qpipopt Solves a linear quadratic problem. xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB) [xopt,fopt,exitflag,output,lamda] = qpipopt( ... ) nbVar : a 1 x 1 matrix of doubles, number of variables nbCon : a 1 x 1 matrix of doubles, number of constraints Q : a n x n symmetric matrix of doubles, where n is number of variables, represents coefficients of quadratic in the quadratic problem. p : a 1 x n matrix of doubles, where n is number of variables, represents coefficients of linear in the quadratic problem LB : a 1 x n matrix of doubles, where n is number of variables, contains lower bounds of the variables. UB : a 1 x n matrix of doubles, where n is number of variables, contains upper bounds of the variables. conMatrix : a m x n matrix of doubles, where n is number of variables and m is number of constraints, contains matrix representing the constraint matrix conLB : a m x 1 matrix of doubles, where m is number of constraints, contains lower bounds of the constraints. conUB : a m x 1 matrix of doubles, where m is number of constraints, contains upper bounds of the constraints. xopt : a 1xn 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'*Q*x + p'*x \\ & \text{subject to} & conLB \leq C(x) \leq conUB \\ & & 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