scholarly journals Pattern-Based Approach to the Workflow Satisfiability Problem with User-Independent Constraints

2019 ◽  
Vol 66 ◽  
pp. 85-122 ◽  
Author(s):  
Daniel Karapetyan ◽  
Andrew J. Parkes ◽  
Gregory Gutin ◽  
Andrei Gagarin
Keyword(s):  
Computational Study ◽  
Empirical Evaluation ◽  
Practical Interest ◽  
General Purpose ◽  
Satisfiability Problem ◽  
Polynomial Space ◽  
Fixed Parameter Tractable ◽  
Fpt Algorithms ◽  
Fpt Algorithm ◽  
New Interpretation

The fixed parameter tractable (FPT) approach is a powerful tool in tackling computationally hard problems.  In this paper, we link FPT results to classic artificial intelligence (AI) techniques to show how they complement each other.  Specifically, we consider the workflow satisfiability problem (WSP) which asks whether there exists an assignment of authorised users to the steps in a workflow specification, subject to certain constraints on the assignment.  It was shown by Cohen et al. (JAIR 2014) that WSP restricted to the class of user-independent constraints (UI), covering many practical cases, admits FPT algorithms, i.e. can be solved in time exponential only in the number of steps k and polynomial in the number of users n.  Since usually k << n in WSP, such FPT algorithms are of great practical interest. We present a new interpretation of the FPT nature of the WSP with UI constraints giving a decomposition of the problem into two levels.  Exploiting this two-level split, we develop a new FPT algorithm that is by many orders of magnitude faster than the previous state-of-the-art WSP algorithm and also has only polynomial-space complexity.  We also introduce new pseudo-Boolean (PB) and Constraint Satisfaction (CSP) formulations of the WSP with UI constraints which efficiently exploit this new decomposition of the problem and raise the novel issue of how to use general-purpose solvers to tackle FPT problems in a fashion that meets FPT efficiency expectations.  In our computational study, we investigate, for the first time, the phase transition (PT) properties of the WSP, under a model for generation of random instances.  We show how PT studies can be extended, in a novel fashion, to support empirical evaluation of scaling of FPT algorithms.

scholarly journals Iterative Plan Construction for the Workflow Satisfiability Problem

2014 ◽  
Vol 51 ◽  
pp. 555-577 ◽  
Author(s):  
D. Cohen ◽  
J. Crampton ◽  
A. Gagarin ◽  
G. Gutin ◽  
M. Jones
Keyword(s):  
Constraint Satisfaction Problem ◽  
Practical Interest ◽  
Satisfiability Problem ◽  
Business Rules ◽  
Generic Algorithm ◽  
Fixed Parameter Tractable ◽  
Novel Approach ◽  
Constraint Language ◽  
Fixed Parameter ◽  
Language User

The Workflow Satisfiability Problem (WSP) is a problem of practical interest that arises whenever tasks need to be performed by authorized users, subject to constraints defined by business rules. We are required to decide whether there exists a plan - an assignment of tasks to authorized users - such that all constraints are satisfied. It is natural to see the WSP as a subclass of the Constraint Satisfaction Problem (CSP) in which the variables are tasks and the domain is the set of users. What makes the WSP distinctive is that the number of tasks is usually very small compared to the number of users, so it is appropriate to ask for which constraint languages the WSP is fixed-parameter tractable (FPT), parameterized by the number of tasks. This novel approach to the WSP, using techniques from CSP, has enabled us to design a generic algorithm which is FPT for several families of workflow constraints considered in the literature. Furthermore, we prove that the union of FPT languages remains FPT if they satisfy a simple compatibility condition. Lastly, we identify a new FPT constraint language, user-independent constraints, that includes many of the constraints of interest in business processing systems. We demonstrate that our generic algorithm has provably optimal running time O*(2^(klog k)), for this language, where k is the number of tasks.

scholarly journals On Structural Parameterizations of the Edge Disjoint Paths Problem

Algorithmica ◽  
2021 ◽  
Author(s):  
Robert Ganian ◽  
Sebastian Ordyniak ◽  
M. S. Ramanujan
Keyword(s):  
Polynomial Time ◽  
Disjoint Paths ◽  
Structural Parameter ◽  
Input Graph ◽  
Feedback Vertex Set ◽  
Fpt Algorithms ◽  
Edge Disjoint ◽  
Fpt Algorithm ◽  
Vertex Set ◽  
Terminal Pair

