We present a class of methods for optimal holonomic planning in high dimensional spaces that automatically learns and leverages low dimensional structure to efficiently find high quality solutions. These methods are founded on the principle that problems possessing such structure are inherently simple to solve. This is demonstrated by presenting algorithms to solve these problems in time that s...

In this paper, a novel hybrid and complete approach for propositional satisfiability, called SATHYS (Sat Hybrid Solver), is introduced. It efficiently combines the strength of both local search and CDCL based SAT solvers. Considering the consistent partial assignment under construction by the CDCL SAT solver, local search is used to extend it to a model of the Boolean formula, while the CDCL co...

This paper analyses an infinite horizon two-echelon supply chain inventory problem and shows that a sequence of the optimum ordering policies does not yield globally optimal solutions for the overall supply chain. First-order autoregressive demand pattern is assumed and each participant adopts the order-up-to (OUT) policy with a minimum mean square error forecasting scheme to generate replenish...

For the problem of solving maximal monotone inclusions, we present a rather general class of algorithms, which contains hybrid inexact proximal point methods as a special case and allows for the use of a variable metric in subproblems. The global convergence and local linear rate of convergence are established under standard assumptions. We demonstrate the advantage of variable metric implement...

A previous paper showed that adding binary symmetry breaking constraints to a model can degrade local search performance. This paper shows that even unary constraints are not necessarily beneficial because they can transform solutions into local minima. Furthermore, performance can be improved by the reverse of symmetry breaking: using a supersymmetric model with extra symmetry. Some improved r...

Model predictive control (MPC) relies on real-time optimization to determine open-loop control profiles and state estimates given process measurements. When the underlying process model is nonlinear, the MPC system exhibits unique behavior not seen in linear MPC. In this article, we highlight some of the characteristics of nonlinear models in the context of closedloop performance. We examine th...

In this paper we present a promising technique that enhances the efficiency of GAs, when they are applied to constrained optimisation problems. According to this technique, the problem constraints are included in the fitness function as penalty terms, that vary during the GA evolution, facilitating thus the location of the global optimum and the avoidance of local optima. Moreover we proceed to...

Differential evolution algorithm has been widely used, because of its efficient optimization and no complex operation and coding mechanism. But DE falls into the local optimum easily. So this study puts forward a memetic algorithm. The algorithm can increase the diversity of population and jump out the local extreme value point effectively. The convergence speed of the algorithm is improved, be...

This paper proposes and analyzes an asynchronously parallel optimization algorithm for finding multiple, highquality minima of nonlinear optimization problems. Our multistart algorithm considers all previously evaluated points when determining where to start or continue a local optimization run. Theoretical results show that, under certain assumptions, the algorithm almost surely starts a finit...

Motivated by our recent work on rooted tree matching, in this paper we provide a solution to the problem of matching two free (i.e., unrooted) trees by constructing an association graph whose maximal cliques are in one-to-one correspondence with maximal common subtrees. We then solve the problem using simple payoff-monotonic dynamics from evolutionary game theory. We illustrate the power of the...