diff --git a/matlab/CHECK.m b/matlab/CHECK.m
index 15f8c2b2b..feb21b896 100644
--- a/matlab/CHECK.m
+++ b/matlab/CHECK.m
@@ -1,4 +1,4 @@
-function CEHCK(name,assertion)
+function CHECK(name,assertion)
 
 if (assertion~=1)
   error(['CHECK ' name ' fails']);
diff --git a/matlab/createLinearFactorGraph.m b/matlab/createLinearFactorGraph.m
index 60a87355d..2d9ef93e3 100644
--- a/matlab/createLinearFactorGraph.m
+++ b/matlab/createLinearFactorGraph.m
@@ -6,36 +6,57 @@ c = createNoisyConfig();
 
 % Create
 fg = GaussianFactorGraph;
+sigma1=.1;
 
 % prior on x1
-A11=[10 0; 0 10];
-b = - c.get('x1')/0.1;
+A11=eye(2);
+b = - c.get('x1');
 
+<<<<<<< .mine
+f1 = LinearFactor('x1', A11, b, sigma1);
+=======
 f1 = GaussianFactor('x1', A11, b);
+>>>>>>> .r1017
 fg.push_back(f1);
 
 % odometry between x1 and x2
-A21=[-10 0; 0 -10];
-A22=[ 10 0; 0  10];
-b = [2;-1];
+sigma2=.1;
 
+<<<<<<< .mine
+A21=-eye(2);
+A22=eye(2);
+b = [.2;-.1];
+
+f2 = LinearFactor('x1', A21,  'x2', A22, b,sigma2);
+=======
 f2 = GaussianFactor('x1', A21,  'x2', A22, b);
+>>>>>>> .r1017
 fg.push_back(f2);
 
 % measurement between x1 and l1
-A31=[-5 0; 0 -5];
-A32=[ 5 0; 0  5];
-b = [0;1];
+sigma3=.2;
+A31=-eye(2);
+A33=eye(2);
+b = [0;.2];
 
+<<<<<<< .mine
+f3 = LinearFactor('x1', A31, 'l1', A33, b,sigma3);
+=======
 f3 = GaussianFactor('x1', A31, 'l1', A32, b);
+>>>>>>> .r1017
 fg.push_back(f3);
 
 % measurement between x2 and l1
-A41=[-5 0; 0 -5];
-A42=[ 5 0; 0  5];
-b = [-1;1.5];
+sigma4=.2;
+A42=-eye(2);
+A43=eye(2);
+b = [-.2;.3];
 
+<<<<<<< .mine
+f4 = LinearFactor('x2', A42, 'l1', A43, b,sigma4);
+=======
 f4 = GaussianFactor('x2', A41, 'l1', A42, b);
+>>>>>>> .r1017
 fg.push_back(f4);
 
 end
\ No newline at end of file
diff --git a/matlab/createZeroDelta.m b/matlab/createZeroDelta.m
index 221d480c5..82ebdb340 100644
--- a/matlab/createZeroDelta.m
+++ b/matlab/createZeroDelta.m
@@ -3,7 +3,7 @@ function c = createZeroDelta()
 v_x1 = [0; 0];
 v_x2 = [0; 0];
 v_l1 = [0; 0];
-c = FGConfig();
+c = VectorConfig();
 c.insert('x1', v_x1);
 c.insert('x2', v_x2);
 c.insert('l1', v_l1);
diff --git a/matlab/create_random_landmarks.m b/matlab/create_random_landmarks.m
index 60894aa9f..4b2048bec 100644
--- a/matlab/create_random_landmarks.m
+++ b/matlab/create_random_landmarks.m
@@ -1,6 +1,8 @@
 % Christian Potthast
 % Create a map with random landmarks
 
-function map = create_random_landmarks(numberOfLandmarks)
+function map = create_random_landmarks(numberOfLandmarks, mappingArea)
 
-map = rand(2,numberOfLandmarks)*100;
+points = rand(2,numberOfLandmarks);
+
+map=[points(1,:)*mappingArea(1);points(2,:)*mappingArea(2)];
diff --git a/matlab/example.m b/matlab/example.m
index 511db8280..b939ea532 100644
--- a/matlab/example.m
+++ b/matlab/example.m
@@ -3,21 +3,21 @@
 
 clear;
 