AbstractIn this paper we revisit the classical edge disjoint paths (EDP) problem, where one is given an undirected graph G and a set of terminal pairs P and asks whether G contains a set of pairwise edge-disjoint paths connecting every terminal pair in P. Our focus lies on structural parameterizations for the problem that allow for efficient (polynomial-time or FPT) algorithms. As our first result, we answer an open question stated in Fleszar et al. (Proceedings of the ESA, 2016), by showing that the problem can be solved in polynomial time if the input graph has a feedback vertex set of size one. We also show that EDP parameterized by the treewidth and the maximum degree of the input graph is fixed-parameter tractable. Having developed two novel algorithms for EDP using structural restrictions on the input graph, we then turn our attention towards the augmented graph, i.e., the graph obtained from the input graph after adding one edge between every terminal pair. In constrast to the input graph, where EDP is known to remain -hard even for treewidth two, a result by Zhou et al. (Algorithmica 26(1):3--30, 2000) shows that EDP can be solved in non-uniform polynomial time if the augmented graph has constant treewidth; we note that the possible improvement of this result to an FPT-algorithm has remained open since then. We show that this is highly unlikely by establishing the [1]-hardness of the problem parameterized by the treewidth (and even feedback vertex set) of the augmented graph. Finally, we develop an FPT-algorithm for EDP by exploiting a novel structural parameter of the augmented graph.

A Computational Study of the Interaction of Melting Soil With Moisture Transport in the Native Soil

1998 ◽  
Author(s):  
Will Schreiber ◽  
John Kuo
Keyword(s):  
Heat Transfer ◽  
Heat Flow ◽  
Moisture Transport ◽  
Moving Boundary ◽  
Computational Study ◽  
Fluid Motion ◽  
Computer Code ◽  
General Purpose ◽  
Simple Method ◽  
Porous Soil

Abstract The current paper describes a computer model designed to analyze the moisture transport in the unmelted, porous soil neighboring a convecting melt. The time-dependent fluid and heat flow in the soil melt is simulated implicitly using the SIMPLE method generalized to predict viscous fluid motion and heat transfer on boundary-fitted, non-orthogonal coordinates which adapt with time. TOUGH2, a general-purpose computer code for multiphase fluid and heat flow developed by K. Pruess at Lawrence Berkekey Laboratory, has been modified for use on time-adaptive, boundary-fitted coordinates to predict heat transfer, moisture and air transport, and pressure distribution in the porous, unmelted soil. The soil melt model is coupled with the modified TOUGH2 model via an interface (moving boundary) whose shape is determined implicitly with the progression of time. The computer model’s utility is demonstrated in the present study with a special two-dimensional study. A soil initially at 20°C and partially-saturated with either a 0.2 or 0.5 relative liquid saturation is contained in a box two meters wide by ten meters high with impermeable bottom and sides. The upper surface of the soil is exposed to a 20°C atmosphere to which vapor and air can escape. Computation begins when the soil, which melts at 1700°C, is heated from one side (maintained at constant temperatures ranging from 1700°C to 4000°C). Heat from the hot wall causes the melt to circulate in such a way that the melt interface grows more rapidly at the top of the box than at the bottom. As the upper portion of the melt approaches the impermeable wall it creates a bottle neck for moisture release from the soil’s lower regions. The pressure history of the trapped moisture is examined as a means for predicting the potential for moisture penetration into the melt. The melt’s interface movement and moisture transport in the unmelted, porous soil are also examined.

Conflict Analysis for MINLP

2021 ◽  
Author(s):  
Timo Berthold ◽  
Jakob Witzig
Keyword(s):  
Nonlinear Optimization ◽  
Branch And Bound ◽  
Computational Study ◽  
General Purpose ◽  
Conflict Analysis ◽  
Mixed Integer ◽  
Mixed Integer Program ◽  
Global Optimality ◽  
Nonlinear Programs ◽  
The Impact

