```% Section 8.7.1, Boyd & Vandenberghe "Convex Optimization"
% Joelle Skaf - 10/23/05
%
% K fixed points (u1,v1),..., (uK,vK) in R^2 are given and the goal is to place
% one additional point (u,v) such that:
% 1) the L1-norm is minimized, i.e.
%           minimize    sum_{i=1}^K ( |u - u_i| + |v - v_i| )
%    the solution in this case is any median of the fixed points
% 2) the L2-norm is minimized, i.e.
%           minimize    sum_{i=1}^K ( |u - u_i|^2 + |v - v_i|^2 )^.5
%    the solution in this case is the Weber point of the fixed points

% Data generation
n = 2;
K = 11;
randn('state',0);
P = randn(n,K);

% L1 - norm
fprintf(1,'Minimizing the L1-norm of the sum of the distances to fixed points...');

cvx_begin
variable x1(2)
minimize ( sum(norms(x1*ones(1,K) - P,1)) )
cvx_end

fprintf(1,'Done! \n');

% L2 - norm
fprintf(1,'Minimizing the L2-norm of the sum of the distances to fixed points...');

cvx_begin
variable x2(2)
minimize ( sum(norms(x2*ones(1,K) - P,2)) )
cvx_end

fprintf(1,'Done! \n');

% Displaying results
disp('------------------------------------------------------------------');
disp('The optimal point location for the L1-norm case is: ');
disp(x1);
disp('The optimal point location for the L2-norm case is: ');
disp(x2);
```
```Minimizing the L1-norm of the sum of the distances to fixed points...
Calling SDPT3 4.0: 44 variables, 20 equality constraints
------------------------------------------------------------

num. of constraints = 20
dim. of socp   var  = 44,   num. of socp blk  = 22
*******************************************************************
SDPT3: Infeasible path-following algorithms
*******************************************************************
version  predcorr  gam  expon  scale_data
NT      1      0.000   1        0
it pstep dstep pinfeas dinfeas  gap      prim-obj      dual-obj    cputime
-------------------------------------------------------------------
0|0.000|0.000|8.8e-01|3.9e+00|3.6e+02| 6.248756e+01  0.000000e+00| 0:0:00| chol  1  1
1|1.000|0.876|1.2e-07|5.5e-01|8.0e+01| 5.455840e+01  1.131909e+01| 0:0:00| chol  1  1
2|1.000|1.000|1.1e-07|8.2e-03|6.9e+00| 1.759114e+01  1.082797e+01| 0:0:00| chol  1  1
3|0.853|0.904|1.5e-07|1.5e-03|1.1e+00| 1.447923e+01  1.337235e+01| 0:0:00| chol  1  1
4|0.808|0.968|2.6e-07|1.3e-04|2.2e-01| 1.401801e+01  1.380170e+01| 0:0:00| chol  1  1
5|1.000|0.923|7.8e-09|1.8e-05|2.4e-02| 1.388024e+01  1.385605e+01| 0:0:00| chol  1  1
6|0.987|0.987|7.2e-10|1.1e-06|3.3e-04| 1.386826e+01  1.386795e+01| 0:0:00| chol  1  1
7|0.989|0.989|4.2e-11|1.2e-08|3.6e-06| 1.386810e+01  1.386810e+01| 0:0:00| chol  1  1
8|0.991|0.993|5.4e-13|9.3e-11|4.8e-08| 1.386810e+01  1.386810e+01| 0:0:00|
stop: max(relative gap, infeasibilities) < 1.49e-08
-------------------------------------------------------------------
number of iterations   =  8
primal objective value =  1.38681000e+01
dual   objective value =  1.38681000e+01
gap := trace(XZ)       = 4.81e-08
relative gap           = 1.67e-09
actual relative gap    = 1.62e-09
rel. primal infeas (scaled problem)   = 5.43e-13
rel. dual     "        "       "      = 9.31e-11
rel. primal infeas (unscaled problem) = 0.00e+00
rel. dual     "        "       "      = 0.00e+00
norm(X), norm(y), norm(Z) = 5.8e+00, 4.2e+00, 6.5e+00
norm(A), norm(b), norm(C) = 7.3e+00, 8.3e+00, 5.7e+00
Total CPU time (secs)  = 0.09
CPU time per iteration = 0.01
termination code       =  0
DIMACS: 9.2e-13  0.0e+00  2.6e-10  0.0e+00  1.6e-09  1.7e-09
-------------------------------------------------------------------

------------------------------------------------------------
Status: Solved
Optimal value (cvx_optval): +13.8681

Done!
