Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313218
Lingqing Gan, P. Djurić
Models of growing networks have attracted a lot of interest during the past few years. An important question about these models is to decide which model explains an observed network formation most accurately. In this work, we propose a Bayesian model selection scheme which chooses the best model based on predictive distributions. The procedure was investigated on three types of models, including the random model, the preferential attachment model and the hybrid model. With the hybrid model, we leverage results on imperfect Bernoulli trial experiments to obtain the posterior distribution of the weight parameter, which is characterized as a polynomial function on the interval [0,1]. A Beta distribution is used to approximate the posterior in order to reduce the growing computational and representation complexity. Simulations in accordance with the proposed scheme are carried out. They demonstrate validity of the proposed approach.
{"title":"Bayesian selection of models of network formation","authors":"Lingqing Gan, P. Djurić","doi":"10.1109/CAMSAP.2017.8313218","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313218","url":null,"abstract":"Models of growing networks have attracted a lot of interest during the past few years. An important question about these models is to decide which model explains an observed network formation most accurately. In this work, we propose a Bayesian model selection scheme which chooses the best model based on predictive distributions. The procedure was investigated on three types of models, including the random model, the preferential attachment model and the hybrid model. With the hybrid model, we leverage results on imperfect Bernoulli trial experiments to obtain the posterior distribution of the weight parameter, which is characterized as a polynomial function on the interval [0,1]. A Beta distribution is used to approximate the posterior in order to reduce the growing computational and representation complexity. Simulations in accordance with the proposed scheme are carried out. They demonstrate validity of the proposed approach.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116893750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313193
Kristina Naskovska, S. Lau, Amr Aboughazala, M. Haardt, J. Haueisen
Simultaneously recorded magnetoencephalography (MEG) and electroencephalography (EEG) signals can benefit from a joint analysis based on coupled Canonical Polyadic (CP) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The Coupled — Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization framework (C-SECSI) efficiently estimates the factor matrices with adjustable complexity-accuracy trade-offs. Our objective is to decompose simultaneously recorded MEG and EEG signals above intact skull and above two conducting skull defects using C-SECSI in order to determine how such a tissue anomaly of the head is reflected in the tensor rank. The source of the MEG and EEG signals is a miniaturized electric dipole that is implanted into a rabbit's brain. The dipole is shifted along a line under the skull defects, and measurements are taken at regularly spaced points. The coupled SECSI analysis is conducted for MEG and EEG measurement series and ranks 1–3. This coupled decomposition produces meaningful components representing the three characteristic signal topographies for source positions under defect 1 and the positions on either side of defect 1. The rank estimation with respect to the complexity-accuracy trade-off of rank 3 reflects the three characteristic cases well and matches the dimensions spanned by the data set. The intact skull MEG signals show a higher complexity (rank 3) than the corresponding EEG signals (rank 1). The C-SECSI framework is a very promising method for blind signal separation in multidimensional data with coupled modalities, such as simultaneous MEG-EEG.
{"title":"Joint MEG-EEG signal decomposition using the coupled SECSI framework: Validation on a controlled experiment","authors":"Kristina Naskovska, S. Lau, Amr Aboughazala, M. Haardt, J. Haueisen","doi":"10.1109/CAMSAP.2017.8313193","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313193","url":null,"abstract":"Simultaneously recorded magnetoencephalography (MEG) and electroencephalography (EEG) signals can benefit from a joint analysis based on coupled Canonical Polyadic (CP) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The Coupled — Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization framework (C-SECSI) efficiently estimates the factor matrices with adjustable complexity-accuracy trade-offs. Our objective is to decompose simultaneously recorded MEG and EEG signals above intact skull and above two conducting skull defects using C-SECSI in order to determine how such a tissue anomaly of the head is reflected in the tensor rank. The source of the MEG and EEG signals is a miniaturized electric dipole that is implanted into a rabbit's brain. The dipole is shifted along a line under the skull defects, and measurements are taken at regularly spaced points. The coupled SECSI analysis is conducted for MEG and EEG measurement series and ranks 1–3. This coupled decomposition produces meaningful components representing the three characteristic signal topographies for source positions under defect 1 and the positions on either side of defect 1. The rank estimation with respect to the complexity-accuracy trade-off of rank 3 reflects the three characteristic cases well and matches the dimensions spanned by the data set. The intact skull MEG signals show a higher complexity (rank 3) than the corresponding EEG signals (rank 1). The C-SECSI framework is a very promising method for blind signal separation in multidimensional data with coupled modalities, such as simultaneous MEG-EEG.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123989074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313169
Michael Pauley, J. Manton
Optimisation geometry studies the geometry of a smooth class of optimisation problems on manifolds. A focus is placed on those classes that are fibre-wise Morse, i.e., such that in all specific problem instances, the objective function is Morse. If this condition holds, optimisation can be split into two parts: a (hard) preparation stage that computes certain lookup tables, and an (easy) optimisation stage that, given parameter values, uses the lookup tables to quickly find the global optimum for the particular problem instance. In this paper we show how the fibre-wise Morse condition can be automatically checked during the preparation stage. We also implement a version of the optimisation stage, thus providing a complete demonstration of the algorithm suggested by the theory. We discuss what goes wrong when the fibre-wise Morse condition fails and put forward some preliminary ideas on how these issues might be handled.
