Systematic and variational truncation of the configuration space in the multiconfiguration time-dependent Hartree method: The MCTDH[n] hierarchy

The multiconfiguration time-dependent Hartree (MCTDH) method is a powerful method for solving the time-dependent Schrödinger equation in quantum molecular dynamics. It is, however, hampered by the so-called curse of dimensionality which results in exponential scaling with respect to the number of degrees of freedom in the system and, thus, limits its applicability to small- and medium-sized molecules. To avoid this scaling, we derive equations of motion for a series of truncated MCTDH methods using a many-mode second-quantization formulation where the configuration space is restricted based on mode-combination levels as also done in the vibrational configuration interaction and vibrational coupled cluster methods for solving the time-independent Schrödinger equation. The full MCTDH wave function is invariant with respect to the choice of constraint (or gauge) operators, but restricting the configuration space removes this invariance. We, thus, analyze the remaining redundancies and derive equations for variationally optimizing the non-redundant matrix elements of the constraint operators. As an alternative, we also present a constraint that keeps the density matrices block diagonal during the propagation and the two choices are compared. Example calculations are performed on formyl fluoride and a series of high-dimensional Henon–Heiles potentials. The results show that the MCTDH[n] methods can be applied to large systems and that an optimal choice of constraint operators is key to obtaining the correct physical behavior of the wave function

Guaranteeing Soundness of Configurable Process Variants in Provop

Usually, for a particular business process a multitude of variants exists. Each of them constitutes an adjustment of a reference process model to specific requirements building the process context. While some progress has been achieved regarding the configuration of process variants, there exists only little work on how to accomplish this in a sound and efficient manner, especially when considering the large number of process variants that exist in practice as well as the many syntactical and semantical constraints they have to obey. In this paper we discuss advanced concepts for the context- and constraint-based configuration of process variants, and show how they can be utilized to ensure soundness of the configured process variants. Enhancing process-aware information systems with the capability to easily configure sound process models, belonging to the same process family and fitting to the given application context, will enable a new quality in engineering process-aware information systems

Taming complexity of industrial printing systems using a constraint-based DSL: An industrial experience report

Flexible printing systems are highly complex systems that consist of printers, that print individual sheets of paper, and finishing equipment, that processes sheets after printing, for example, assembling a book. Integrating finishing equipment with printers involves the development of control software that configures the devices, taking hardware constraints into account. This control software is highly complex to realize due to (1) the intertwined nature of printing and finishing, (2) the large variety of print products and production options for a given product, and (3) the large range of finishers produced by different vendors. We have developed a domain-specific language called CSX that offers an interface to constraint solving specific to the printing domain. We use it to model printing and finishing devices and to automatically derive constraint solver-based environments for automatic configuration. We evaluate CSX on its coverage of the printing domain in an industrial context, and we report on lessons learned on using a constraint-based DSL in an industrial context

A Middleware Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications

We propose a middleware framework for deployment and subsequent autonomic management of component-based distributed applications. An initial deployment goal is specified using a declarative constraint language, expressing constraints over aspects such as component-host mappings and component interconnection topology. A constraint solver is used to find a configuration that satisfies the goal, and the configuration is deployed automatically. The deployed application is instrumented to allow subsequent autonomic management. If, during execution, the manager detects that the original goal is no longer being met, the satisfy/deploy process can be repeated automatically in order to generate a revised deployment that does meet the goal.Comment: Submitted to Middleware 0

A Framework for Constraint-Based Deployment and Autonomic Management of Distributed Applications

We propose a framework for deployment and subsequent autonomic management of component-based distributed applications. An initial deployment goal is specified using a declarative constraint language, expressing constraints over aspects such as component-host mappings and component interconnection topology. A constraint solver is used to find a configuration that satisfies the goal, and the configuration is deployed automatically. The deployed application is instrumented to allow subsequent autonomic management. If, during execution, the manager detects that the original goal is no longer being met, the satisfy/deploy process can be repeated automatically in order to generate a revised deployment that does meet the goal.Comment: Submitted to ICAC-0

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing