A Weiszfeld algorithm for the solution of an asymmetric extension of the generalized Fermat location problem

AbstractThe Generalized Fermat Problem (in the plane) is: given n≥3 destination points find the point x̄∗ which minimizes the sum of Euclidean distances from x̄∗ to each of the destination points.The Weiszfeld iterative algorithm for this problem is globally convergent, independent of the initial guess. Also, a test is available, à priori, to determine when x̄∗ a destination point. This paper generalizes earlier work by the first author by introducing an asymmetric Euclidean distance in which, at each destination, the x-component is weighted differently from the y-component. A Weiszfeld algorithm is studied to compute x̄∗ and is shown to be a descent method which is globally convergent (except possibly for a denumerable number of starting points). Local convergence properties are characterized. When x̄∗ is not a destination point the iteration matrix at x̄∗ is shown to be convergent and local convergence is always linear. When x̄∗ is a destination point, local convergence can be linear, sub-linear or super-linear, depending upon a computable criterion. A test, which does not require iteration, for x̄∗ to be a destination, is derived. Comparisons are made between the symmetric and asymmetric problems. Numerical examples are given

L1-rotation averaging using the Weiszfeld algorithm

We consider the problem of rotation averaging under the L1 norm. This problem is related to the classic Fermat-Weber problem for finding the geometric median of a set of points in IRn. We apply the classical Weiszfeld algorithm to this problem, adapting it iteratively in tangent spaces of SO(3) to obtain a provably convergent algorithm for finding the L1 mean. This results in an extremely simple and rapid averaging algorithm, without the need for line search. The choice of L1 mean (also called geometric median) is motivated by its greater robustness compared with rotation averaging under the L2 norm (the usual averaging process). We apply this problem to both single-rotation averaging (under which the algorithm provably finds the global L1 optimum) and multiple rotation averaging (for which no such proof exists). The algorithm is demonstrated to give markedly improved results, compared with L2 averaging. We achieve a median rotation error of 0.82 degrees on the 595 images of the Notre Dame image set

The space of essential matrices as a Riemannian quotient manifold

The essential matrix, which encodes the epipolar constraint between points in two projective views, is a cornerstone of modern computer vision. Previous works have proposed different characterizations of the space of essential matrices as a Riemannian manifold. However, they either do not consider the symmetric role played by the two views, or do not fully take into account the geometric peculiarities of the epipolar constraint. We address these limitations with a characterization as a quotient manifold which can be easily interpreted in terms of camera poses. While our main focus in on theoretical aspects, we include applications to optimization problems in computer vision.This work was supported by grants NSF-IIP-0742304, NSF-OIA-1028009, ARL MAST-CTA W911NF-08-2-0004, and ARL RCTA W911NF-10-2-0016, NSF-DGE-0966142, and NSF-IIS-1317788

Stochastic Multifacility Location Problem under Triangular Area Constraint with Euclidean Norm

The multifacility location issue is an augmentation of the single-location problem in which we might be keen on finding the location of various new facilities concerning different existing locations. In the present study, multifacility location under triangular zone limitation with probabilistic methodology for the weights considered in the objective function and the Euclidean distances between the locations has been presented. Scientific detailing and the explanatory arrangement have been acquired by utilizing Kuhn-Tucker conditions. The arrangement strategy has been represented with the assistance of a numerical illustration. Two sub-instances of the issue in each of which the new locations are to be situated in semi-open rectangular zone have likewise been talked about

An allocation based modeling and solution framework for location problems with dense demand /

In this thesis we present a unified framework for planar location-allocation problems with dense demand. Emergence of such information technologies as Geographical Information Systems (GIS) has enabled access to detailed demand information. This proliferation of demand data brings about serious computational challenges for traditional approaches which are based on discrete demand representation. Furthermore, traditional approaches model the problem in location variable space and decide on the allocation decisions optimally given the locations. This is equivalent to prioritizing location decisions. However, when allocation decisions are more decisive or choice of exact locations is a later stage decision, then we need to prioritize allocation decisions. Motivated by these trends and challenges, we herein adopt a modeling and solution approach in the allocation variable space.Our approach has two fundamental characteristics: Demand representation in the form of continuous density functions and allocation decisions in the form of service regions. Accordingly, our framework is based on continuous optimization models and solution methods. On a plane, service regions (allocation decisions) assume different shapes depending on the metric chosen. Hence, this thesis presents separate approaches for two-dimensional Euclidean-metric and Manhattan-metric based distance measures. Further, we can classify the solution approaches of this thesis as constructive and improvement-based procedures. We show that constructive solution approach, namely the shooting algorithm, is an efficient procedure for solving both the single dimensional n-facility and planar 2-facility problems. While constructive solution approach is analogous for both metric cases, improvement approach differs due to the shapes of the service regions. In the Euclidean-metric case, a pair of service regions is separated by a straight line, however, in the Manhattan metric, separation takes place in the shape of three (at most) line segments. For planar 2-facility Euclidean-metric problems, we show that shape preserving transformations (rotation and translation) of a line allows us to design improvement-based solution approaches. Furthermore, we extend this shape preserving transformation concept to n-facility case via vertex-iteration based improvement approach and design first-order and second-order solution methods. In the case of planar 2-facility Manhattan-metric problems, we adopt translation as the shape-preserving transformation for each line segment and develop an improvement-based solution approach. For n-facility case, we provide a hybrid algorithm. Lastly, we provide results of a computational study and complexity results of our vertex-based algorithm

