129 lines
3.2 KiB
Mathematica
129 lines
3.2 KiB
Mathematica
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function varargout = plot(varargin)
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%PLOT Plots the feasible region of a set of constraints in an optimizer
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%
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% p = plot(C,x,c,n,options)
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%
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% Note that only convex sets are allowed, or union of convex sets
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% represented using binary variables (either defined explictly or
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% introduced by YALMIP when modelling, e.g., mixed integer linear
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% programming representable operators)
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%
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% C: Optimizer object
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% x: Plot variables [At most three variables]
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% c: color [double] ([r g b] format) or char from 'rymcgbk'
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% n: #vertices [double ] (default 100 in 2D and 300 otherwise)
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% options: options structure from sdpsettings
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% Get the onstraints
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if nargin<1
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return
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end
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F = varargin{1};
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if length(F)==0
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return;
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end
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if nargin < 5
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opts = sdpsettings('verbose',0);
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else
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opts = varargin{5};
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if isempty(opts)
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opts = sdpsettings('verbose',0);
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end
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end
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opts.verbose = max(opts.verbose-1,0);
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if nargin < 3
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color=['rymcgbk']';
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else
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color = varargin{3};
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if isa(color,'sdpvar')
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error('The variables should be specified in the second argument.');
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end
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color = color(:)';
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if isempty(color)
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color=['rymcgbk']';
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end
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end
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% Plot onto this projection (at most in 3D)
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if nargin < 2
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x = [];
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else
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x = varargin{2};
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if ~isempty(x)
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if ~isa(x,'sdpvar')
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error('Second argument should be empty or an SDPVAR');
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end
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x = x(:);
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x = x(1:min(3,length(x)));
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end
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end
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if isempty(F)
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return
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end
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% Create a model in YALMIPs low level format
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% All we change later is the cost vector
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%sol = solvesdp(F,sum(x),opts);
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internalmodel = F.model;
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internalmodel.options.saveduals = 0;
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internalmodel.getsolvertime = 0;
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internalmodel.options.dimacs = 0;
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% Try to find a suitable set to plot
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if isempty(x)
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if isempty(internalmodel.extended_variables) & isempty(internalmodel.aux_variables)
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x = internalmodel.used_variables;
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x = x(1:min(3,length(x)));
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localindex = find(ismember(internalmodel.used_variables,x));
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else
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% not extended variables
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candidates = setdiff(1:length(internalmodel.c),[ internalmodel.aux_variables(:)' internalmodel.extended_variables(:)']);
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% Not nonlinear variables
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candidates = candidates(find(internalmodel.variabletype(candidates)==0));
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% Settle with this guess
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localindex = candidates(1:min(3,length(candidates)));
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x = localindex;
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end
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else
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localindex = [];
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for i = 1:length(x)
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localindex = [localindex find(ismember(internalmodel.used_variables,getvariables(x(i))))];
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end
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end
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if nargin < 4
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if length(x)==3
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n = 300;
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else
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n = 100;
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end
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else
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n = varargin{4};
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if isempty(n)
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if length(x)==3
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n = 300;
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else
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n = 100;
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end
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end
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if ~isa(n,'double')
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error('4th argument should be an integer>0');
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end
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end
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if ~isempty(internalmodel.integer_variables)
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error('PLOT can currently not display sets involving integer variables');
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end
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% Use common function for lmi/plot and optimizer/plot
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if nargout == 0
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plotInternalModel(internalmodel,x,n,localindex,color,opts);
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else
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x_opt = plotInternalModel(internalmodel,x,n,localindex,color,opts);
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end
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