QSPIN: A High Level Java API for Quantum Computing Experimentation

QSPIN is a high level Java language API for experimentation in QC models used in the calculation of Ising spin glass ground states and related quadratic unconstrained binary optimization (QUBO) problems. The Java API is intended to facilitate research in advanced QC algorithms such as hybrid quantum-classical solvers, automatic selection of constraint and optimization parameters, and techniques for the correction and mitigation of model and solution errors. QSPIN includes high level solver objects tailored to the D-Wave quantum annealing architecture that implement hybrid quantum-classical algorithms [Booth et al.] for solving large problems on small quantum devices, elimination of variables via roof duality, and classical computing optimization methods such as GPU accelerated simulated annealing and tabu search for comparison. A test suite of documented NP-complete applications ranging from graph coloring, covering, and partitioning to integer programming and scheduling are provided to demonstrate current capabilities

Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

Inhibition and young children's performance on the Tower of London task

Young children, when performing problem solving tasks, show a tendency to break task rules and produce incomplete solutions. We propose that this tendency can be explained by understanding problem solving within the context of the development of “executive functions” – general cognitive control functions, which serve to regulate the operation of the cognitive system. This proposal is supported by the construction of two computational models that simulate separately the performance of 3–4 year old and 5–6 year old children on the Tower of London planning task. We seek in particular to capture the emerging role of inhibition in the older group. The basic framework within which the models are developed is derived from Fox and Das’ Domino model [Fox, J., & Das, S. (2000). Safe and sound: Artificial intelligence in hazardous applications. Cambridge, MA: MIT Press] and Norman and Shallice’s [Norman, D.A., & Shallice, T. (1986). Attention to action: Willed and automatic control of behaviour. In R. Davidson, G. Schwartz, & D. Shapiro (Eds.), Consciousness and Self Regulation (Vol. 4). New York: Plenum] theory of willed and automatic action. Two strategies and a simple perceptual bias are implemented within the models and comparisons between model and child performance reveal a good fit for the key dependent measures (number of rule breaks and percentage of incomplete solutions) of the two groups

Which Problems Does a Multi-Language Virtual Machine Need to Solve in the Multicore/Manycore Era?

While parallel programming for very regular problems has been used in the scientific community by non-computer-scientists successfully for a few decades now, concurrent programming and solving irregular problems remains hard. Furthermore, we shift from few expert system programmers mastering concurrency for a constrained set of problems to mainstream application developers being required to master concurrency for a wide variety of problems. Consequently, high-level language virtual machine (VM) research faces interesting questions. What are processor design changes that have an impact on the abstractions provided by VMs to provide platform independence? How can application programmers' diverse needs be facilitated to solve concurrent programming problems? We argue that VMs will need to be ready for a wide range of different concurrency models that allow solving concurrency problems with appropriate abstractions. Furthermore, they need to abstract from heterogeneous processor architectures, varying performance characteristics, need to account for memory access cost and inter-core communication mechanisms but should only expose the minimal useful set of notions like locality, explicit communication, and adaptable scheduling to maintain their abstracting nature. Eventually, language designers need to be enabled to guarantee properties like encapsulation, scheduling guarantees, and immutability also when an interaction between different problem-specific concurrency abstractions is required

Evaluation of Kermeta for Solving Graph-based Problems

Kermeta is a meta-language for specifying the structure and behavior of graphs of interconnected objects called models. In this paper,\ud we show that Kermeta is relatively suitable for solving three graph-based\ud problems. First, Kermeta allows the specification of generic model\ud transformations such as refactorings that we apply to different metamodels\ud including Ecore, Java, and Uml. Second, we demonstrate the extensibility\ud of Kermeta to the formal language Alloy using an inter-language model\ud transformation. Kermeta uses Alloy to generate recommendations for\ud completing partially specified models. Third, we show that the Kermeta\ud compiler achieves better execution time and memory performance compared\ud to similar graph-based approaches using a common case study. The\ud three solutions proposed for those graph-based problems and their\ud evaluation with Kermeta according to the criteria of genericity,\ud extensibility, and performance are the main contribution of the paper.\ud Another contribution is the comparison of these solutions with those\ud proposed by other graph-based tools