Minimizing the L2-norm of the sum of the distances to fixed points...
Calling SDPT3 4.0: 33 variables, 13 equality constraints
For improved efficiency, SDPT3 is solving the dual problem.
------------------------------------------------------------

num. of constraints = 13
dim. of socp   var  = 33,   num. of socp blk  = 11
*******************************************************************
SDPT3: Infeasible path-following algorithms
*******************************************************************
version  predcorr  gam  expon  scale_data
NT      1      0.000   1        0
it pstep dstep pinfeas dinfeas  gap      prim-obj      dual-obj    cputime
-------------------------------------------------------------------
0|0.000|0.000|5.6e-01|3.4e+00|9.8e+01| 0.000000e+00  0.000000e+00| 0:0:00| chol  1  1
1|1.000|0.930|1.3e-07|3.0e-01|1.1e+01|-6.474954e+00 -1.154705e+01| 0:0:00| chol  1  1
2|0.926|0.859|2.5e-07|4.7e-02|1.4e+00|-1.077944e+01 -1.129839e+01| 0:0:00| chol  1  1
3|0.861|0.969|8.0e-08|2.1e-03|1.7e-01|-1.137189e+01 -1.150434e+01| 0:0:00| chol  1  1
4|0.954|0.965|9.5e-08|1.4e-04|7.4e-03|-1.147883e+01 -1.148379e+01| 0:0:00| chol  1  1
5|0.948|0.981|2.3e-08|8.9e-06|3.1e-04|-1.148369e+01 -1.148385e+01| 0:0:00| chol  1  1
6|0.919|0.963|6.5e-09|3.4e-07|2.3e-05|-1.148391e+01 -1.148393e+01| 0:0:00| chol  1  1
7|0.871|0.977|1.7e-09|9.0e-09|3.4e-06|-1.148393e+01 -1.148393e+01| 0:0:00| chol  2  2
8|0.824|0.992|4.6e-10|4.0e-10|5.1e-07|-1.148393e+01 -1.148393e+01| 0:0:00| chol  2  2
9|0.955|0.984|4.2e-11|9.9e-11|3.2e-08|-1.148393e+01 -1.148393e+01| 0:0:00|
stop: max(relative gap, infeasibilities) < 1.49e-08
-------------------------------------------------------------------
number of iterations   =  9
primal objective value = -1.14839292e+01
dual   objective value = -1.14839293e+01
gap := trace(XZ)       = 3.22e-08
relative gap           = 1.34e-09
actual relative gap    = 1.27e-09
rel. primal infeas (scaled problem)   = 4.16e-11
rel. dual     "        "       "      = 9.93e-11
rel. primal infeas (unscaled problem) = 0.00e+00
rel. dual     "        "       "      = 0.00e+00
norm(X), norm(y), norm(Z) = 4.7e+00, 4.1e+00, 5.8e+00
norm(A), norm(b), norm(C) = 6.7e+00, 4.3e+00, 5.2e+00
Total CPU time (secs)  = 0.08
CPU time per iteration = 0.01
termination code       =  0
DIMACS: 9.0e-11  0.0e+00  1.7e-10  0.0e+00  1.3e-09  1.3e-09
-------------------------------------------------------------------

------------------------------------------------------------
Status: Solved
Optimal value (cvx_optval): +11.4839

Done!
------------------------------------------------------------------
The optimal point location for the L1-norm case is:
-0.0956
0.1139

The optimal point location for the L2-norm case is:
0.1252
0.1716

```