A new bidirectional search algorithm with shortened postprocessing

For finding a shortest path in a network bidirectional A^∗ is a widely known algorithm. This algorithm distinguishes between the main phase and the postprocessing phase. The version of bidirectional A^∗ that is considered the most appropriate in literature hitherto, uses a so-called balanced heuristic estimate. This type of heuristic is chosen, as it accounts for a short postprocessing phase. In this paper, we do not restrict ourselves any longer to balanced heuristics. First, we introduce an algorithm containing a new method for the postprocessing phase, reducing this phase considerably for non-balanced heuristics. For a balanced heuristic the new algorithm is nearly equivalent to the existing versions of bidirectional A^∗. An obvious choice for a non-balanced heuristic turns out to be superior in terms of storage space and computation time. Second, we show that the main phase on its own, when using this non-balanced heuristic estimate, is a useful algorithm, which provides us quickly with a feasible approximation.     

A finite steepest-ascent algorithm for maximizing piecewise-linear concave functions

In this note, we describe a finitely convergent steepest-ascent scheme for maximizing piecewise-linear concave functions. Given any point, the algorithm moves along the direction of steepest ascent, that is, along the shortest subgradient, until a new ridge is reached. The overall process is then repeated by moving along the new steepest-ascent direction.     

Short correctness proofs for two self-stabilizing algorithms under the distributed daemon model

The distributed daemon model introduced by Burns in 1987 is a natural generalization of the central daemon model introduced by Dijkstra in 1974. In this paper, we show that a well-known shortest path algorithm is self-stabilizing under the distributed daemon model. Although this result has been proven only recently, the correctness proof provided here is from a different point of view and is much more concise. We also show that Bruell et al.’s center-finding algorithm is actually self-stabilizing under the distributed daemon model. Finally, we compute the worst-case stabilization times of the two algorithms under the distributed daemon model.     

L1 cheapest paths in “Fjord Scenery”

We show a simple proof of the existence of a path on the “border of water and rocks” based on combinatorial induction procedure and we present an algorithm for computing L₁ shortest path in “Fjord Scenery”. The proposed algorithm is a version of the Dijkstra technique adapted to a rectangle map with a square network. A few pre-processing modifications of the algorithm following from the combinatorial procedure are included. The validity of this approach is shown by numerical calculations for an example.     

A constructive proof of the Peter‐Weyl theorem

We present a new and constructive proof of the Peter‐Weyl theorem on the representations of compact groups. We use the Gelfand representation theorem for commutative C*‐algebras to give a proof which may be seen as a direct generalization of Burnside's algorithm [3]. This algorithm computes the characters of a finite group. We use this proof as a basis for a constructive proof in the style of Bishop. In fact, the present theory of compact groups may be seen as a natural continuation in the line of Bishop's work on locally compact, but Abelian, groups [2]. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A novel proof of the existence of solutions for a new system of generalized mixed quasi-variational-like inclusions involving (<Emphasis Type="Italic">A</Emphasis>, <Emphasis Type="Italic">η</Emphasis>, <Emphasis Type="Italic">m</Emphasis>)-accretive operators

In this paper, we introduce a new system of generalized mixed quasi-variational-like inclusions with (A, η, m)-accretive operators and relaxed cocoercive mappings. By using the fixed point theorem of Nadler, we prove the existence of solutions for this general system of generalized mixed quasi-variational-like inclusions and its special cases. The results in this paper unify, extend and improve some known results in the literature. The novel proof method is simpler than those iterative algorithm approach for proving the existence of solutions of all classes of system of set-valued variational inclusions in the literature.     

A Purely Geometric Approach to the Problem of Computing the Projection of a Point on a Simplex

In this short note, we present a new proof of a classical algorithm for finding the projection of a point on the standard simplex. The new proof is based only on elementary geometric arguments.     

A scatter-search-based learning algorithm for neural network training

In this article, we propose a new scatter-search-based learning algorithm to train feed-forward neural networks. The algorithm also incorporates elements of tabu search. We describe the elements of the new approach and test the new learning algorithm on a series of classification problems. The test results demonstrate that the algorithm is significantly superior to several implementations of back-propagation.     