{"title":"Optimisation geometry and its implications for optimisation algorithms","authors":"Michael Pauley, J. Manton","doi":"10.1109/CAMSAP.2017.8313169","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313169","url":null,"abstract":"Optimisation geometry studies the geometry of a smooth class of optimisation problems on manifolds. A focus is placed on those classes that are fibre-wise Morse, i.e., such that in all specific problem instances, the objective function is Morse. If this condition holds, optimisation can be split into two parts: a (hard) preparation stage that computes certain lookup tables, and an (easy) optimisation stage that, given parameter values, uses the lookup tables to quickly find the global optimum for the particular problem instance. In this paper we show how the fibre-wise Morse condition can be automatically checked during the preparation stage. We also implement a version of the optimisation stage, thus providing a complete demonstration of the algorithm suggested by the theory. We discuss what goes wrong when the fibre-wise Morse condition fails and put forward some preliminary ideas on how these issues might be handled.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122387288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313214
P. Dreesen, K. Tiels, Mariya Ishteva, J. Schoukens
The use of black-box models is wide-spread in signal processing and system identification applications. However, often such models possess a large number of parameters, and obfuscate their inner workings, as there are cross-connections between all inputs and all outputs (and possibly all internal states) of the model. Although black-box models have proven their success and wide applicability, there is a need to shed a light on what goes on inside the model. We have developed a tensor-based method that aims at pinpointing the nonlinearities of a given nonlinear model into a small number of univariate nonlinear mappings, with the advantageous side-effect of reducing the parametric complexity. In this contribution we will discuss how the method is conceived, and how it can be applied to the task of finding structure in blackbox models. We have found that the tensor-based decoupling method is able to reconstruct up to high accuracy a given blackbox nonlinear model, while reducing the parametric complexity and revealing some of the inner operation of the model. Due to their universal use, we will focus the presentation on the use of nonlinear state-space models, but the method is also suitable for other model structures. We validate the method on a case study in nonlinear system identification.
{"title":"Nonlinear system identification: Finding structure in nonlinear black-box models","authors":"P. Dreesen, K. Tiels, Mariya Ishteva, J. Schoukens","doi":"10.1109/CAMSAP.2017.8313214","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313214","url":null,"abstract":"The use of black-box models is wide-spread in signal processing and system identification applications. However, often such models possess a large number of parameters, and obfuscate their inner workings, as there are cross-connections between all inputs and all outputs (and possibly all internal states) of the model. Although black-box models have proven their success and wide applicability, there is a need to shed a light on what goes on inside the model. We have developed a tensor-based method that aims at pinpointing the nonlinearities of a given nonlinear model into a small number of univariate nonlinear mappings, with the advantageous side-effect of reducing the parametric complexity. In this contribution we will discuss how the method is conceived, and how it can be applied to the task of finding structure in blackbox models. We have found that the tensor-based decoupling method is able to reconstruct up to high accuracy a given blackbox nonlinear model, while reducing the parametric complexity and revealing some of the inner operation of the model. Due to their universal use, we will focus the presentation on the use of nonlinear state-space models, but the method is also suitable for other model structures. We validate the method on a case study in nonlinear system identification.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122602034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313174
Jianshu Zhang, M. Haardt
In this paper we study the channel estimation problem for a CP-OFDM based mmWave hybrid analog-digital MIMO system, where the analog processing is achieved using only phase shift networks. A two-stage three-dimensional (3-D) Unitary ESPRIT in DFT beamspace based channel estimation algorithm is proposed to estimate the angular-delay profile and subsequently the unknown frequency-selective channel. The required training protocol, analog precoding and decoding matrices, as well as pilot patterns are discussed. Simulation results show that the proposed multi-stage 3-D Unitary ESPRIT in DFT beamspace based channel estimation algorithm provides high resolution channel estimates.
