A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs

Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors. The algorithms of interestinclude exact methods, such as the interior point method, the Nelder–Mead method, theactive set method, the sequential quadratic programming, and metaheuristic algorithms,such as particle swarm optimization, simulated annealing and genetic algorithms.Several simulations are performed, which provide general recommendations on theutility and performance of each method, including hybridized versions of metaheuristicalgorithms for finding optimal experimental designs. A key result is that general-purposeoptimization algorithms, both exact methods and metaheuristic algorithms, perform wellfor finding optimal approximate experimental designs

Quantum Alternation: Prospects and Problems

We propose a notion of quantum control in a quantum programming language which permits the superposition of finitely many quantum operations without performing a measurement. This notion takes the form of a conditional construct similar to the IF statement in classical programming languages. We show that adding such a quantum IF statement to the QPL programming language simplifies the presentation of several quantum algorithms. This motivates the possibility of extending the denotational semantics of QPL to include this form of quantum alternation. We give a denotational semantics for this extension of QPL based on Kraus decompositions rather than on superoperators. Finally, we clarify the relation between quantum alternation and recursion, and discuss the possibility of lifting the semantics defined by Kraus operators to the superoperator semantics defined by Selinger.Comment: In Proceedings QPL 2015, arXiv:1511.0118

A study on exponential-size neighborhoods for the bin packing problem with conflicts

We propose an iterated local search based on several classes of local and large neighborhoods for the bin packing problem with conflicts. This problem, which combines the characteristics of both bin packing and vertex coloring, arises in various application contexts such as logistics and transportation, timetabling, and resource allocation for cloud computing. We introduce $O(1)$ evaluation procedures for classical local-search moves, polynomial variants of ejection chains and assignment neighborhoods, an adaptive set covering-based neighborhood, and finally a controlled use of 0-cost moves to further diversify the search. The overall method produces solutions of good quality on the classical benchmark instances and scales very well with an increase of problem size. Extensive computational experiments are conducted to measure the respective contribution of each proposed neighborhood. In particular, the 0-cost moves and the large neighborhood based on set covering contribute very significantly to the search. Several research perspectives are open in relation to possible hybridizations with other state-of-the-art mathematical programming heuristics for this problem.Comment: 26 pages, 8 figure

Understanding and profiling user requirements to support the conceptual design of an integrated land monitoring system

Acquiring and organizing knowledge and information elements can be essential not only to understand, but also to eliminate, reduce and control complexity and uncertainty. An integration of tools from different disciplines could systematically help in the construction of an agreed framework for problem formulation, above all when the situation is "new". An application was de-veloped in relation to an industrial project, in order to propose profiles of the potential users of an innovative system and of their requirements, and to for-mally develop models that can orient analysis, decision and action. Some ele-ments and results of this integrated application of "soft" and "hard" decision aid tools are here proposed as steps of an organizational learning cycle, which is a basic element of each innovation proces

TRX: A Formally Verified Parser Interpreter

Parsing is an important problem in computer science and yet surprisingly little attention has been devoted to its formal verification. In this paper, we present TRX: a parser interpreter formally developed in the proof assistant Coq, capable of producing formally correct parsers. We are using parsing expression grammars (PEGs), a formalism essentially representing recursive descent parsing, which we consider an attractive alternative to context-free grammars (CFGs). From this formalization we can extract a parser for an arbitrary PEG grammar with the warranty of total correctness, i.e., the resulting parser is terminating and correct with respect to its grammar and the semantics of PEGs; both properties formally proven in Coq.Comment: 26 pages, LMC