qpipoptmat Solves a linear quadratic problem. xopt = qpipoptmat(H,f) xopt = qpipoptmat(H,f,A,b) xopt = qpipoptmat(H,f,A,b,Aeq,beq) xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub) xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0) xopt = qpipoptmat(H,f,A,b,Aeq,beq,lb,ub,x0,param) [xopt,fopt,exitflag,output,lamda] = qpipoptmat( ... ) H : a symmetric matrix of doubles, represents coefficients of quadratic in the quadratic problem. f : a vector of doubles, represents coefficients of linear in the quadratic problem A : a vector of doubles, represents the linear coefficients in the inequality constraints b : a vector of doubles, represents the linear coefficients in the inequality constraints Aeq : a matrix of doubles, represents the linear coefficients in the equality constraints beq : a vector of doubles, represents the linear coefficients in the equality constraints LB : a vector of doubles, contains lower bounds of the variables. UB : a vector of doubles, contains upper bounds of the variables. x0 : a vector of doubles, contains initial guess of variables. param : a list containing the the parameters to be set. xopt : a vector of doubles, the computed solution of the optimization problem. fopt : a double, the function value at x. exitflag : Integer identifying the reason the algorithm terminated. output : Structure containing information about the optimization. Right now it contains number of iteration. lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type).It contains lower, upper and linear equality, inequality constraints. Search the minimum of a constrained linear quadratic optimization problem specified by : find the minimum of f(x) such that \begin{eqnarray} &\mbox{min}_{x} & 1/2*x'*H*x + f'*x \\ & \text{subject to} & A*x \leq b \\ & & Aeq*x = beq \\ & & lb \leq x \leq ub \\ \end{eqnarray} We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. Keyur Joshi, Saikiran, Iswarya, Harpreet Singh