The generalization of mixed integer program (MIP) techniques to deal with nonlinear, potentially nonconvex, constraints has been a fruitful direction of research for computational mixed integer nonlinear programs (MINLPs) in the last decade. In this paper, we follow that path in order to extend another essential subroutine of modern MIP solvers toward the case of nonlinear optimization: the analysis of infeasible subproblems for learning additional valid constraints. To this end, we derive two different strategies, geared toward two different solution approaches. These are using local dual proofs of infeasibility for LP-based branch-and-bound and the creation of nonlinear dual proofs for NLP-based branch-and-bound, respectively. We discuss implementation details of both approaches and present an extensive computational study, showing that both techniques can significantly enhance performance when solving MINLPs to global optimality. Summary of Contribution: This original article concerns the advancement of exact general-purpose algorithms for solving one of the largest and most prominent problem classes in optimization, mixed integer nonlinear programs (MINLPs). It demonstrates how methods for conflict analysis that learn from infeasible subproblems can be transferred to nonlinear optimization. Further, it develops theory for how nonlinear dual infeasibility proofs can be derived from a nonlinear relaxation. This paper features a thoroughly computational study regarding the impact of conflict analysis techniques on the overall performance of a state-of-the-art MINLP solver when solving MINLPs to global optimality.

scholarly journals A study of the flow-field evolution and mixing in a planar turbulent jet using direct numerical simulation

2002 ◽  
Vol 450 ◽  
pp. 377-407 ◽  
Author(s):  
S. A. STANLEY ◽  
S. SARKAR ◽  
J. P. MELLADO
Keyword(s):  
Numerical Simulation ◽  
Flow Field ◽  
Direct Numerical Simulation ◽  
Large Scale ◽  
Computational Study ◽  
Practical Interest ◽  
Small Scale ◽  
Mixing Process ◽  
Mean Velocity ◽  
Small Scales

Turbulent plane jets are prototypical free shear flows of practical interest in propulsion, combustion and environmental flows. While considerable experimental research has been performed on planar jets, very few computational studies exist. To the authors' knowledge, this is the first computational study of spatially evolving three-dimensional planar turbulent jets utilizing direct numerical simulation. Jet growth rates as well as the mean velocity, mean scalar and Reynolds stress profiles compare well with experimental data. Coherency spectra, vorticity visualization and autospectra are obtained to identify inferred structures. The development of the initial shear layer instability, as well as the evolution into the jet column mode downstream is captured well.The large- and small-scale anisotropies in the jet are discussed in detail. It is shown that, while the large scales in the flow field adjust slowly to variations in the local mean velocity gradients, the small scales adjust rapidly. Near the centreline of the jet, the small scales of turbulence are more isotropic. The mixing process is studied through analysis of the probability density functions of a passive scalar. Immediately after the rollup of vortical structures in the shear layers, the mixing process is dominated by large-scale engulfing of fluid. However, small-scale mixing dominates further downstream in the turbulent core of the self-similar region of the jet and a change from non-marching to marching PDFs is observed. Near the jet edges, the effects of large-scale engulfing of coflow fluid continue to influence the PDFs and non-marching type behaviour is observed.

An FPT Algorithm for the Correlation Clustering Problem

2011 ◽  
Vol 474-476 ◽  
pp. 924-927 ◽  
Author(s):  
Xiao Xin
Keyword(s):  
Approximation Algorithms ◽  
Polynomial Time ◽  
Undirected Graph ◽  
Fixed Parameter Tractable ◽  
Correlation Clustering ◽  
Time Approximation ◽  
Running Time ◽  
Clustering Problem ◽  
Fpt Algorithm ◽  
Fixed Parameter

Given an undirected graph G=(V, E) with real nonnegative weights and + or – labels on its edges, the correlation clustering problem is to partition the vertices of G into clusters to minimize the total weight of cut + edges and uncut – edges. This problem is APX-hard and has been intensively studied mainly from the viewpoint of polynomial time approximation algorithms. By way of contrast, a fixed-parameter tractable algorithm is presented that takes treewidth as the parameter, with a running time that is linear in the number of vertices of G.

scholarly journals Optimal Surveillance of Covert Networks by Minimizing Inverse Geodesic Length

2019 ◽  
Vol 33 ◽  
pp. 533-540
Author(s):  
Serge Gaspers ◽  
Kamran Najeebullah
Keyword(s):  
Network Performance ◽  
Vertex Cover ◽  
Quadratic Program ◽  
Edge Deletion ◽  
Fixed Parameter Tractable ◽  
Fpt Algorithms ◽  
Neighborhood Diversity ◽  
Geodesic Length ◽  
Split Graphs ◽  
Covert Networks

