4,791 research outputs found
Technique to eliminate computational instability in multibody simulations employing the Lagrange multiplier
A programming technique to eliminate computational instability in multibody simulations that use the Lagrange multiplier is presented. The computational instability occurs when the attached bodies drift apart and violate the constraints. The programming technique uses the constraint equation, instead of integration, to determine the coordinates that are not independent. Although the equations of motion are unchanged, a complete derivation of the incorporation of the Lagrange multiplier into the equation of motion for two bodies is presented. A listing of a digital computer program which uses the programming technique to eliminate computational instability is also presented. The computer program simulates a solid rocket booster and parachute connected by a frictionless swivel
A Symmetric Approach to the Massive Nonlinear Sigma Model
In the present paper we extend to the massive case the procedure of
divergences subtraction, previously introduced for the massless nonlinear sigma
model (D=4). Perturbative expansion in the number of loops is successfully
constructed. The resulting theory depends on the Spontaneous Symmetry Breaking
parameter v, on the mass m and on the radiative correction parameter \Lambda.
Fermions are not considered in the present work. SU(2) X SU(2) is the group
used.Comment: 20 page
Methodological Issues in a Real-Time Study of Glaswegian Vowels: Automation and Comparability
No abstract available
An overview of Stackelberg pricing in networks
The Stackelberg pricing problem has two levels of decision making: tariff setting by an operator, and then selection of the cheapest alternative by customers. In the network version, an operator determines tariffs on a subset of the arcs that he owns. Customers, who wish to connect two vertices with a path of a certain capacity, select the cheapest path. The revenue for the operator is determined by the tariff and the amount of usage of his arcs. The most natural model for the problem is a (bilinear) bilevel program, where the upper level problem is the pricing problem of the operator, and the lower level problem is a shortest path problem for each of the customers. This paper contains a compilation of theoretical and algorithmic results on the network Stackelberg pricing problem. The description of the theory and algorithms is generally informal and intuitive. We redefine the underlying network of the problem, to obtain a compact representation. Then we describe a basic branch-and-bound enumeration procedure. Both concepts are used for complexity issues and for the development of algorithms: establishing NP-hardness, approximability, special cases solvable in polynomial time, and an efficient exact branch-and-bound algorithm.Economics ;
Polynomial cases of the tarification problem
We consider the problem of determining a set of optimal tariffs for an agent in a network, who owns a subset of the arcs of the network, and who wishes to maximize his revenues on this subset from a set of clients that make use of the network.The general variant of this problem is NP-hard, already with a single client. This paper introduces several new polynomially solvable special cases. An important case is the following.For multiple clients, if the number of tariff arcs is bounded from above, we can solve the problem by a polynomial number of linear programs (each of which is of polynomial size). Furthermore, we show that the parametric tarification problem and the single arc fixed charge tarification problem can be solved in polynomial time.Economics ;
An overview of Stackelberg pricing in networks
The Stackelberg pricing problem has two levels of decision making: tariff setting by an operator, and then selection of the cheapest alternative by customers. In the network version, an operator determines tariffs on a subset of the arcs that he owns. Customers, who wish to connect two vertices with a path of a certain capacity, select the cheapest path. The revenue for the operator is determined by the tariff and the amount of usage of his arcs. The most natural model for the problem is a (bi-linear) bilevel program, where the upper level problem is the pricing problem of the operator, and the lower level problem is a shortest path problem for each of the customers. This manuscript contains a compilation of theoretical and algorithmic results on the Stackelberg pricing problem. The description of the theory and algorithms is generally informal and intuitive. We redefine the underlying network of the problem, to obtain a compact representation. Then, we describe a basic branch-and-bound enumeration procedure. Both concepts are used for complexity issues and the development of algorithms: establishing NP-hardness, approximability, and polynomially solvable cases, and an efficient exact branch-and-bound algorithm.mathematical applications;
Verifying Monadic Second-Order Properties of Graph Programs
The core challenge in a Hoare- or Dijkstra-style proof system for graph
programs is in defining a weakest liberal precondition construction with
respect to a rule and a postcondition. Previous work addressing this has
focused on assertion languages for first-order properties, which are unable to
express important global properties of graphs such as acyclicity,
connectedness, or existence of paths. In this paper, we extend the nested graph
conditions of Habel, Pennemann, and Rensink to make them equivalently
expressive to monadic second-order logic on graphs. We present a weakest
liberal precondition construction for these assertions, and demonstrate its use
in verifying non-local correctness specifications of graph programs in the
sense of Habel et al.Comment: Extended version of a paper to appear at ICGT 201
Graph Subsumption in Abstract State Space Exploration
In this paper we present the extension of an existing method for abstract
graph-based state space exploration, called neighbourhood abstraction, with a
reduction technique based on subsumption. Basically, one abstract state
subsumes another when it covers more concrete states; in such a case, the
subsumed state need not be included in the state space, thus giving a
reduction. We explain the theory and especially also report on a number of
experiments, which show that subsumption indeed drastically reduces both the
state space and the resources (time and memory) needed to compute it.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611
A Probabilistic Tabu Search Algorithm for the Generalized Minimum Spanning Tree Problem
In this paper we present a probabilistic tabu search algorithm for the generalized minimum spanning tree problem. The basic idea behind the algorithm is to use preprocessing operations to arrive at a probability value for each vertex which roughly corresponds to its probability of being included in an optimal solution, and to use such probability values to shrink the size of the neighborhood of solutions to manageable proportions. We report results from computational experiments that demonstrate the superiority of this method over the generic tabu search method.
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