| 1. |
- Anistratov, Pavel, 1990-, et al.
(författare)
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Autonomous-Vehicle Maneuver Planning Using Segmentation and the Alternating Augmented Lagrangian Method
- 2020
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Ingår i: 21th IFAC World Congress Proceedings. - : Elsevier. ; 53:2, s. 15558-15565
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Konferensbidrag (refereegranskat)abstract
- Segmenting a motion-planning problem into smaller subproblems could be beneficial in terms of computational complexity. This observation is used as a basis for a new sub-maneuver decomposition approach investigated in this paper in the context of optimal evasive maneuvers for autonomous ground vehicles. The recently published alternating augmented Lagrangianmethod is adopted and leveraged on, which turns out to fit the problem formulation with several attractive properties of the solution procedure. The decomposition is based on moving the coupling constraints between the sub-maneuvers into a separate coordination problem, which is possible to solve analytically. The remaining constraints and the objective function are decomposed into subproblems, one for each segment, which means that parallel computation is possible and benecial. The method is implemented and evaluated in a safety-critical double lane-change scenario. By using the solution of a low-complexity initialization problem and applying warm-start techniques in the optimization, a solution is possible to obtain after just a few alternating iterations using the developed approach. The resulting computational time is lower than solving one optimization problem for the full maneuver.
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| 2. |
- Andersson, Mats, et al.
(författare)
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Sparsity Optimization in Design of Multidimensional Filter Networks
- 2013
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Filter networks is a powerful tool used for reducing the image processing time, while maintaining its reasonably high quality.They are composed of sparse sub-filters whose low sparsity ensures fast image processing.The filter network design is related to solvinga sparse optimization problem where a cardinality constraint bounds above the sparsity level.In the case of sequentially connected sub-filters, which is the simplest network structure of those considered in this paper, a cardinality-constrained multilinear least-squares (MLLS) problem is to be solved. If to disregard the cardinality constraint, the MLLS is typically a large-scale problem characterized by a large number of local minimizers. Each of the local minimizers is singular and non-isolated.The cardinality constraint makes the problem even more difficult to solve.An approach for approximately solving the cardinality-constrained MLLS problem is presented.It is then applied to solving a bi-criteria optimization problem in which both thetime and quality of image processing are optimized. The developed approach is extended to designing filter networks of a more general structure. Its efficiency is demonstrated by designing certain 2D and 3D filter networks. It is also compared with the existing approaches.
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| 3. |
- Andersson, Mats, et al.
(författare)
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Global search strategies for solving multilinear least-squares problems
- 2012
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Ingår i: Sultan Qaboos University Journal for Science. - : Sultan Qaboos University. - 1027-524X. ; 17:1, s. 12-21
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Tidskriftsartikel (refereegranskat)abstract
- The multilinear least-squares (MLLS) problem is an extension of the linear leastsquares problem. The difference is that a multilinear operator is used in place of a matrix-vector product. The MLLS is typically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows for moving from one local minimizer to a better one. The efficiency of this strategy is illustrated by results of numerical experiments performed for some problems related to the design of filter networks.
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| 4. |
- Andersson, Mats, et al.
(författare)
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Global Search Strategies for Solving Multilinear Least-squares Problems
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The multilinear least-squares (MLLS) problem is an extension of the linear least-squares problem. The difference is that a multilinearoperator is used in place of a matrix-vector product. The MLLS istypically a large-scale problem characterized by a large number of local minimizers. It originates, for instance, from the design of filter networks. We present a global search strategy that allows formoving from one local minimizer to a better one. The efficiencyof this strategy isillustrated by results of numerical experiments performed forsome problems related to the design of filter networks.
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| 5. |
- Hoppmann-Baum, Kai, et al.
(författare)
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Length-Constrained Cycle Partition with an Application to UAV Routing
- 2020
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this article, we discuss the Length-Constrained Cycle Partition Problem (LCCP). Besides edge weights, the undirected graph in LCCP features an individual critical weight value for each vertex. A cycle partition, i.e., a vertex disjoint cycle cover, is a feasible solution if the length of each cycle is not greater than the critical weight of each of the vertices in the cycle. The goal is to find a feasible partition with the minimum number of cycles. In this article, we discuss theoretical properties, preprocessing techniques, and two mixed-integer programming models (MIP) for LCCP both inspired by formulations for the closely related Travelling Salesperson Problem (TSP). Further, we introduce conflct hypergraphs, whose cliques yield valid constraints for the MIP models. We conclude with a report on computational experiments conducted on (A)TSPLIB-based instances. As an example, we use a routing problem in which a set of uncrewed aerial vehicles (UAVs) patrols a set of areas.
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| 6. |
- Hoppmann, Kai, et al.
(författare)
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Minimum Cycle Partition with Length Requirements
- 2020
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Ingår i: Integration of Constraint Programming, Artificial Intelligence, and Operations Research. - Cham : Springer International Publishing. - 9783030589417 ; , s. 273-282
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Konferensbidrag (refereegranskat)abstract
- In this article we introduce a Minimum Cycle Partition Problem with Length Requirements (CPLR). This generalization of the Travelling Salesman Problem (TSP) originates from routing Unmanned Aerial Vehicles (UAVs). Apart from nonnegative edge weights, CPLR has an individual critical weight value associated with each vertex. A cycle partition, i.e., a vertex disjoint cycle cover, is regarded as a feasible solution if the length of each cycle, which is the sum of the weights of its edges, is not greater than the critical weight of each of its vertices. The goal is to find a feasible partition, which minimizes the number of cycles. In this article, a heuristic algorithm is presented together with a Mixed Integer Programming (MIP) formulation of CPLR. We furthermore introduce a conflict graph, whose cliques yield valid constraints for the MIP model. Finally, we report on computational experiments conducted on TSPLIB-based test instances.
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| 7. |
- Hussian, Mohamed, 1969-, et al.
(författare)
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Monotonic regression for assessment of trends in environmental quality data
- 2004
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Ingår i: European Congress on Computational Methods in Applied Sciences and Engineering ECCOMAS. - Jyväskylä : University of Jyväskylä, Department of Mathematical Information Technology. - 9513918688 ; , s. 1-12
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Konferensbidrag (refereegranskat)abstract
- Monotonic regression is a non-parametric method that is designed especially for applications in which the expected value of a response variable increases or decreases in one or more explanatory variables. Here, we show how the recently developed generalised pool-adjacent-violators (GPAV) algorithm can greatly facilitate the assessment of trends in time series of environmental quality data. In particular, we present new methods for simultaneous extraction of a monotonic trend and seasonal components, and for normalisation of environmental quality data that are influenced by random variation in weather conditions or other forms of natural variability. The general aim of normalisation is to clarify the human impact on the environment by suppressing irrelevant variation in the collected data. Our method is designed for applications that satisfy the following conditions: (i) the response variable under consideration is a monotonic function of one or more covariates; (ii) the anthropogenic temporal trend is either increasing or decreasing; (iii) the seasonal variation over a year can be defined by one increasing and one decreasing function. Theoretical descriptions of our methodology are accompanied by examples of trend assessments of water quality data and normalisation of the mercury concentration in cod muscle in relation to the length of the analysed fish.
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