NA Digest, V. 19, # 45

NA Digest Sunday, November 24, 2019 Volume 19 : Issue 45


Today's Editor:

Daniel M. Dunlavy
Sandia National Labs
dmdunla@sandia.gov

Today's Topics: Subscribe, unsubscribe, change address, or for na-digest archives: http://www.netlib.org/na-digest-html/faq.html

Submissions for NA Digest:

http://icl.utk.edu/na-digest/


From: LAPACK and ScaLAPACK team julie@icl.utk.edu
Date: November 22, 2019
Subject: LAPACK 3.9.0 and ScaLAPACK 2.1 released


The LAPACK and ScaLAPACK team would like to thank all the users who
contributed to those releases.

LAPACK 3.9.0
For details, please see http://www.netlib.org/lapack
To download LAPACK 3.9.0 directly from Github:
https://github.com/Reference-LAPACK/lapack/archive/v3.9.0.tar.gz
For bug, issue report and contributions, just use our Github
repository: https://github.com/Reference-LAPACK/lapack

ScaLAPACK 2.1
For details please see http://netlib.org/scalapack
To download SCALAPACK 2.1 directly from Github:
https://github.com/Reference-ScaLAPACK/scalapack/archive/v2.1.tar.gz
For bug, issue report and contributions, just use our Github
repository: https://github.com/Reference-ScaLAPACK/scalapack


From: Elias Jarlebring eliasj@kth.se
Date: November 21, 2019
Subject: NEP-PACK: A Julia package for nonlinear eigenvalue problems


We are proud to announce version 1.0 of NEP-PACK - a Julia package for
nonlinear eigenvalue problems with eigenvalue nonlinearity. The
package is released with an open-source license. The development is
carried out openly, using modern software engineering techniques such
as unit testing and continuous integration. The package makes full use
of the efficiency of Julia, yet maintains usability, and integrates
well with other software packages and other programming languages
(including matlab and python). The package is designed to be easy to
use for application researchers and engineers as well as algorithm
developers.

Currently, the package contains implementations of many
state-of-the-art algorithms including projection methods (nonlinear
Arnoldi, two variants of Jacobi-Davidson methods), contour integral
methods (Beyn's method and block SS), Krylov methods (nleigs, infinite
Arnoldi variants, CORK-pencil data type, infinite Lanczos), Rayleigh
functional methods (residual inverse iteration, Rayleigh functional
iteration), Newton-type methods (augmented Newton, implicit
determinant, Newton-QR, quasi-Newton, Broydens method).

Examples of other features: Nonlinear Rayleigh-Ritz projection can be
combined with any NEP-solver of the package; Parallelization of
contour integral methods without modification of internals of the
package; Algorithm independent deflation; Chebyshev interpolation and
Chebyshev companion form; Direct access to Berlin-Manchester benchmark
collection

Documentation & Tutorials:
https://nep-pack.github.io/NonlinearEigenproblems.jl/
Development: https://github.com/nep-pack/NonlinearEigenproblems.jl


From: Axel Klawonn axel.klawonn@uni-koeln.de
Date: November 24, 2019
Subject: Node-Level Performance Engineering, Germany, Jan 2020


Node-Level Performance Engineering Course
University of Cologne, Germany, January 20-22, 2020

This course covers performance engineering approaches on the compute
node level. Even application developers who are fluent in OpenMP and
MPI often lack a good grasp of how much performance could at best be
achieved by their code. This is because parallelism takes us only half
the way to good performance. Even worse, slow serial code tends to
scale very well, hiding the fact that resources are wasted. This
course conveys the required knowledge to develop a thorough
understanding of the interactions between software and hardware. This
process must start at the core, socket, and node level, where the code
gets executed that does the actual computational work. We introduce
the basic architectural features and bottlenecks of modern processors
and compute nodes. Pipelining, SIMD, superscalarity, caches, memory
interfaces, ccNUMA, etc., are covered. A cornerstone of node-level
performance analysis is the Roofline model, which is introduced in due
detail and applied to various examples from computational science. We
also show how simple software tools can be used to acquire knowledge
about the system, run code in a reproducible way, and validate
hypotheses about resource consumption. Finally, once the architectural
requirements of a code are understood and correlated with performance
measurements, the potential benefit of code changes can often be
predicted, replacing hope-for-the-best optimizations by a scientific
process.

Course Instructors: Prof. Dr. Gerhard Wellein, Dr. Georg Hager

Registration starts November 1, 2019 and ends December 15, 2019.
Participation is free of charge, but the number of participants is
limited.