Algorithms for time-dependent bicriteria shortest path problems

In this paper we generalize the classical shortest path problem in two ways. We consider two objective functions and time-dependent data. The resulting problem, called the time-dependent bicriteria shortest path problem (TdBiSP), has several interesting practical applications, but has not gained much attention in the literature. After reviewing relevant literature we develop a new algorithm for the TdBiSP with non-negative data. Numerical tests show the superiority of our algorithm compared with an existing algorithm in the literature. Furthermore, we discuss algorithms for the TdBiSP with negative travel times and costs.     

Point-to-point shortest paths on dynamic time-dependent road networks

This a summary of the author’s PhD thesis supervised by Leo Liberti, Philippe Baptiste and Daniel Krob and defended on 18 June 2009 at Ecole Polytechnique, Palaiseau, France. The thesis is written in English and is available at . The computation of point-to-point shortest paths in road networks has many practical applications that require very fast solution times, meaning that Dijkstra’s algorithm is not a viable option. In this work we develop an efficient algorithm to find the shortest route between two nodes of a large-scale, time-dependent graph, where we also allow the time-dependent arc cost functions to be updated at regular intervals. Furthermore, we propose a mathematical programming formulation for the shortest paths problem on time-dependent networks, that gives rise to integer programs. Within the context of solving Mixed-Integer Linear Programs through a Branch-and-Bound algorithm, we propose a new strategy for branching, mixing branching on single variables with branching on general hyperplanes. Finally, we introduce an effective heuristic for nonconvex Mixed-Integer Nonlinear Programss which combines VNS, Local Branching, NLP local search and Branch-and-Bound.     

New efficient shortest path simplex algorithm: pseudo permanent labels instead of permanent labels

We introduce a new network simplex pivot rule for the shortest path simplex algorithm. This new pivot rule chooses a subset of non-basic arcs to simultaneously enter into the basis. We call this operation a multiple pivot. We show that a shortest path simplex algorithm with this pivot rule performs O(n) multiple pivots and runs in O(nm) time. Our pivot rule is based on the new concept of a pseudo permanently labeled node, and it can be adapted to design a new label-correcting algorithm that runs in O(nm). Moreover, this concept lets us introduce new rules to identify negative cycles. Finally, we compare the network simplex algorithm with multiple pivots with other previously proposed efficient network simplex algorithm in a computational experiment.     

Computing shortest paths in networks derived from recurrence relations

Dynamic programming formulations of optimization problems often call for the computation of shortest paths in networks derived from recurrence relations. These derived networks tend to be very large, but they are also very regular and lend themselves to the computation of nontrivial lower bounds on path lengths. In this tutorial paper, we describe unidirectional and bidirectional search procedures that make use of bounding information in computing shortest paths. When applied to many optimization problems, these shortest path algorithms capture the advantages of both dynamic programming and branch-and-bound.     

Yet more frogs

Extending a recent paper by Derek Holton, we show how to represent the algorithm for the Frog Problem diagrammatically. This diagrammatic representation suggests a simpler proof of the symmetrical case (equal numbers of frogs of each colour) by allowing the even and odd cases to be treated together. It also provides a proof in the asymmetrical case (unequal numbers of frogs) as an extension of the symmetrical case. The issue of whether frogs of a given colour should be allowed to move in either direction is discussed. While it is possible to restrict to the case of movement in a single direction, results for bi-directional movement can be obtained by making the correspondence between the algorithm and its diagrammatic representation more concrete. The Frog Problem then becomes a form of constrained shortest path problem around the diagram, and from this point of view optimality of the algorithm becomes much clearer.     

Multiobjective shortest path problems with lexicographic goal-based preferences

Multiobjective shortest path problems are computationally harder than single objective ones. In particular, execution time is an important limiting factor in exact multiobjective search algorithms. This paper explores the possibility of improving search performance in those cases where the interesting portion of the Pareto front can be initially bounded. We introduce a new exact label-setting algorithm that returns the subset of Pareto optimal paths that satisfy a set of lexicographic goals, or the subset that minimizes deviation from goals if these cannot be fully satisfied. Formal proofs on the correctness of the algorithm are provided. We also show that the algorithm always explores a subset of the labels explored by a full Pareto search. The algorithm is evaluated over a set of problems with three objectives, showing a performance improvement of up to several orders of magnitude as goals become more restrictive.