{"title":"Channel estimation for hybrid multi-carrier mmwave MIMO systems using three-dimensional unitary esprit in DFT beamspace","authors":"Jianshu Zhang, M. Haardt","doi":"10.1109/CAMSAP.2017.8313174","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313174","url":null,"abstract":"In this paper we study the channel estimation problem for a CP-OFDM based mmWave hybrid analog-digital MIMO system, where the analog processing is achieved using only phase shift networks. A two-stage three-dimensional (3-D) Unitary ESPRIT in DFT beamspace based channel estimation algorithm is proposed to estimate the angular-delay profile and subsequently the unknown frequency-selective channel. The required training protocol, analog precoding and decoding matrices, as well as pilot patterns are discussed. Simulation results show that the proposed multi-stage 3-D Unitary ESPRIT in DFT beamspace based channel estimation algorithm provides high resolution channel estimates.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130458432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313211
Bakht Zaman, L. M. Lopez-Ramos, Daniel Romero, B. Beferull-Lozano
An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.
{"title":"Online topology estimation for vector autoregressive processes in data networks","authors":"Bakht Zaman, L. M. Lopez-Ramos, Daniel Romero, B. Beferull-Lozano","doi":"10.1109/CAMSAP.2017.8313211","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313211","url":null,"abstract":"An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorithm and offer complementary benefits in terms of computational efficiency. Numerical results showcase the merits of the proposed schemes in both estimation and prediction tasks.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128844493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313082
P. Tichavský, A. Phan, A. Cichocki
Analysis of multidimensional arrays, usually called tensors, often becomes difficult in cases when the tensor rank (a minimum number of rank-one components) exceeds all the tensor dimensions. Traditional methods of canonical polyadic decomposition of such tensors, namely the alternating least squares, can be used, but a presence of a large number of false local minima can make the problem hard. Usually, multiple random initializations are advised in such cases, but the question is how many such random initializations are sufficient to get a good chance of finding the right solution. It appears that the number of the initializations can be very large. We propose a novel approach to the problem. The given tensor is augmented by some unknown parameters to the shape that admits ordinary tensor diagonalization, i.e., transforming the augmented tensor into an exact or nearly diagonal form through multiplying the tensor by non-orthogonal invertible matrices. Three possible constraints are proposed to make the optimization problem well defined. The method can be modified for an under-determined block-term decomposition.
{"title":"Under-Determined tensor diagonalization for decomposition of difficult tensors","authors":"P. Tichavský, A. Phan, A. Cichocki","doi":"10.1109/CAMSAP.2017.8313082","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313082","url":null,"abstract":"Analysis of multidimensional arrays, usually called tensors, often becomes difficult in cases when the tensor rank (a minimum number of rank-one components) exceeds all the tensor dimensions. Traditional methods of canonical polyadic decomposition of such tensors, namely the alternating least squares, can be used, but a presence of a large number of false local minima can make the problem hard. Usually, multiple random initializations are advised in such cases, but the question is how many such random initializations are sufficient to get a good chance of finding the right solution. It appears that the number of the initializations can be very large. We propose a novel approach to the problem. The given tensor is augmented by some unknown parameters to the shape that admits ordinary tensor diagonalization, i.e., transforming the augmented tensor into an exact or nearly diagonal form through multiplying the tensor by non-orthogonal invertible matrices. Three possible constraints are proposed to make the optimization problem well defined. The method can be modified for an under-determined block-term decomposition.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313133
Puyang Wang, He Zhang, Vishal M. Patel
Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Generative Adversarial Network (ID-GAN), for automatically removing speckle from the input noisy images. In particular, ID-GAN is trained in an end-to-end fashion using a combination of Euclidean loss, Perceptual loss and Adversarial loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.
