Commit be5407af authored by Gianluca Frison's avatar Gianluca Frison

added solution to 5.5 and 5.6

parent f3eb2342
% In this script one solves the MDR (missing data recovery) problem
% A data vector f represented by a sparse (few nonzeros xs) expansion
% in a frame (here the discrete cosine transform DCT) Phi. f experiences
% a loss of information, its last n-m components are lost in b. The
% system Phi(1:m,:)*xr = b that would need to be solved to recover the
% coefficients xr is underdetermined and thus has infinitely many
% solutions. Since it is known that xs is sparse, one tries to
% find a sparse xr vector by solving the optimization problem
% min ||x||_0 subject to Ax = b, ||.||_0 denotes no. of nonzeros
% This is a combinatorial problem and NP hard. One replaces the ||.||_0
% norm by the ||.||_1 norm which encourages sparsity when minimized.
% (MDR) min ||x||_1 subject to Ax = b (x free)
% This problem can be rewritten as standard form LP and solved by
% Mehrotra's interior point method. Sparsity should be exploited
% Use m = 400, 350, 300
n = 500; % data size
m = 400; % available data size; may be changed
Phi = dctmtx(n); % get the frame
xs = zeros(n,1); % initialize x*
k = 10; % number of nonzeros in x*
rand('state',1011); % for reproducibility
p = randperm(60); % indices of nonzeros in x*
randn('state',11); % for reproducibility
xs(p(1:k)) = randn(k,1); % random values for x*
f = Phi*xs; % complete data vector v*
b = f(1:m); % available data b
A = Phi(1:m,:); % matrix A
subplot(4,1,1); % put all graphs in one figure
plot(1:n,f); % plot complete data
plot(1:n,[b;zeros(n-m,1)]); % plot available data
% fill in here; set up the LP and solve with Matlab's linprog
nn = 2*n;
xx = 1e0*ones(nn,1);
c = ones(nn,1);
AA = [A -A];
[xx,ff] = meh(AA,c,b,xx,m,nn);
xr = xx(1:n) - xx(n+1:nn);
plot(1:n,Phi*xr); % plot recovered data
plot(1:n,Phi*xr-f); % plot error
| Solution of ex05 |
conv1.mod: CPLEX complains that there are diagonal QP coefficients of the wrong sign..
conv2.mod: QP Hessian is not positive semidefinite.
This only works with QP, while here the constraints are quadratic.
conv3.mod: linearized constraint.
Locally optimal solution of indefinite QP (using QP barrier iterations), solution (0,-2.5).
conv4.mod: optimal integer solution (using branch-and-bound) (0,-2.5)
conv5.mod: Gurobi complains that quadratic objective is not positive definite.
conv6.mod: Xpress complains that quadratic objective is not conves.
conv7.mod: optimal solution found (0,-2.5).
Original problem with (convex) quadratic constraints.
conv8.mod: optimal solution found, (0,1).
There is another solution in (0,-1) (the problem is non-convex).
Problem with non-convex feasible set.
conv9.mod: optimal solution found (0,1.73).
There is another solution in (0,-1.73) (the problem is non-convex).
See svm.mod.
See mdr.m.
You need to double the number of variables, since now for each x_i there are x_i^+ (that is equal to x_i if x_i>0 and equal to 0 otherwise), and x_i^- (that is equalt to 0 if x_i>0 and equal to -x_i otherwise).
The objective is
c^T x = e^T (x^+ + x^-) = e^T x^+ + e^T x^- = [e; e]^T [x^+; x^-]
where e = [1; ... ; 1]
The equality constraint is
b = A x = A (x^+ - x^-) = A x^+ - A x^- = [A -A] [x^+ ; x^-]
And there are the additional inequality constraints on the sign of x^+ and x^-
#option solver gurobi;
#option solver cplex;
option solver mosek;
display w;
param n integer;
param m integer;
param x{1..n,1..m+1};
var w{1..m};
var z{1..n} >= 0;
var gamma;
minimize obj:
0.5*sum{i in 1..m} w[i]^2 + sum{i in 1..n} z[i];
s.t. c1{i in 1..n}: x[i,m+1]*(sum{j in 1..m} w[j]*x[i,j] + gamma) >= 1 - z[i];
data svm.dat;
#option solver gurobi;
option solver cplex;
#option solver cbc;
display w;
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