_b_bridge;

        mt_pol_inf_borr_nobridge(it_a_j,it_z_i) = fl_opti_inf_borr_nobridge;

        mt_pol_for_borr(it_a_j,it_z_i) = fl_opti_for_borr;

        mt_pol_for_save(it_a_j,it_z_i) = fl_opti_for_save;

    end

end

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

result_map('cl_mt_coh') = {f_coh(ar_z, ar_a'), zeros(1)};

result_map('cl_mt_pol_c') = {mt_pol_cons, zeros(1)};

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)};

result_map('ar_st_pol_names') = ["cl_mt_pol_a", "cl_mt_coh", "cl_mt_pol_c", ...

    "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);

%% Post Solution Graph and Table Generation

% Note in comparison with *abz*, results here, even when using identical

% parameters would differ because in *abz* solved where choices are

% principle. Here choices are principle + interests in order to facilitate

% using the informal choice functions.

%

% Note that this means two things are

% different, on the one hand, the value of asset for to coh is different

% based on the grid of assets. If the asset grid is negative, now per grid

% point, there is more coh because that grid point of asset no longer has

% interest rates. On the other hand, if one has positive asset grid point

% on arrival, that is worth less to coh. Additionally, when making choices

% for the next period, now choices aprime includes interests. What these

% mean is that the a grid no longer has the same meaning. We should expect

% at higher savings levels, for the same grid points, if optimal grid

% choices are the same as before, consumption should be lower when b

% includes interest rates and principle. This is however, not true when

% arriving in a period with negative a levels, for the same negative a

% level and same a prime negative choice, could have higher consumption

% here becasue have to pay less interests on debt. This tends to happen for

% smaller levels of borrowing choices.

%

% Graphically, when using interest + principle, big difference in

% consumption as a fraction of (coh - aprime) figure. In those figures,

% when counting in principles only, the gap in coh and aprime is

% consumption, but now, as more is borrowed only a small fraction of coh

% and aprime gap is consumption, becuase aprime/(1+r) is put into

% consumption.

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, :);

    % Standard AZ graphs

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

    % Graphs for results_map with FIBS contents

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

end

%% Display Various Containers

bl_display_defparam = true;

if (bl_display_defparam)

    %% Display 1 support_map    

    fft_container_map_display(support_map);

    %% Display 2 armt_map

    fft_container_map_display(armt_map);

    %% Display 3 param_map

    fft_container_map_display(param_map);

    %% Display 4 func_map

    fft_container_map_display(func_map);

    %% Display 5 result_map

    fft_container_map_display(result_map);

end

end