% "Filter design" lecture notes (EE364) by S. Boyd
% (figures are generated)
%
% Designs an FIR filter given a desired frequency response H_des(w).
% The design is judged by the maximum absolute error (Chebychev norm).
% This is a convex problem (after sampling it can be formulated as an SOCP).
%
%   minimize   max |H(w) - H_des(w)|     for w in [0,pi]
%
% where H is the frequency response function and variable is h
% (the filter impulse response).
%
% Written for CVX by Almir Mutapcic 02/02/06

%********************************************************************
% problem specs
%********************************************************************
% number of FIR coefficients (including the zeroth one)
n = 20;

% rule-of-thumb frequency discretization (Cheney's Approx. Theory book)
m = 15*n;
w = linspace(0,pi,m)'; % omega

%********************************************************************
% construct the desired filter
%********************************************************************
% fractional delay
D = 8.25;            % delay value
Hdes = exp(-j*D*w);  % desired frequency response

% Gaussian filter with linear phase (uncomment lines below for this design)
% var = 0.05;
% Hdes = 1/(sqrt(2*pi*var))*exp(-(w-pi/2).^2/(2*var));
% Hdes = Hdes.*exp(-j*n/2*w);

%*********************************************************************
% solve the minimax (Chebychev) design problem
%*********************************************************************
% A is the matrix used to compute the frequency response
% A(w,:) = [1 exp(-j*w) exp(-j*2*w) ... exp(-j*n*w)]
A = exp( -j*kron(w,[0:n-1]) );

% optimal Chebyshev filter formulation
cvx_begin
  variable h(n,1)
  minimize( max( abs( A*h - Hdes ) ) )
cvx_end

% check if problem was successfully solved
disp(['Problem is ' cvx_status])
if ~strfind(cvx_status,'Solved')
  h = [];
end

%*********************************************************************
% plotting routines
%*********************************************************************
% plot the FIR impulse reponse
figure(1)
stem([0:n-1],h)
xlabel('n')
ylabel('h(n)')

% plot the frequency response
H = [exp(-j*kron(w,[0:n-1]))]*h;
figure(2)
% magnitude
subplot(2,1,1);
plot(w,20*log10(abs(H)),w,20*log10(abs(Hdes)),'--')
xlabel('w')
ylabel('mag H in dB')
axis([0 pi -30 10])
legend('optimized','desired','Location','SouthEast')
% phase
subplot(2,1,2)
plot(w,angle(H))
axis([0,pi,-pi,pi])
xlabel('w'), ylabel('phase H(w)')

 
Calling Mosek 9.1.9: 1199 variables, 321 equality constraints
   For improved efficiency, Mosek is solving the dual problem.
------------------------------------------------------------

MOSEK Version 9.1.9 (Build date: 2019-11-21 11:32:15)
Copyright (c) MOSEK ApS, Denmark. WWW: mosek.com
Platform: MACOSX/64-X86

MOSEK warning 710: #1 (nearly) zero elements are specified in sparse col '' (990) of matrix 'A'.
MOSEK warning 710: #1 (nearly) zero elements are specified in sparse col '' (1059) of matrix 'A'.
MOSEK warning 710: #6 (nearly) zero elements are specified in sparse col '' (1197) of matrix 'A'.
Problem
  Name                   :                 
  Objective sense        : min             
  Type                   : CONIC (conic optimization problem)
  Constraints            : 321             
  Cones                  : 300             
  Scalar variables       : 1199            
  Matrix variables       : 0               
  Integer variables      : 0               

Optimizer started.
Presolve started.
Linear dependency checker started.
Linear dependency checker terminated.
Eliminator started.
Freed constraints in eliminator : 0
Eliminator terminated.
Eliminator - tries                  : 1                 time                   : 0.00            
Lin. dep.  - tries                  : 1                 time                   : 0.00            
Lin. dep.  - number                 : 0               
Presolve terminated. Time: 0.00    
Problem
  Name                   :                 
  Objective sense        : min             
  Type                   : CONIC (conic optimization problem)
  Constraints            : 321             
  Cones                  : 300             
  Scalar variables       : 1199            
  Matrix variables       : 0               
  Integer variables      : 0               

Optimizer  - threads                : 8               
Optimizer  - solved problem         : the primal      
Optimizer  - Constraints            : 21
Optimizer  - Cones                  : 300
Optimizer  - Scalar variables       : 899               conic                  : 899             
Optimizer  - Semi-definite variables: 0                 scalarized             : 0               
Factor     - setup time             : 0.00              dense det. time        : 0.00            
Factor     - ML order time          : 0.00              GP order time          : 0.00            
Factor     - nonzeros before factor : 231               after factor           : 231             
Factor     - dense dim.             : 0                 flops                  : 3.82e+05        
ITE PFEAS    DFEAS    GFEAS    PRSTATUS   POBJ              DOBJ              MU       TIME  
0   3.0e+02  1.0e+00  1.0e+00  0.00e+00   0.000000000e+00   0.000000000e+00   1.0e+00  0.01  
1   3.7e+01  1.2e-01  3.0e-02  -7.74e-02  -9.404267175e-01  -1.027139074e+00  1.2e-01  0.01  
2   1.4e+01  4.7e-02  9.1e-03  2.82e+00   -6.722914608e-01  -6.681299006e-01  4.7e-02  0.01  
3   2.0e+00  6.8e-03  4.8e-04  1.05e+00   -7.106031190e-01  -7.103719672e-01  6.8e-03  0.02  
4   4.2e-02  1.4e-04  1.4e-06  1.02e+00   -7.071883645e-01  -7.071789603e-01  1.4e-04  0.02  
5   8.1e-05  2.7e-07  1.2e-10  1.00e+00   -7.071069665e-01  -7.071069484e-01  2.7e-07  0.02  
6   1.4e-07  4.7e-10  9.1e-15  1.00e+00   -7.071067815e-01  -7.071067815e-01  4.7e-10  0.02  
7   2.4e-10  1.2e-10  4.0e-17  1.00e+00   -7.071067812e-01  -7.071067812e-01  1.0e-12  0.02  
Optimizer terminated. Time: 0.02    


Interior-point solution summary
  Problem status  : PRIMAL_AND_DUAL_FEASIBLE
  Solution status : OPTIMAL
  Primal.  obj: -7.0710678116e-01   nrm: 1e+00    Viol.  con: 2e-10    var: 0e+00    cones: 0e+00  
  Dual.    obj: -7.0710678119e-01   nrm: 9e-01    Viol.  con: 0e+00    var: 7e-13    cones: 0e+00  
Optimizer summary
  Optimizer                 -                        time: 0.02    
    Interior-point          - iterations : 7         time: 0.02    
      Basis identification  -                        time: 0.00    
        Primal              - iterations : 0         time: 0.00    
        Dual                - iterations : 0         time: 0.00    
        Clean primal        - iterations : 0         time: 0.00    
        Clean dual          - iterations : 0         time: 0.00    
    Simplex                 -                        time: 0.00    
      Primal simplex        - iterations : 0         time: 0.00    
      Dual simplex          - iterations : 0         time: 0.00    
    Mixed integer           - relaxations: 0         time: 0.00    

------------------------------------------------------------
Status: Solved
Optimal value (cvx_optval): +0.707107
 
Problem is Solved