{"title":"Generative adversarial network-based restoration of speckled SAR images","authors":"Puyang Wang, He Zhang, Vishal M. Patel","doi":"10.1109/CAMSAP.2017.8313133","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313133","url":null,"abstract":"Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Generative Adversarial Network (ID-GAN), for automatically removing speckle from the input noisy images. In particular, ID-GAN is trained in an end-to-end fashion using a combination of Euclidean loss, Perceptual loss and Adversarial loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126408571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313171
M. Nokleby, W. Bajwa
We present and analyze two algorithms — termed distributed stochastic approximation mirror descent (D-SAMD) and accelerated distributed stochastic approximation mirror descent (AD-SAMD)—for distributed, stochastic optimization from high-rate data streams over rate-limited networks. Devices contend with fast streaming rates by mini-batching samples in the data stream, and they collaborate via distributed consensus to compute variance-reduced averages of distributed subgradients. This induces a trade-off: Mini-batching slows down the effective streaming rate, but may also slow down convergence. We present two theoretical contributions that characterize this trade-off: (i) bounds on the convergence rates of D-SAMD and AD-SAMD, and (ii) sufficient conditions for order-optimum convergence of D-SAMD and AD-SAMD, in terms of the network size/topology and the ratio of the data streaming and communication rates. We find that AD-SAMD achieves order-optimum convergence in a larger regime than D-SAMD. We demonstrate the effectiveness of the proposed algorithms using numerical experiments.
{"title":"Distributed mirror descent for stochastic learning over rate-limited networks","authors":"M. Nokleby, W. Bajwa","doi":"10.1109/CAMSAP.2017.8313171","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313171","url":null,"abstract":"We present and analyze two algorithms — termed distributed stochastic approximation mirror descent (D-SAMD) and accelerated distributed stochastic approximation mirror descent (AD-SAMD)—for distributed, stochastic optimization from high-rate data streams over rate-limited networks. Devices contend with fast streaming rates by mini-batching samples in the data stream, and they collaborate via distributed consensus to compute variance-reduced averages of distributed subgradients. This induces a trade-off: Mini-batching slows down the effective streaming rate, but may also slow down convergence. We present two theoretical contributions that characterize this trade-off: (i) bounds on the convergence rates of D-SAMD and AD-SAMD, and (ii) sufficient conditions for order-optimum convergence of D-SAMD and AD-SAMD, in terms of the network size/topology and the ratio of the data streaming and communication rates. We find that AD-SAMD achieves order-optimum convergence in a larger regime than D-SAMD. We demonstrate the effectiveness of the proposed algorithms using numerical experiments.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122468680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-12-01DOI: 10.1109/CAMSAP.2017.8313161
Loris Cannelli, F. Facchinei, G. Scutari
We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest. The proposed algorithmic framework is distributed and asynchronous: i) agents update their variables at arbitrary times, without any coordination with the others; and ii) agents may use outdated information from their neighbors. Convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup with respect to the number of agents, when the delays are not too large.
{"title":"Multi-Agent asynchronous nonconvex large-scale optimization","authors":"Loris Cannelli, F. Facchinei, G. Scutari","doi":"10.1109/CAMSAP.2017.8313161","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313161","url":null,"abstract":"We propose a novel algorithmic framework for the asynchronous and distributed optimization of multi-agent systems. We consider the constrained minimization of a nonconvex and nonsmooth partially separable sum-utility function, i.e., the cost function of each agent depends on the optimization variables of that agent and of its neighbors. This partitioned setting arises in several applications of practical interest. The proposed algorithmic framework is distributed and asynchronous: i) agents update their variables at arbitrary times, without any coordination with the others; and ii) agents may use outdated information from their neighbors. Convergence to stationary solutions is proved, and theoretical complexity results are provided, showing nearly ideal linear speedup with respect to the number of agents, when the delays are not too large.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122702201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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