Facility location and related problems

PRINTAUSGABE IN HAUPTBIBLIOTHEK NICHT EINGELANGT! -- Bei Standortoptimierungsproblemen geht es um eine strategisch günstige Auswahl von Orten unter den Gesichtspunkten des Nutzens und der Aufwände, die mit den Standort-entscheidungen einhergehen. Beispielsweise können in der Planung die lageabhängigen Betriebskosten und die Errichtungskosten gegeneinander aufgewogen werden. Der zentrale Beitrag der vorliegenden Arbeit sind zwei Erweiterungen von Standortproblemen die durch einen Überblick klassischer Modelle eingefasst werden. Die eine Erweiterung behandelt ein dynamisches Warehouse-Location Problem in einem stochastischen Umfeld: Während mehrerer Perioden können Standorte geöffnet und geschlossen werden. Ziel ist die Minimierung der erwarteten Kosten die sich aus Betriebskosten, Produktionskosten, Transportkosten, Lagerhaltungskosten und Strafkosten bei Fehlmengen zusammensetzen. Ein exaktes und ein heuristisches Lösungsverfahren werden vorgestellt. Die zweite Erweiterung kann man als doppeltes Set-Cover Problem verstehen. Es sollen Kunden mit zwei Dienstleistungen bedient werden, die an Zentren gebunden sind. Jeder Kunde muss von mindestens einem Zentrum eines jeden Dienstleistungstyps erreichbar sein. Gleichzeitig ist darauf zu achten, dass die Anzahl verwendeter Zentren beschränkt ist und dass die Zentren höchstens einer Dienstleistung zugeordnet sind. Es werden verschiedene Anwendungen vorgestellt, und durch Einschränkungen wird versucht die Grenze zwischen Problemen mit polynomiellem Aufwand und NP-schweren Problemen zu ziehen. Im Rahmen einer bioinformatischen Anwendung wird eine Ant-Colony Metaheuristik eingesetzt.Facility location treats the problem of choosing locations while respecting effort and utility. E.g.: we can think of balancing the maintenance and setup costs for a facility. The central contribution of this work are two extensions of classical location models that get enclosed into the presentation of standard facility location models. One of the extensions is a dynamic warehouse location problem in a stochastic environment. Within a planning horizon of given number of periods we are able to open and close facilities and the aim is to minimize the expected costs. The costs consist of operating costs, production costs, inventory costs and penalty costs for shortages. We present an exact method and a heuristic approach. The second extension can be regarded as a double Set Cover Problem. We have to maintain two services by allocating corresponding sites and each customer has to be reachable by at least one of the centers and each service type. Simultaneously we have to respect that the number of used locations is limited, while no location is assigned to two services. We present different applications and by restricting the problem we draw the line between polynomially solvable problems and intractable ones. In the context of an application in bio-informatics we develop an ACO heuristic

Interior-point methods on manifolds: theory and applications

Interior-point methods offer a highly versatile framework for convex optimization that is effective in theory and practice. A key notion in their theory is that of a self-concordant barrier. We give a suitable generalization of self-concordance to Riemannian manifolds and show that it gives the same structural results and guarantees as in the Euclidean setting, in particular local quadratic convergence of Newton's method. We analyze a path-following method for optimizing compatible objectives over a convex domain for which one has a self-concordant barrier, and obtain the standard complexity guarantees as in the Euclidean setting. We provide general constructions of barriers, and show that on the space of positive-definite matrices and other symmetric spaces, the squared distance to a point is self-concordant. To demonstrate the versatility of our framework, we give algorithms with state-of-the-art complexity guarantees for the general class of scaling and non-commutative optimization problems, which have been of much recent interest, and we provide the first algorithms for efficiently finding high-precision solutions for computing minimal enclosing balls and geometric medians in nonpositive curvature.Comment: 85 pages. v2: Merged with independent work arXiv:2212.10981 by Hiroshi Hira

Classical and quantum algorithms for scaling problems

This thesis is concerned with scaling problems, which have a plethora of connections to different areas of mathematics, physics and computer science. Although many structural aspects of these problems are understood by now, we only know how to solve them efficiently in special cases.We give new algorithms for non-commutative scaling problems with complexity guarantees that match the prior state of the art. To this end, we extend the well-known (self-concordance based) interior-point method (IPM) framework to Riemannian manifolds, motivated by its success in the commutative setting. Moreover, the IPM framework does not obviously suffer from the same obstructions to efficiency as previous methods. It also yields the first high-precision algorithms for other natural geometric problems in non-positive curvature.For the (commutative) problems of matrix scaling and balancing, we show that quantum algorithms can outperform the (already very efficient) state-of-the-art classical algorithms. Their time complexity can be sublinear in the input size; in certain parameter regimes they are also optimal, whereas in others we show no quantum speedup over the classical methods is possible. Along the way, we provide improvements over the long-standing state of the art for searching for all marked elements in a list, and computing the sum of a list of numbers.We identify a new application in the context of tensor networks for quantum many-body physics. We define a computable canonical form for uniform projected entangled pair states (as the solution to a scaling problem), circumventing previously known undecidability results. We also show, by characterizing the invariant polynomials, that the canonical form is determined by evaluating the tensor network contractions on networks of bounded size