94,983 research outputs found
Arc and path consistency revisited
Journal ArticleMackworth and Freuder have analyzed the time complexity of several constraint satisfaction algorithms [4]. We present here new algorithms for arc and path consistency and show that the arc consistency algorithm is optimal in time complexity and of the same order space complexity as the earlier algorithms. A refined solution for the path consistency problem is proposed. However, the space complexity of the path consistency algorithm makes it practicable only for small problems. These algorithms are the result of the synthesis techniques used in ALICE (a general constraint satisfaction system) and local consistency methods
EC Maritime Transport Policy and Regulation
When designing robust controllers, H-infinity synthesis is a common tool touse. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the (augmented) system, the problem becomes nonconvex and it is relatively hard to solve. These problems become very complex, even when the order of the system is low. The approach used in this work is based on formulating the constraint onthe maximum order of the controller as a polynomial (or rational) equation.This equality constraint is added to the optimization problem of minimizingan upper bound on the H-innity norm of the closed loop system subjectto linear matrix inequality (LMI) constraints. The problem is then solvedby reformulating it as a partially augmented Lagrangian problem where theequality constraint is put into the objective function, but where the LMIsare kept as constraints. The proposed method is evaluated together with two well-known methodsfrom the literature. The results indicate that the proposed method hascomparable performance in most cases, especially if the synthesized con-troller has many parameters, which is the case if the system to be controlledhas many input and output signals
Approximation, abstraction and decomposition in search and optimization
In this paper, I discuss four different areas of my research. One portion of my research has focused on automatic synthesis of search control heuristics for constraint satisfaction problems (CSPs). I have developed techniques for automatically synthesizing two types of heuristics for CSPs: Filtering functions are used to remove portions of a search space from consideration. Another portion of my research is focused on automatic synthesis of hierarchic algorithms for solving constraint satisfaction problems (CSPs). I have developed a technique for constructing hierarchic problem solvers based on numeric interval algebra. Another portion of my research is focused on automatic decomposition of design optimization problems. We are using the design of racing yacht hulls as a testbed domain for this research. Decomposition is especially important in the design of complex physical shapes such as yacht hulls. Another portion of my research is focused on intelligent model selection in design optimization. The model selection problem results from the difficulty of using exact models to analyze the performance of candidate designs
Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms
We present a straightforward source-to-source transformation that introduces
justifications for user-defined constraints into the CHR programming language.
Then a scheme of two rules suffices to allow for logical retraction (deletion,
removal) of constraints during computation. Without the need to recompute from
scratch, these rules remove not only the constraint but also undo all
consequences of the rule applications that involved the constraint. We prove a
confluence result concerning the rule scheme and show its correctness. When
algorithms are written in CHR, constraints represent both data and operations.
CHR is already incremental by nature, i.e. constraints can be added at runtime.
Logical retraction adds decrementality. Hence any algorithm written in CHR with
justifications will become fully dynamic. Operations can be undone and data can
be removed at any point in the computation without compromising the correctness
of the result. We present two classical examples of dynamic algorithms, written
in our prototype implementation of CHR with justifications that is available
online: maintaining the minimum of a changing set of numbers and shortest paths
in a graph whose edges change.Comment: Pre-proceedings paper presented at the 27th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur,
Belgium, 10-12 October 2017 (arXiv:1708.07854
Bounded Synthesis of Reactive Programs
Most algorithms for the synthesis of reactive systems focus on the
construction of finite-state machines rather than actual programs. This often
leads to badly structured, unreadable code. In this paper, we present a bounded
synthesis approach that automatically constructs, from a given specification in
linear-time temporal logic (LTL), a program in Madhusudan's simple imperative
language for reactive programs. We develop and compare two principal approaches
for the reduction of the synthesis problem to a Boolean constraint satisfaction
problem. The first reduction is based on a generalization of bounded synthesis
to two-way alternating automata, the second reduction is based on a direct
encoding of the program syntax in the constraint system. We report on
preliminary experience with a prototype implementation, which indicates that
the direct encoding outperforms the automata approach
Restoration of Poissonian Images Using Alternating Direction Optimization
Much research has been devoted to the problem of restoring Poissonian images,
namely for medical and astronomical applications. However, the restoration of
these images using state-of-the-art regularizers (such as those based on
multiscale representations or total variation) is still an active research
area, since the associated optimization problems are quite challenging. In this
paper, we propose an approach to deconvolving Poissonian images, which is based
on an alternating direction optimization method. The standard regularization
(or maximum a posteriori) restoration criterion, which combines the Poisson
log-likelihood with a (non-smooth) convex regularizer (log-prior), leads to
hard optimization problems: the log-likelihood is non-quadratic and
non-separable, the regularizer is non-smooth, and there is a non-negativity
constraint. Using standard convex analysis tools, we present sufficient
conditions for existence and uniqueness of solutions of these optimization
problems, for several types of regularizers: total-variation, frame-based
analysis, and frame-based synthesis. We attack these problems with an instance
of the alternating direction method of multipliers (ADMM), which belongs to the
family of augmented Lagrangian algorithms. We study sufficient conditions for
convergence and show that these are satisfied, either under total-variation or
frame-based (analysis and synthesis) regularization. The resulting algorithms
are shown to outperform alternative state-of-the-art methods, both in terms of
speed and restoration accuracy.Comment: 12 pages, 12 figures, 2 tables. Submitted to the IEEE Transactions on
Image Processin
ANALYSIS-BASED SPARSE RECONSTRUCTION WITH SYNTHESIS-BASED SOLVERS
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