Human-Machine Collaborative Optimization via Apprenticeship Scheduling

Coordinating agents to complete a set of tasks with intercoupled temporal and resource constraints is computationally challenging, yet human domain experts can solve these difficult scheduling problems using paradigms learned through years of apprenticeship. A process for manually codifying this domain knowledge within a computational framework is necessary to scale beyond the ``single-expert, single-trainee" apprenticeship model. However, human domain experts often have difficulty describing their decision-making processes, causing the codification of this knowledge to become laborious. We propose a new approach for capturing domain-expert heuristics through a pairwise ranking formulation. Our approach is model-free and does not require enumerating or iterating through a large state space. We empirically demonstrate that this approach accurately learns multifaceted heuristics on a synthetic data set incorporating job-shop scheduling and vehicle routing problems, as well as on two real-world data sets consisting of demonstrations of experts solving a weapon-to-target assignment problem and a hospital resource allocation problem. We also demonstrate that policies learned from human scheduling demonstration via apprenticeship learning can substantially improve the efficiency of a branch-and-bound search for an optimal schedule. We employ this human-machine collaborative optimization technique on a variant of the weapon-to-target assignment problem. We demonstrate that this technique generates solutions substantially superior to those produced by human domain experts at a rate up to 9.5 times faster than an optimization approach and can be applied to optimally solve problems twice as complex as those solved by a human demonstrator.Comment: Portions of this paper were published in the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper consists of 50 pages with 11 figures and 4 table

OptBPPlanner: Automatic Generation of Optimized Business Process Enactment Plans

Unlike imperative models, the specifi cation of business process (BP) properties in a declarative way allows the user to specify what has to be done instead of having to specify how it has to be done, thereby facilitating the human work involved, avoiding failures, and obtaining a better optimization. Frequently, there are several enactment plans related to a specifi c declarative model, each one presenting specifi c values for different objective functions, e.g., overall completion time. As a major contribution of this work, we propose a method for the automatic generation of optimized BP enactment plans from declarative specifi cations. The proposed method is based on a constraint-based approach for planning and scheduling the BP activities. These optimized plans can then be used for different purposes like simulation, time prediction, recommendations, and generation of optimized BP models. Moreover, a tool-supported method, called OptBPPlanner, has been implemented to demonstrate the feasibility of our approach. Furthermore, the proposed method is validated through a range of test models of varying complexity.Ministerio de Ciencia e Innovación TIN2009-1371

A Novel SAT-Based Approach to the Task Graph Cost-Optimal Scheduling Problem

The Task Graph Cost-Optimal Scheduling Problem consists in scheduling a certain number of interdependent tasks onto a set of heterogeneous processors (characterized by idle and running rates per time unit), minimizing the cost of the entire process. This paper provides a novel formulation for this scheduling puzzle, in which an optimal solution is computed through a sequence of Binate Covering Problems, hinged within a Bounded Model Checking paradigm. In this approach, each covering instance, providing a min-cost trace for a given schedule depth, can be solved with several strategies, resorting to Minimum-Cost Satisfiability solvers or Pseudo-Boolean Optimization tools. Unfortunately, all direct resolution methods show very low efficiency and scalability. As a consequence, we introduce a specialized method to solve the same sequence of problems, based on a traditional all-solution SAT solver. This approach follows the "circuit cofactoring" strategy, as it exploits a powerful technique to capture a large set of solutions for any new SAT counter-example. The overall method is completed with a branch-and-bound heuristic which evaluates lower and upper bounds of the schedule length, to reduce the state space that has to be visited. Our results show that the proposed strategy significantly improves the blind binate covering schema, and it outperforms general purpose state-of-the-art tool