3,992 research outputs found
Closed orbit correction at synchrotrons for symmetric and near-symmetric lattices
This contribution compiles the benefits of lattice symmetry in the context of
closed orbit correction. A symmetric arrangement of BPMs and correctors results
in structured orbit response matrices of Circulant or block Circulant type.
These forms of matrices provide favorable properties in terms of computational
complexity, information compression and interpretation of mathematical vector
spaces of BPMs and correctors. For broken symmetries, a nearest-Circulant
approximation is introduced and the practical advantages of symmetry
exploitation are demonstrated with the help of simulations and experiments in
the context of FAIR synchrotrons
Reformulation in planning
Reformulation of a problem is intended to make the problem more amenable to efficient solution. This is equally true in the special case of reformulating a planning problem. This paper considers various ways in which reformulation can be exploited in planning
Taming Numbers and Durations in the Model Checking Integrated Planning System
The Model Checking Integrated Planning System (MIPS) is a temporal least
commitment heuristic search planner based on a flexible object-oriented
workbench architecture. Its design clearly separates explicit and symbolic
directed exploration algorithms from the set of on-line and off-line computed
estimates and associated data structures. MIPS has shown distinguished
performance in the last two international planning competitions. In the last
event the description language was extended from pure propositional planning to
include numerical state variables, action durations, and plan quality objective
functions. Plans were no longer sequences of actions but time-stamped
schedules. As a participant of the fully automated track of the competition,
MIPS has proven to be a general system; in each track and every benchmark
domain it efficiently computed plans of remarkable quality. This article
introduces and analyzes the most important algorithmic novelties that were
necessary to tackle the new layers of expressiveness in the benchmark problems
and to achieve a high level of performance. The extensions include critical
path analysis of sequentially generated plans to generate corresponding optimal
parallel plans. The linear time algorithm to compute the parallel plan bypasses
known NP hardness results for partial ordering by scheduling plans with respect
to the set of actions and the imposed precedence relations. The efficiency of
this algorithm also allows us to improve the exploration guidance: for each
encountered planning state the corresponding approximate sequential plan is
scheduled. One major strength of MIPS is its static analysis phase that grounds
and simplifies parameterized predicates, functions and operators, that infers
knowledge to minimize the state description length, and that detects domain
object symmetries. The latter aspect is analyzed in detail. MIPS has been
developed to serve as a complete and optimal state space planner, with
admissible estimates, exploration engines and branching cuts. In the
competition version, however, certain performance compromises had to be made,
including floating point arithmetic, weighted heuristic search exploration
according to an inadmissible estimate and parameterized optimization
Symmetry reduction and heuristic search for error detection in model checking
The state explosion problem is the main limitation of model checking. Symmetries in the system being verified can be exploited in order to avoid this problem by defining an equivalence (symmetry) relation on the states of the system, which induces a semantically equivalent quotient system of smaller size. On the other hand, heuristic search algorithms can be applied to improve the bug finding capabilities of model checking. Such algorithms use
heuristic functions to guide the exploration. Bestfirst
is used for accelerating the search, while A* guarantees optimal error trails if combined with admissible estimates. We analyze some aspects of combining both approaches, concentrating on the problem of finding the optimal path to the equivalence class of a given error state. Experimental
results evaluate our approach
Tensor network states and algorithms in the presence of a global U(1) symmetry
Tensor network decompositions offer an efficient description of certain
many-body states of a lattice system and are the basis of a wealth of numerical
simulation algorithms. In a recent paper [arXiv:0907.2994v1] we discussed how
to incorporate a global internal symmetry, given by a compact, completely
reducible group G, into tensor network decompositions and algorithms. Here we
specialize to the case of Abelian groups and, for concreteness, to a U(1)
symmetry, often associated with particle number conservation. We consider
tensor networks made of tensors that are invariant (or covariant) under the
symmetry, and explain how to decompose and manipulate such tensors in order to
exploit their symmetry. In numerical calculations, the use of U(1) symmetric
tensors allows selection of a specific number of particles, ensures the exact
preservation of particle number, and significantly reduces computational costs.
We illustrate all these points in the context of the multi-scale entanglement
renormalization ansatz.Comment: 22 pages, 25 figures, RevTeX
State space c-reductions for concurrent systems in rewriting logic
We present c-reductions, a state space reduction technique.
The rough idea is to exploit some equivalence relation on states (possibly capturing system regularities) that preserves behavioral properties, and explore the induced quotient system. This is done by means of a canonizer
function, which maps each state into a (non necessarily unique) canonical representative of its equivalence class. The approach exploits the expressiveness of rewriting logic and its realization in Maude to enjoy several advantages over similar approaches: exibility and simplicity in
the definition of the reductions (supporting not only traditional symmetry reductions, but also name reuse and name abstraction); reasoning support for checking and proving correctness of the reductions; and automatization
of the reduction infrastructure via Maude's meta-programming
features. The approach has been validated over a set of representative case studies, exhibiting comparable results with respect to other tools
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