Further, to improve computational efficiency, we present a relaxed non-linear programming reformulation of the problem that allows us to find good initial points for the mixed-integer nonlinear programming problem.
This complexity issue is relaxed by problem (P2) and its convex second-order cone programming reformulation (P3), in which the dimension of the optimization variable f u grows only linearly with the size of the FD-MIMO antenna array.
Based on previous results, this work presents a new algorithm that features: (i) a dynamic programming reformulation of the MPC optimization, (ii) a robust reformulation of the constraints that accounts for uncertainty and (iii) a multi parametric programming solution step where the controls are obtained as an explicit function of the states.
In contrast, we give the robust reformulation and reformulate the problem as a quadratically constrained quadratic program or convex program with a conic quadratic inequality quadratic program, which is tractable in optimization theory.
Computers and Chemical Engineering, 24, 2125 2141] have developed a reformulation of Generalized Disjunctive Programming (GDP) problems that is based on determining the convex hull of each disjunction.
We extend known reformulation approaches to reformulate the general 0 1 fractional programming problem such that it can be solved by standard MILP software.
Computers and Chemical Engineering, 24, 2125 2141] have developed a reformulation for nonlinear Generalized Disjunctive Programming (GDP) problems that obtains from the intersection of the convex hulls of every disjunction.
Based on new results related to exact penalty and error bounds in DC programming [28], the same reformulation technique via exact penalty can be used for Linear constrained mixed zero-one DC programming problems.
Based on the optimal value function reformulation approach, the linear semivectorial bilevel programming problem is transformed into a nonsmooth optimization problem, and a solution algorithm is proposed.
Multiparametric programming allowed for the reformulation of the MPC problem into an explicit form (21, 22) where a single set of optimizations can be performed a priori.
