function varargout = sumk(varargin) % SUMK Returns sum of k largest (eigen-)values. % % s = SUMK(X,k) % % For a vector X, SUMK returns the sum of the k largest elements. % % For a symmetric matrix X, SUMK returns the sum of the k largest eigen-values. % % See also SUMABSK % Author Johan Löfberg % $Id: sumk.m,v 1.1 2006/03/30 13:36:39 joloef Exp $ % *************************************************** % This file defines a nonlinear operator for YALMIP % % It can take three different inputs % For double inputs, it returns standard double values % For sdpvar inputs, it genreates a an internal variable % When first input is 'model' it generates the epigraph % and a structure descring properties of the operator % *************************************************** switch class(varargin{1}) case 'double' % What is the numerical value of this argument (needed for displays etc) if nargin == 1 error('sumk needs two arguments'); else X = varargin{1}; [n,m] = size(X); if (min(n,m) > 1 & ~issymmetric(X)) error('sumk can only be applied on vectors and symmetric matrices'); else k = min(length(X),varargin{2}); if min(n,m)==1 sorted = sort(X); else sorted = sort(eig(X)); end varargout{1} = sum(sorted(max(1,end-k+1):end)); end end case 'sdpvar' % Overloaded operator for SDPVAR objects. Pass on args and save them. X = varargin{1}; [n,m] = size(X); if (min(n,m) > 1 & ~issymmetric(X)) error('sumk can only be applied on vectors and symmetric matrices'); else if nargin<2 error('sumk needs two arguments'); else varargout{1} = yalmip('addextendedvariable',mfilename,varargin{:}); end end case 'char' % YALMIP send 'model' when it wants the epigraph or hypograph if isequal(varargin{1},'graph') t = varargin{2}; % Second arg is the extended operator variable X = varargin{3}; % Third arg and above are the args user used when defining t. k = min(varargin{4},length(X)); [n,m] = size(X); Z = sdpvar(n,m); s = sdpvar(1,1); if min(n,m)==1 varargout{1} = set(t-k*s-sum(Z) > 0) + set(Z > 0) + set(Z-X+s > 0); varargout{2} = struct('convexity','convex','monotonicity','increasing','definiteness','none'); else varargout{1} = set(t-k*s-trace(Z) > 0) + set(Z > 0) + set(Z-X+s*eye(n) > 0); varargout{2} = struct('convexity','convex','monotonicity','none','definiteness','none'); end varargout{3} = X; else end otherwise end