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检索条件"主题词=primal-dual algorithms"

共 100 条记录，以下是81-90 订阅

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Directional Total Variation Based Image Deconvolution with Unknown Boundaries 17th

Directional Total Variation Based Image Deconvolution with U...

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17th International Conference on Computer Analysis of Images and Patterns (CAIP)

作者： Demircan-Tureyen, Ezgi Kamasak, Mustafa E. Istanbul Kultur Univ Fac Engn Dept Comp Engn TR-34156 Istanbul Turkey Istanbul Tech Univ Fac Comp & Informat Dept Comp Engn TR-34390 Istanbul Turkey

ISBN: (纸本)9783319646985;9783319646978

Like many other imaging inverse problems, image deconvolution suffers from ill-posedness and needs for an adequate regularization. Total variation (TV) is an effective regularizer;hence, frequently used in such problems. Various anisotropic alternatives to isotropic TV have also been proposed to capture different characteristics in the image. Directional total variation (DTV) is such an instance, which is convex, has the ability to capture the smooth boundaries as conventional TV does, and also handles the directional dominance by enforcing piecewice constancy through a direction. In this paper, we solve the deconvolution problem under DTV regularization, by using simple forward-backward splitting machinery. Besides, there are two bottlenecks of the deconvolution problem, that need to be addressed;one is the computational load revealed due to matrix inversions, second is the unknown boundary conditions (BCs). We tackle with the former one by switching to the frequency domain using fast Fourier transform (FFT), and the latter one by iteratively estimating a boundary zone to surrounder the blurred image by plugging a recently proposed framework into our algorithm. The proposed approach is evaluated in terms of the reconstruction quality and the speed. The results are compared to a very recent TV-based deconvolution algorithm, which uses a "partial" alternating direction method of multipliers (ADMM) as the optimization tool, by also plugging the same framework to cope with the unknown BCs.

关键词： Deconvolution Deblurring Inpainting Convex optimization Directional total variation Image reconstruction primal-dual algorithms

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A superlinearly convergent primal - dual algorithm model for constrained optimization problems with bounded variables

A superlinearly convergent primal - Dual algorithm model for...

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International Conference on Nonlinear Programming and Variational Inequalities

作者： Di Pillo, G Lucidi, S Palagi, L Univ Rome La Sapienza Dipartimento Informat & Sistemist I-00185 Rome Italy

In this paper we introduce a Newton-type algorithm model for solving smooth nonlinear optimization problems with general constraints and bound constraints on the variables. Under very mild assumptions and without requiring the strict complementarity assumption, the algorithm produces a sequence of pairs {(x(k), gimel (k))} converging quadratically to ((x) over bar, <()over bar>), where (x ) over bar is the solution of the problem and gimel is the KKT multiplier associated with the general constraints. As regards the behaviour of the sequence {x(k)} alone, it converges at least superlinearly. A distinguishing feature of the proposed algorithm is that it exploits the particular structure of the constraints of the optimization problem so as to limit the computational burden as much as possible. In fact, at each iteration, it requires only the solution of a linear system whose dimension is equal at most to the number of variables plus the number of the general constraints. Hence, the proposed algorithm model may be well suited to tackle large scale problems. Even though the analysis is concerned mainly with the local behavior of the algorithm, we also suggest a way of forcing the global convergence.

关键词： nonlinear programming primal-dual algorithms bound constraints

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Control Reconfiguration for Improved Performance via Reverse-engineering and Forward-engineering 21st

Control Reconfiguration for Improved Performance via Reverse...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Shu, Han Zhang, Xuan Li, Na Papachristodoulou, Antonis Tsinghua Berkeley Shenzhen Inst Shenzhen 518055 Guangdong Peoples R China Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Univ Oxford Dept Engn Sci Oxford England

This paper presents a control redesign approach to improve the performance of a certain class of dynamical systems. Motivated by recent research on re-engineering cyber-physical systems, we propose a three-step control retrofit procedure. First, we reverse-engineer a dynamical system to dig out an optimization problem it actually solves. Second, we apply an augmented Lagrangian or a hat-x method to solve this optimization problem. Finally, by comparing the original and new dynamics, we obtain the implementation of the redesigned part (i.e., the extra dynamics). As a result, the convergence rate/speed or transient behavior of the given system can be improved while the system structure remains. To show the effectiveness of the proposed approach and its potential applications, we present two practical examples including Internet congestion control and distributed proportional-integral (PI) control. Copyright (C) 2020 The Authors.

关键词： Control redesign reverse-engineering optimization primal-dual algorithms augmented Lagrangian

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A primal-dual Algorithm for Higher-Order Multilabel Markov Random Fields 27

A Primal-Dual Algorithm for Higher-Order Multilabel Markov R...