The inverse geodesic length (IGL) is a well-known and widely used measure of network performance. It equals the sum of the inverse distances of all pairs of vertices. In network analysis, IGL of a network is often used to assess and evaluate how well heuristics perform in strengthening or weakening a network. We consider the edge-deletion problem MINIGLED. Formally, given a graph G, a budget k, and a target inverse geodesic length T, the question is whether there exists a subset of edges X with |X| ≤ ck, such that the inverse geodesic length of G − X is at most T.In this paper, we design algorithms and study the complexity of MINIGL-ED. We show that it is NP-complete and cannot be solved in subexponential time even when restricted to bipartite or split graphs assuming the Exponential Time Hypothesis. In terms of parameterized complexity, we consider the problem with respect to various parameters. We show that MINIGL-ED is fixed-parameter tractable for parameter T and vertex cover by modeling the problem as an integer quadratic program. We also provide FPT algorithms parameterized by twin cover and neighborhood diversity combined with the deletion budget k. On the negative side we show that MINIGL-ED is W[1]-hard for parameter tree-width.

An Experimental and Computational Study of Temperature and Strain Fields in Metal Cutting

2001 ◽  
Author(s):  
Alan T. Zehnder ◽  
Yogesh K. Potdar ◽  
Xiaomin Deng ◽  
Chandrakant Shet
Keyword(s):  
Spatial Resolution ◽  
Metal Cutting ◽  
Computational Study ◽  
Orthogonal Cutting ◽  
Critical Role ◽  
Rake Angle ◽  
General Purpose ◽  
Temperature Fields ◽  
Ir Detectors ◽  
Strain Fields

Abstract Metal cutting is a thermo-mechanically coupled process in which plasticity induced heating and friction play a critical role. In this paper, we outline a methodology that combines high resolution experiments with numerical simulations. The simulations were performed with a general purpose finite element code. With this code we evaluate the effects of chip-tool interface friction and rake angle on temperature and cutting force and show that results for residual stresses in the workpiece are consistent with experimental data. We hypothesize that by closely coupling simulations to fine scale spatial and temporal experimental measurements of temperature and strain fields, questions related to choice of parameters in FE simulations can be resolved. We have designed and conducted orthogonal cutting experiments to measure temperatures, using IR detectors, with a spatial resolution of 27 × 27 μm and time scale of 200 ns. Experimentally obtained temperature fields are compared with FE results. We also obtain deformation fields with a spatial resolution of 50 × 50 μm.

scholarly journals Conflict-Driven Heuristics for Mixed Integer Programming

2020 ◽  
Author(s):  
Jakob Witzig ◽  
Ambros Gleixner
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
State Of The Art ◽  
Computational Study ◽  
Search Tree ◽  
General Purpose ◽  
Conflict Analysis ◽  
Mixed Integer ◽  
Linear Programs ◽  
Feasible Solutions

Two essential ingredients of modern mixed-integer programming solvers are diving heuristics, which simulate a partial depth-first search in a branch-and-bound tree, and conflict analysis, which learns valid constraints from infeasible subproblems. So far, these techniques have mostly been studied independently: primal heuristics for finding high-quality feasible solutions early during the solving process and conflict analysis for fathoming nodes of the search tree and improving the dual bound. In this paper, we pose the question of whether and how the orthogonal goals of proving infeasibility and generating improving solutions can be pursued in a combined manner such that a state-of-the-art solver can benefit. To do so, we integrate both concepts in two different ways. First, we develop a diving heuristic that simultaneously targets the generation of valid conflict constraints from the Farkas dual and the generation of improving solutions. We show that, in the primal, this is equivalent to the optimistic strategy of diving toward the best bound with respect to the objective function. Second, we use information derived from conflict analysis to enhance the search of a diving heuristic akin to classic coefficient diving. In a detailed computational study, both methods are evaluated on the basis of an implementation in the source-open-solver SCIP. The experimental results underline the potential of combining both diving heuristics and conflict analysis. Summary of Contribution. This original article concerns the advancement of exact general-purpose algorithms for solving one of the largest and most prominent problem classes in optimization, mixed-integer linear programs. It demonstrates how methods for conflict analysis that learn from infeasible subproblems can be combined successfully with diving heuristics that aim at finding primal solutions. For two newly designed diving heuristics, this paper features a thoroughly computational study regarding their impact on the overall performance of a state-of-the-art MIP solver.

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