-n = 100;
-m = 20;
+n = 1000;
+m = 200;
 
 % Set up the map 
-map = create_random_landmarks(n);
+    map = create_random_landmarks(n,[1000,1000]);
 figure(1);clf;
 plot(map(1,:), map(2,:),'g.'); hold on;
 
 % have the robot move in this world
 trajectory = random_walk([0.1,0.1],5,m);
 plot(trajectory(1,:),trajectory(2,:),'b+');
-axis([0 100 0 100]);axis square;
+axis([0 1000 0 1000]);axis square;
 
 % Check visibility and plot this on the problem figure
-visibility = create_visibility(map, trajectory,10);
+visibility = create_visibility(map, trajectory,50);
 gplot(visibility,[map trajectory]');
 figure(2);clf;
 spy(visibility)
@@ -38,12 +38,20 @@ ord = create_ordering(n,m);
 
 % show the matrix
 figure(3); clf;
-[A,b] = linearFactorGraph.matrix(ord);
-spy(A);
-
+[A_dense,b] = linearFactorGraph.matrix(ord);
+%spy(A);
+ A=sparse(A_dense);
 % eliminate with that ordering
+ck = cputime;
 BayesNet = linearFactorGraph.eliminate(ord);
+time_gtsam = cputime - ck
 
+ckqr = cputime;
+R = qr(A);
+time_qr = cputime - ckqr
+
+
+%time_gtsam=[time_gtsam,(cputime-ck)]
 % show the eliminated matrix
 figure(4); clf;
 [R,d] = BayesNet.matrix();
@@ -57,4 +65,4 @@ figure(5);clf;
 plot_config(optimal,n,m);hold on
 plot(trajectory(1,:),trajectory(2,:),'b+');
 plot(map(1,:), map(2,:),'g.');
-axis([0 100 0 100]);axis square;
+axis([0 1000 0 1000]);axis square;
diff --git a/matlab/testConditionalGaussian.m b/matlab/testConditionalGaussian.m
index b4a7c63b8..8f06928bc 100644
--- a/matlab/testConditionalGaussian.m
+++ b/matlab/testConditionalGaussian.m
@@ -8,13 +8,15 @@ A1 =[1 2; 3 4];
 A2 = [6 0.2;8 0.4];
 R = [0.1 0.3; 0.0 0.34];
 d=[0.2;0.5];
+tau=[1;.34];
 
-cg = ConditionalGaussian(d, R, 'x1', A1, 'l1', A2);
+
+cg = ConditionalGaussian('x',d, R, 'x1', A1, 'l1', A2, tau);
 
 sx1 = [0.2;0.5];
 sl1 = [0.5;0.8];
 
-solution = FGConfig;
+%solution = FGConfig;
 solution.insert('x1', sx1);
 solution.insert('l1', sl1);
 
diff --git a/matlab/test_time.m b/matlab/test_time.m
index b7440124b..b09971d4c 100644
--- a/matlab/test_time.m
+++ b/matlab/test_time.m
@@ -3,7 +3,7 @@
 clear;
 
 %Parameters
-noRuns=100;
+noRuns=5;
 steps=1;
 m = 5;
 velocity=1;
@@ -28,7 +28,7 @@ for steps=1:noRuns
     visibility = create_visibility(map, trajectory,visibilityTh);
     %gplot(visibility,[map trajectory]');
     
-
+steps
     % simulate the measurements
     measurement_sigma = 1;
     odo_sigma = 0.1;
@@ -46,9 +46,12 @@ for steps=1:noRuns
 