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27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Fix, Alex Wang, Chen Zabih, Ramin Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781479951178

Graph cuts method such as a-expansion [4] and fusion moves [22] have been successful at solving many optimization problems in computer vision. Higher-order Markov Random Fields (MRF's), which are important for numerous applications, have proven to be very difficult, especially for multilabel MRF's (i.e. more than 2 labels). In this paper we propose a new primal-dual energy minimization method for arbitrary higher-order multilabel MRF's. primal-dual methods provide guaranteed approximation bounds, and can exploit information in the dual variables to improve their efficiency. Our algorithm generalizes the PD3 [19] technique for first-order MRFs, and relies on a variant of max-flow that can exactly optimize certain higher-order binary MRF's [14]. We provide approximation bounds similar to PD3 [19], and the method is fast in practice. It can optimize non-submodular MRF's, and additionally can incorporate problem-specific knowledge in the form of fusion proposals. We compare experimentally against the existing approaches that can efficiently handle these difficult energy functions [6, 10, 11]. For higher-order denoising and stereo MRF's, we produce lower energy while running significantly faster.

关键词： Markov Random Fields Optimization primal-dual algorithms Markov random fields higher-order Maximum Flow Minimization methods Fusion reactions approximation FUSION fusion (melting)

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Approximating k-forest with resource augmentation: A primal-dual approach 11th

Approximating k-forest with resource augmentation: A primal-...

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11th Annual International Conference on Combinatorial Optimization and Applications (COCOA)

作者： Angel, Eric Nguyen Kim Thang Singh, Shikha Univ Evry Val Essonne IBISC Evry France Wellesley Coll Wellesley MA 02181 USA

ISBN: (纸本)9783319711461

In this paper, we study the k-forest problem in the model of resource augmentation. In the k-forest problem, given an edge-weighted graph G (V, E), a parameter k, and a set of m demand pairs subset of V x V, the objective is to construct a minimum-cost subgraph that connects at least k demands. The problem is hard to approximate-the best-known approximation ratio is O(min{root vertical bar V vertical bar, root K}). Furthermore, k-forest is as hard to approximate as the notoriously-hard densest k-subgraph problem. While the k-forest problem is hard to approximate in the worst-case, we show that with the use of resource augmentation, we can efficiently approximate it up to a constant factor. First, we restate the problem in terms of the number of demands that are not connected. In particular, the objective of the k-forest problem can be viewed as to remove at most m - k demands and find a minimum-cost subgraph that connects the remaining demands. We use this perspective of the problem to explain the performance of our algorithm (in terms of the augmentation) in a more intuitive way. Specifically, we present a polynomial-time algorithm for the k-forest problem that, for every E > 0, removes at most m - k demands and has cost no more than O(1/epsilon(2)) times the cost of an optimal algorithm that removes at most (1 - epsilon)(m - k) demands. (C) 2018 Elsevier B.V. All rights reserved.

关键词： primal-dual algorithms Resource augmentation k-forest problem Approximation algorithms LP duality algorithms Optimization problems Prize-collecting generalized Steiner tree problem

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The clustering matroid and the optimal clustering tree

The clustering matroid and the optimal clustering tree

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Workshop on Integer Programming

作者： Korach, E Stern, M Ben Gurion Univ Negev Dept Ind Engn & Management IL-84105 Beer Sheva Israel

We consider the following problem: Given a complete graph G=(V,E) with a cost on every edge and a given collection of subsets of V, we have to find a minimum cost spanning tree T such that each subset of the vertices in the collection induces a subtree in T. One motivation for this problem is to construct a minimum cost communication tree network for a collection of non-disjoint groups of customers such that the network will provide ``group fault tolerance'' and ``group privacy''. We model this problem as a matroid. We extend it to general matroids and call the new matroids ``clustering matroids''. We define three variations of the clustering tree problem and show that from an algorithmic point of view they are polynomially equivalent. We present a polynomial algorithm for one of the three variations, which implies that all of them can be solved polynomially. For the case where the cardinality of the subsets in the collection does not exceed three, we provide a greedy algorithm, a linear algorithm and also a polyhedron description of the convex hull of all the feasible solutions.

关键词： combinatorial optimization clustering spanning trees matroids hypergraphs polynomial graph algorithms primal-dual algorithms polyhedra

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Total Variation Denoising Method to Improve the Detection Process in IR Images 8

Total Variation Denoising Method to Improve the Detection Pr...

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8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)

作者： Chato, Lina Latifi, Shahram Kachroo, Pushkin Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA

ISBN: (纸本)9781538611043

Noise reduces the quality of images, resulting in the poor performance of many image processing applications. This paper presents an accurate denoising method to improve the performance of human detection in noisy infrared (IR) images. The local binary pattern (LBP) detector is used to study and analyze the poor effects of noise and to test the efficiency of the proposed denoising method. Noise is removed by minimizing the total variation (TV) of the images, an application that uses partial differential equations (PDEs) and optimal numerical methods to denoise images. The LBP detector shows abnormal behavior with noisy IR images. The Rudin, Osher, and Fatemi (ROF) method based on TV and TV norm1 (TV-L1) methods are efficient in denoising the IR images. In addition, the performance of the LBP detector is improved. Denoising using ROF improves the detection results efficiently as well. Measures for the true positive rate increased from 87% to 89%, and both the false negative per frame and false positive per frame are decreased with respect to the clear IR images. The results prove the efficiency of the proposed method in denoising IR images as well as restoring most of the LBP texture features.

