This paper presents a sequential optimization technique for the design of optimal trajectories while minimizing a cost index subject to system dynamics, path and actuation constraints. A novel coordinate transformation is introduced to cast the non-convex trajectory generation problem as a convex optimization problem. A sequential linear programming approach is discussed to solve the resulting ...

Cooperative relay systems have become an active area of research during recent years since they help cellular networks to enhance data rate and coverage. In this paper we develop a method to jointly optimize precoding matrices for amplify-and-forward relay station and base station. Our objective is to increase max–min SINR fairness within co-channel users in a cell. The main achievement of this...

This paper proposes a constrained stochastic successive convex approximation (CSSCA) algorithm to find a stationary point for a general non-convex stochastic optimization problem, whose objective and constraint functions are nonconvex and involve expectations over random states. The existing methods for non-convex stochastic optimization, such as the stochastic (average) gradient and stochastic...

To ensure efficient performance of irrigation canals, the losses from the canals need to be minimized. In this paper a modified formulation is presented to solve the optimization model for the design of different canal geometries for minimum seepage loss, in meta-heuristic environment. The complex non-linear and non-convex optimization model for canal design is solved using a probabilistic sear...

This paper introduces to constructing problems of convex relaxations for nonconvex polynomial optimization problems. Branch-and-bound algorithms are convex relaxation based. The convex envelopes are of primary importance since they represent the uniformly best convex underestimators for nonconvex polynomials over some region. The reformulationlinearization technique (RLT) generates LP (linear p...

We extend the model predictive control framework, which is very popular in the process industry due to its ability to handle constraints on inputs and outputs, to a class of discrete event systems that can be modeled using the operations maximization, minimization, addition and scalar multiplication, and that we call max-min-plus-scaling systems. We show that this class encompasses several othe...

Over the last decades, many efforts have been made toward understanding of convergence rate gradient-based method for both constrained and unconstrained optimization. The cases strongly convex weakly payoff function extensively studied are nowadays fully understood. Despite impressive advances in optimization context, nonlinear non-convex problems still not exploited. In this paper, we concerne...

The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving a design of a scalable test suite of constrained optimization problems, in which many features could be...

There are many problems related to the design of networks. Among them, the message routing problem plays a determinant role in the optimization of network performance. Much of the motivation for this work comes from this problem which is shown to belong to the class of nonlinear convex multicommodity ow problems. This paper emphasizes the message routing problem in data networks, but it include...

An important and perhaps interesting topic in nonlinear analysis and convex optimization concerns solving inclusions of the form 0 ∈ A(x), where A is a maximal monotone operator on a Hilbert space H. Its importance in convex optimization is evidenced from the fact that many problems that involve convexity can be formulated as finding zeros of maximal monotone operators. For example, convex mini...