     % show the matrix
    % figure(3); clf;
-    [A_dense,b] = linearFactorGraph.matrix(ord);
-    A=sparse(A_dense);
-    size(A)
+    %[A_dense,b] = linearFactorGraph.matrix(ord);
+    %A=sparse(A_dense);
+
+    ijs = linearFactorGraph.sparse(ord);
+    A=sparse(ijs(1,:),ijs(2,:),ijs(3,:)); 
+    
     %spy(A);
     %time qr
     ck=cputime;
diff --git a/matlab/test_time_new.m b/matlab/test_time_new.m
new file mode 100644
index 000000000..d2ecf41a8
--- /dev/null
+++ b/matlab/test_time_new.m
@@ -0,0 +1,112 @@
+% Set up a small SLAM example in MATLAB to test the execution time
+
+clear;
+
+%Parameters
+
+noRuns=5;
+steps=50;
+m = 5*steps;
+velocity=1*steps;
+time_qr=[];
+time_gtsam=[];
+%    for steps=1:noRuns
+%  
+    figure(1);clf;
+    % robot moves in the world
+    trajectory = walk([0.1,0.1],velocity,m);
+    mappingArea=max(trajectory,[],2);
+    plot(trajectory(1,:),trajectory(2,:),'b+'); hold on;
+
+    visibilityTh=sqrt(mappingArea(1)^2+mappingArea(2)^2)/m; %distance between poses
+    % Set up the map
+    map = create_landmarks(visibilityTh, mappingArea,steps);
+    plot(map(1,:), map(2,:),'g.');
+    axis([0 mappingArea(1) 0 mappingArea(2)]); axis square;
+    n=size(map,2);
+    % Check visibility and plot this on the problem figure
+    visibilityTh=visibilityTh+steps;
+    visibility = create_visibility(map, trajectory,visibilityTh);
+    gplot(visibility,[map trajectory]');
+    
+
+    % simulate the measurements
+    measurement_sigma = 1;
+    odo_sigma = 0.1;
+    [measurements, odometry] = simulate_measurements(map, trajectory, visibility, measurement_sigma, odo_sigma);
+    
+    
+%     % create a configuration of all zeroes
+     config = create_config(n,m);
+
+    % create the factor graph
+    linearFactorGraph = create_linear_factor_graph(config, measurements, odometry, measurement_sigma, odo_sigma, n);
+    % 
+    % create an ordering
+    %ord = create_good_ordering(n,m,measurements);
+    ord = create_ordering(n,m);
+    % show the matrix
+   % figure(3); clf;
+    %[A_dense,b] = linearFactorGraph.matrix(ord);
+   %A=sparse(A_dense);
+    
+    %sparse matrix !!!
+    ijs = linearFactorGraph.sparse(ord);
+    A=sparse(ijs(1,:),ijs(2,:),ijs(3,:)); 
+    %spy(A);
+    %time qr
+    runs=1;
+    
+    ck_qr=cputime;
+    for i=1:runs
+        R_qr = qr(A);      
+    end
+    time_qr=(cputime-ck_qr)/runs
+    %time_qr=[time_qr,(cputime-ck)];
+
+    %figure(2)
+    %clf
+    %spy(R_qr);
+    
+    % eliminate with that ordering
+    %time gt_sam
+%     for i=1:runs+10
+%         if i==11
+%         ck_gt=cputime;
+%         end
+%         BayesNet = linearFactorGraph.eliminate(ord); 
+%     end
+ck_gt=cputime;
+for i=1:runs+10
+    BayesNet = linearFactorGraph.eliminate(ord);
+end
+    time_gtsam=(cputime-ck_gt)/runs
+    %time_gtsam=[time_gtsam,(cputime-ck)];
+    
+%     clear trajectory visibility linearFactorGraph measurements odometry;
+%     m = m+5;
+%     velocity=velocity+1;
+
+% end
+% %time_qr=time_qr/noRuns
+%  plot(time_qr,'r');hold on;
+%  plot(time_gtsam,'b');
+
+
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% % show the eliminated matrix
+% figure(4); clf;
+% [R,d] = BayesNet.matrix();
+% spy(R);
+% 
+% % optimize in the BayesNet
+% optimal = BayesNet.optimize;
+% 
+% % plot the solution
+% figure(5);clf; 
+% plot_config(optimal,n,m);hold on
+% plot(trajectory(1,:),trajectory(2,:),'b+');
+% plot(map(1,:), map(2,:),'g.');
+% axis([0 10 0 10]);axis square;