```% Section 8.4.1, Boyd & Vandenberghe "Convex Optimization"
% Original version by Lieven Vandenberghe
% Updated for CVX by Almir Mutapcic - Jan 2006
% (a figure is generated)
%
% We find a smallest ellipsoid containing m ellipsoids
% { x'*A_i*x + 2*b_i'*x + c < 0 }, for i = 1,...,m
%
% Problem data:
% As = {A1, A2, ..., Am}:  cell array of m pos. def. matrices
% bs = {b1, b2, ..., bm}:  cell array of m 2-vectors
% cs = {c1, c2, ..., cm}:  cell array of m scalars

% ellipse data
As = {}; bs = {}; cs = {};
As{1} = [ 0.1355    0.1148;  0.1148    0.4398];
As{2} = [ 0.6064   -0.1022; -0.1022    0.7344];
As{3} = [ 0.7127   -0.0559; -0.0559    0.9253];
As{4} = [ 0.2706   -0.1379; -0.1379    0.2515];
As{5} = [ 0.4008   -0.1112; -0.1112    0.2107];
bs{1} = [ -0.2042  0.0264]';
bs{2} = [  0.8259 -2.1188]';
bs{3} = [ -0.0256  1.0591]';
bs{4} = [  0.1827 -0.3844]';
bs{5} = [  0.3823 -0.8253]';
cs{1} = 0.2351;
cs{2} = 5.8250;
cs{3} = 0.9968;
cs{4} = -0.2981;
cs{5} = 2.6735;

% dimensions
n = 2;
m = size(bs,2);    % m ellipsoids given

% construct and solve the problem as posed in the book
cvx_begin sdp
variable Asqr(n,n) symmetric
variable btilde(n)
variable t(m)
maximize( det_rootn( Asqr ) )
subject to
t >= 0;
for i = 1:m
[ -(Asqr - t(i)*As{i}), -(btilde - t(i)*bs{i}), zeros(n,n);
-(btilde - t(i)*bs{i})', -(- 1 - t(i)*cs{i}), -btilde';
zeros(n,n), -btilde, Asqr] >= 0;
end
cvx_end

% convert to ellipsoid parametrization E = { x | || Ax + b || <= 1 }
A = sqrtm(Asqr);
b = A\btilde;

% plot ellipsoids using { x | || A_i x + b_i || <= alpha } parametrization
noangles = 200;
angles   = linspace( 0, 2 * pi, noangles );

clf
for i=1:m
Ai = sqrtm(As{i}); bi = Ai\bs{i};
alpha = bs{i}'*inv(As{i})*bs{i} - cs{i};
ellipse  = Ai \ [ sqrt(alpha)*cos(angles)-bi(1) ; sqrt(alpha)*sin(angles)-bi(2) ];
plot( ellipse(1,:), ellipse(2,:), 'b-' );
hold on
end
ellipse  = A \ [ cos(angles) - b(1) ; sin(angles) - b(2) ];

plot( ellipse(1,:), ellipse(2,:), 'r--' );
axis square
axis off
hold off
```
```
Calling SDPT3 4.0: 94 variables, 15 equality constraints
For improved efficiency, SDPT3 is solving the dual problem.
------------------------------------------------------------

num. of constraints = 15
dim. of sdp    var  = 31,   num. of sdp  blk  =  7
dim. of linear var  =  6
*******************************************************************
SDPT3: Infeasible path-following algorithms
*******************************************************************
version  predcorr  gam  expon  scale_data
HKM      1      0.000   1        0
it pstep dstep pinfeas dinfeas  gap      prim-obj      dual-obj    cputime
-------------------------------------------------------------------
0|0.000|0.000|5.3e+01|1.9e+01|3.7e+03| 5.000000e+01  0.000000e+00| 0:0:00| chol  1  1
1|0.784|0.775|1.2e+01|4.3e+00|9.5e+02| 2.983363e+01 -7.077314e+00| 0:0:00| chol  1  1
2|0.845|0.744|1.8e+00|1.1e+00|3.3e+02| 4.983568e+01 -8.153378e+00| 0:0:00| chol  1  1
3|1.000|0.894|8.9e-06|1.2e-01|5.6e+01| 3.017981e+01 -1.898935e+00| 0:0:00| chol  1  1
4|0.733|1.000|5.6e-06|1.0e-04|1.5e+01| 1.511527e+01 -3.114373e-01| 0:0:00| chol  1  1
5|0.925|0.986|4.3e-07|1.2e-05|1.2e+00| 1.190285e+00  3.201085e-03| 0:0:00| chol  1  1
6|1.000|1.000|6.9e-09|1.1e-06|5.3e-01| 5.626657e-01  3.398809e-02| 0:0:00| chol  1  1
7|0.896|0.991|2.1e-09|1.1e-07|5.9e-02| 1.220844e-01  6.287837e-02| 0:0:00| chol  1  1
8|1.000|0.924|6.4e-09|1.8e-08|2.1e-02| 9.631015e-02  7.562064e-02| 0:0:00| chol  1  1
9|0.955|0.945|6.1e-10|2.6e-09|1.2e-03| 7.968284e-02  7.848102e-02| 0:0:00| chol  1  1
10|0.936|0.984|3.9e-11|2.6e-10|7.6e-05| 7.875163e-02  7.867538e-02| 0:0:00| chol  1  1
11|0.955|0.967|1.8e-12|1.6e-11|3.6e-06| 7.868475e-02  7.868112e-02| 0:0:00| chol  1  1
12|1.000|1.000|1.8e-09|1.0e-12|5.6e-07| 7.868191e-02  7.868136e-02| 0:0:00| chol  1  1
13|1.000|1.000|7.5e-11|1.5e-12|2.2e-08| 7.868148e-02  7.868146e-02| 0:0:00|# chol  1  1
14|1.000|1.000|5.3e-11|2.2e-12|1.0e-09| 7.868147e-02  7.868146e-02| 0:0:00|
stop: max(relative gap, infeasibilities) < 1.49e-08
-------------------------------------------------------------------
number of iterations   = 14
primal objective value =  7.86814654e-02
dual   objective value =  7.86814643e-02
gap := trace(XZ)       = 1.01e-09
relative gap           = 8.75e-10
actual relative gap    = 8.81e-10
rel. primal infeas (scaled problem)   = 5.33e-11
rel. dual     "        "       "      = 2.25e-12
rel. primal infeas (unscaled problem) = 0.00e+00
rel. dual     "        "       "      = 0.00e+00
norm(X), norm(y), norm(Z) = 2.4e+00, 3.0e+00, 8.1e+00
norm(A), norm(b), norm(C) = 1.4e+01, 2.0e+00, 3.2e+00
Total CPU time (secs)  = 0.23
CPU time per iteration = 0.02
termination code       =  0
DIMACS: 5.3e-11  0.0e+00  3.6e-12  0.0e+00  8.8e-10  8.7e-10
-------------------------------------------------------------------

------------------------------------------------------------
Status: Solved
Optimal value (cvx_optval): +0.0786815

```