qpipopt Solves a linear quadratic problem. xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB) xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0) xopt = qpipopt(nbVar,nbCon,Q,p,LB,UB,conMatrix,conLB,conUB,x0,param) [xopt,fopt,exitflag,output,lamda] = qpipopt( ... ) nbVar : a double, number of variables nbCon : a double, number of constraints Q : a symmetric matrix of double, represents coefficients of quadratic in the quadratic problem. p : a vector of double, represents coefficients of linear in the quadratic problem LB : a vector of double, contains lower bounds of the variables. UB : a vector of double, contains upper bounds of the variables. conMatrix : a matrix of double, contains matrix representing the constraint matrix conLB : a vector of double, contains lower bounds of the constraints. conUB : a vector of double, contains upper bounds of the constraints. x0 : a vector of double, contains initial guess of variables. param : a list containing the the parameters to be set. xopt : a vector of double, 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'*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++. Keyur Joshi, Saikiran, Iswarya, Harpreet Singh