Improved optimization

Removes repeating optic sets
Stores possible solutions
Returns waist from fitness function
Solutions with same significant digits are truncated
Visualize possible solutions
Outputs solutions with a fitness threshold

1 files changed, 49 insertions, 5 deletions

diff --git a/fitter_check.m b/fitter_check.m
index 53d3299..ff93c13 100644
--- a/fitter_check.m
+++ b/fitter_check.m
@@ -1,5 +1,6 @@
 
 % ########################################## 
+% Sample Solution
 clear;
 lambda= 1.064E-6 ;
 Ltot= 1.010675025828971 ;
@@ -31,38 +32,62 @@ w_final_handmade = solution_visualization(q0,x0, qf, xf, optics, lambda);
 title('Hand made');
 % ########################################## 
 
+
 %Initialize sample arrays
 sample_energy = [];
 sample_x = [];
+possible_soln = [];
+possible_lens_pos = [];
 lens_size = .03;
 
 %Lens permutations
 lens_permutations = perms( [ focal_length1, focal_length2, focal_length3 ]);
-n_perms = size(lens_permutations,1);
 n_shuffles=10;
 
+%Check if permutation has duplicates
+lens_permutations = unique(lens_permutations,'rows');
+n_perms = size(lens_permutations,1);
+
 for i = 1:n_perms
     
     
     lenses_choice=lens_permutations(i,:)
-    
+      
     for iteration = 1:n_shuffles
         optics_x_rand = sort(lens_size+(xf-2*lens_size)*rand(1,3));
         
-        fitness_simplified=@(x) fitness(q0, qf, Ltot, x, lenses_choice );
+        fitness_simplified=@(x) fitness(q0, qf, Ltot, x, lenses_choice, lambda );
         [x_sol, energy]=fminsearch(fitness_simplified, optics_x_rand, optimset('TolX',1e-8,'TolFun',1e-8,'MaxFunEvals',1e8,'MaxIter',2000));
         
         sample_energy = [sample_energy; energy];
         sample_x = [sample_x; x_sol];
         
+        %Return final Waist of trial
+        q_f_trial = gbeam_propagation(Ltot,q0,x0,optics_placer(x_sol, lenses_choice));
+        [waist, Radius] = q2wr(q_f_trial, lambda);
+        
+        %If it is a good solution, add to list of possible solutions
+        waist_desired = wf;
+        compare_waist = abs(waist - waist_desired);
+        tolerance = 1E-6;
+                
+        if compare_waist < tolerance
+            possible_soln = [possible_soln; x_sol];
+            possible_lens_pos = [possible_lens_pos; lenses_choice];
+        end
+        
+        %Visualize solution
         figure(2)
         solution_visualization(q0,x0, qf, xf, optics_placer(x_sol, lenses_choice), lambda);
-        drawnow;
+        title('Testing Points');
+        drawnow;       
+      
     end
     
 end
 
-[energy_min, index_of_energy_min] = min(sample_energy(:))
+%Display solution with lowest energy
+[energy_min, index_of_energy_min] = min(sample_energy(:));
 x_sol = sample_x(index_of_energy_min,:);
 lenses_choice=lens_permutations(ceil(index_of_energy_min/n_shuffles),:);
 
@@ -70,5 +95,24 @@ figure(2)
 w_final_trial = solution_visualization(q0,x0, qf, xf, optics_placer(x_sol, lenses_choice), lambda);
 title('Optimized made');
 
+
+%Truncate other possible solutions to an accuracy of n decimal places
+n=4; 
+possible_soln = round(possible_soln*10^n)./10^n;
+[possible_soln, index] = unique(possible_soln,'rows'); %Unique solutions only
+
+rounded_x_sol = round(x_sol*10^n)./10^n;
+remove_index = find(ismember(possible_soln, rounded_x_sol,'rows'),1);
+possible_soln(remove_index,:) = [];
+index(remove_index,:) = [];
+
+%Visualize other solutions
+n_possible_soln = size(possible_soln,1);
+for n_graph = 1:n_possible_soln
+    figure(n_graph+2)
+    w_final_trial = solution_visualization(q0,x0, qf, xf, optics_placer(possible_soln(n_graph,:), possible_lens_pos(index(n_graph),:)), lambda);
+    title('Other Solutions');
+end
+
 w_final_handmade;
 x_sol