关键词： LBP detector primal-dual algorithms minimizing image variation ROF TV-L1 human detection texture features SVM AWGN

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Faster PET Reconstruction with a Stochastic primal-dual Hybrid Gradient Method 17

Faster PET Reconstruction with a Stochastic Primal-Dual Hybr...

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Conference on Wavelets and Sparsity XVII

作者： Ehrhardt, Matthias J. Markiewicz, Pawel Chambolle, Antonin Richtarik, Peter Schott, Jonathan Schonlieb, Carola-Bibiane Univ Cambridge Dept Appl Math & Theoret Phys Cambridge England UCL Dept Med Phys & Biomed Engn CMIC Translat Imaging Grp London England CNRS Ecole Polytech CMAP Paris France KAUST Visual Comp Ctr Thuwal Saudi Arabia KAUST Extreme Comp Res Ctr Thuwal Saudi Arabia Univ Edinburgh Sch Math Edinburgh Midlothian Scotland Alan Turing Inst London England UCL Inst Neurol Dementia Res Ctr London England

ISBN: (数字)9781510612464

ISBN: (纸本)9781510612464;9781510612457

Image reconstruction in positron emission tomography (PET) is computationally challenging due to Poisson noise, constraints and potentially non-smooth priors-let alone the sheer size of the problem. An algorithm that can cope well with the first three of the aforementioned challenges is the primal-dual hybrid gradient algorithm (PDHG) studied by Chambolle and Pock in 2011. However, PDHG updates all variables in parallel and is therefore computationally demanding on the large problem sizes encountered with modern PET scanners where the number of dual variables easily exceeds 100 million. In this work, we numerically study the usage of SPDHG- a stochastic extension of PDHG- but is still guaranteed to converge to a solution of the deterministic optimization problem with similar rates as PDHG. Numerical results on a clinical data set show that by introducing randomization into PDHG, similar results as the deterministic algorithm can be achieved using only around 10% of operator evaluations. Thus, making significant progress towards the feasibility of sophisticated mathematical models in a clinical setting.

关键词： positron emission tomography PET total variation convex optimization primal-dual algorithms saddle point problems stochastic optimization inverse problems

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Eisenberg-Gale Markets: algorithms and Structural Properties 07

Eisenberg-Gale Markets: Algorithms and Structural Properties

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39th Annual ACM Symposium on Theory of Computing

作者： Jain, Kamal Vazirani, Vijay V. Microsoft Res Redmond WA 98007 USA

ISBN: (纸本)9781595936318

We define a new class of markets, the Eisenberg-Gale markets. This class contains Fisher's linear market, markets from the resource allocation framework of Kelly [20], as well as numerous interesting new markets. We obtain combinatorial, strongly polynomial algorithms for several markets in this class. Our algorithms have a simple description as ascending price auctions. Our algorithms lead to insights into the efficiency, fairness, rationality of solutions, and competition monotonicity of these markets. A classification of Eisenberg-Gale markets w.r.t. these properties reveals a surprisingly rich set of possibilities.

关键词： Markets equilibria primal-dual algorithms resource allocation convex programs KKT conditions

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A primal-dual Online Deterministic Algorithm for Matching with Delays 16th

A Primal-Dual Online Deterministic Algorithm for Matching wi...

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16th International Workshop on Approximation and Online algorithms (WAOA)

作者： Bienkowski, Marcin Kraska, Artur Liu, Hsiang-Hsuan Schmidt, Pawel Univ Wroclaw Inst Comp Sci Wroclaw Poland

ISBN: (纸本)9783030046934;9783030046927

In the Min-cost Perfect Matching with Delays (MPMD) problem, 2m requests arrive over time at points of a metric space. An online algorithm has to connect these requests in pairs, but a decision to match may be postponed till a more suitable matching pair is found. The goal is to minimize the joint cost of connection and the total waiting time of all requests. We present an O(m)-competitive deterministic algorithm for this problem, improving on an existing bound of O (m(log25.5)) = O (m(2.46)). Our algorithm also solves (with the same competitive ratio) a bipartite variant of MPMD, where requests are either positive or negative and only requests with different polarities may be matched with each other. Unlike the existing randomized solutions, our approach does not depend on the size of the metric space and does not have to know it in advance.

关键词： Online algorithms Delayed service Metric matching primal-dual algorithms Competitive analysis

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