Deterministic parallelism has become an increasingly attractive concept: a deterministic parallel program may be easier to construct, debug, understand, and maintain. However, there exist many different definitions of “determinism” for parallel programming. Many existing definitions have not yet been fully formalized, and the relationships among these definitions are still unclear. We argue tha...

The advent of multicore processors has made concurrent programming models mandatory. However, most concurrent programming models come with a repertoire of problems. The two major ones are non-determinism and deadlocks. By determinism, we mean the output behavior of the program is independent of the interleaving caused by the schedule and depends only on the input behavior. A few concurrent mode...

The present work describes the structure of a generic modeling tool for stochastic dynamic programming problems (also called Markov Decision Processes – MDPs). Deterministic problems are handled as particular cases of MDP’s. The program is intended for general purpose problems and its structure permits independent modeling and solving. It is designed to capture the essential modeling elements o...

Abstract — This paper considers a linear programming problem with ellipsoidal distributions including fuzziness. Since this problem is not well-defined due to randomness and fuzziness, it is hard to solve it directly. Therefore, introducing chance constraints, fuzzy goals and possibility measures, the proposed model is transformed into the deterministic equivalent problems. Furthermore, since i...

Here, we consider single vendor-buyer model with multi-product and multi-customer and multi-facility location-production-distribution problem. It is assumed that the players of the supply chain are coordinated by sharing information. Vendor manufactures produce different products at different plants with limited capacities and then distribute the products to the consumers according to determini...

where L(t) is a given matrix. Unlike the deterministic setting, when the state is a random variable one cannot directly use the variational optimality conditions since the adjoint equation λ̇ = −∂xH cannot describe all possible random evolutions of x(t). We must resort to dynamic programming. We next consider dynamic programming for stochastic systems through the derivation of the stochastic Ham...

This paper addresses eecient parsing in the context of logical inference for the purpose of using logic programming languages in compiler writing. A bottom-up, deterministic parsing mechanism is formulated for \bounded right context" grammars, a subclass of LR(k) grammars with characteristics amenable to declarative parser speciica-tion. A working parser generator for Prolog is described, altho...

We study in this lecture the literature on mixed integer programming models and formulations for a specific problem class, namely deterministic production planning problems. The objective is to present the classical optimization approaches used, and the known models, for dealing with such management problems. We describe first production planning models in the general context of manufacturing p...

Concurrent programming languages should be a good fit for embedded systems because they match the intrinsic parallelism of their architectures and environments. Unfortunately, most concurrent programming formalisms are prone to races and nondeterminism, despite the presence of mechanisms such as monitors. In this paper, we propose SHIM, the core of a concurrent language with disciplined shared ...

We present a new algorithm for deterministic sorting on the Bulk-Synchronous Parallel (BSP) model of computation. We sort n keys using a partitioning scheme that achieves the requirements of eeciency (one-optimality) and insensitivity against initial data distribution. Although we employ sampling to realize eeciency, we give a precise worst-case estimation of the maximum imbalance which might o...