qpipopt_mat Solves a linear quadratic problem. xopt = qpipopt_mat(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB) x = qpipopt_mat(H,f) x = qpipopt_mat(H,f,A,b) x = qpipopt_mat(H,f,A,b,Aeq,beq) x = qpipopt_mat(H,f,A,b,Aeq,beq,lb,ub) [xopt,fopt,exitflag,output,lamda] = qpipopt_mat( ... ) H : a n x n matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem. f : a n x 1 matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem A : a m x n matrix of doubles, represents the linear coefficients in the inequality constraints b : a column vector of doubles, represents the linear coefficients in the inequality constraints Aeq : a meq x n 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 n x 1 matrix of doubles, where n is number of variables, contains lower bounds of the variables. UB : a n x 1 matrix of doubles, where n is number of variables, contains upper bounds of the variables. xopt : a nx1 matrix of doubles, the computed solution of the optimization problem. fopt : a 1x1 matrix of doubles, the function value at x. exitflag : Integer identifying the reason the algorithm terminated. output : Structure containing information about the optimization. lambda : Structure containing the Lagrange multipliers at the solution x (separated by constraint type). 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 \leq beq \\ & & lb \leq x \leq ub \\ \end{eqnarray} We are calling IPOpt for solving the quadratic problem, IPOpt is a library written in C++. The code has been written by ​Andreas Wächter and ​Carl Laird. Keyur Joshi, Saikiran, Iswarya, Harpreet Singh