For more information, please visit the workshop website:
https://www.cds.uni-koeln.de/index.php?id=14559



From: Matthias Gobbert gobbert@umbc.edu
Date: November 20, 2019
Subject: Paid NSF-Funded Training in Data Science, USA, Jan-May 2020


CyberTraining at UMBC is an NSF-funded training program in data
science using tools from High-Performance Computing (HPC) with
application examples from atmospheric science. The training consists
of instruction in all three areas of "Big Data + HPC + Atmospheric
Sciences", followed by a faculty-guided research project. All work is
conducted in multidisciplinary teams with one participant from each of
the three disciplines. After sucessful face-to-face training in 2018
and online training in 2019, the next training is in Spring 2020
(01/24/20-05/17/20) and will be conducted again ONLINE WITH
PARTICIPANTS FROM AROUND THE NATION.

Participants can be graduate students, post-docs, and early-career
faculty/researchers from US institutions who want to gain demonstrated
experience in multidisciplinary research and have the opportunity for
significant career impact. Both instruction and research are mentored
by faculty and supported by teaching assistants from each discipline.
Each participant who completes the program will be paid a $1,500
stipend.

We would appreciate help in identifying suitable candidates, ideally
with skills in one of the areas of the training. Please forward this
information to anyone. For inquiries, e-mail cybertraining@umbc.edu
or contact Matthias Gobbert, co-PI for HPC, at gobbert@umbc.edu.

For all information and to apply by 01/01/20, please visit
http://cybertraining.umbc.edu or see the flyer at
http://cybertraining.umbc.edu/docs/UMBC_CyberTraining_Spring_2020.pdf

This training is funded by the NSF under OAC-1730250 "CyberTraining:
DSE: Cross-Training of Researchers in Computing, Applied Mathematics
and Atmospheric Sciences using Advanced Cyberinfrastructure Resources"
(PI Jianwu Wang) under the solicitation Training-based Workforce
Development for Advanced Cyberinfrastructure (CyberTraining).



From: Michele Benzi michele.benzi@sns.it
Date: November 22, 2019
Subject: Copper Mountain Conference on Iterative Methods, USA, Mar 2020


The Sixteenth Copper Mountain Conference on Iterative Methods
March 21 - 26, 2020
Copper Mountain, Colorado, USA

The Copper Mountain Conference on Iterative Methods, held every other
year, is the premiere conference on all aspects of iterative solution
methods and their applications. The meeting is organized in
cooperation with SIAM.

Important Deadlines:

Student competition papers: January 10, 2020
[reserved to students who submitted an abstract by December 13, 2019]

Author abstracts: January 15, 2020

Early registration: January 17, 2020

Lodging: Please reserve early as rooms are limited (we recommend
booking no later than February 20 as any unclaimed rooms will be
released after that date)

Tutorial: on Saturday, March 21, starting at 1PM there will be a 3-4
hours tutorial led by Van Henson on Iterative Methods for Data
Analysis, aimed at graduate-level research assistants in numerical
analysis.

For additional information, see
http://grandmaster.colorado.edu/~copper/2020/



From: Miro Rozloznik miro@math.cas.cz
Date: November 18, 2019
Subject: ESSAM MASC School, Czech Republic, May 2020


The ESSAM European School on Mathematical Modelling, Numerical
Analysis and Scientific Computing will be held in Kacov, Czech
Republic, in May 24 - May 29, 2020.

The aim of the school is to promote interconnection of mathematical
modeling, analysis, and computational methods used in solving complex
(real-world) problems. The lectures are usually prepared with a broad
multidisciplinary audience in mind, and at each school a broad scope,
ranging from modeling to scientific computing, will be covered. Four
main speakers usually deliver a series of three 70 minutes lectu
minutes lectures. Ample time within the school is allocated for the
promotion of informal scientific discussions among the participants.

The speakers of the 2020 school are:
- Patrick Farell (University of Oxford, UK)
- Chi Wang Shu (Brown University, USA)
- Jan Zeman (Czech Technical University, Czech Republic)
- Walter Zulehner (Johannes Kepler University of Linz, Austria)

The school is organized under the auspices of the European Ma
Mathematical Society (EMS) and Faculty of Mathematics and Physics,
Charles University in Prague ( (MFF UK), as an activity of the Necas
Center for Mathematical Modeling. Participants will have a pos
possibility to present a short communication (maximally 10 minutes in
the dependence on th the number of participants contributing to this
kind of activity). The deadline for registration and s submission of
abstracts is April 30, 2020. Registration is now open at
http://essam-masc.cuni.cz/ including the ad additional information
about the venue, program and deadlines.


From: Martin Vohralik martin.vohralik@inria.fr
Date: November 19, 2019
Subject: European Finite Element Fair, France, May 2020


We are happy to announce the 18th European Finite Element Fair, May 15
- 16, 2020, Inria Paris.

There is no workshop fee. However, registration at efef2020@inria.fr
is compulsory, before April 30, 2020.

All the details can be found at https://efef2020.inria.fr/.


From: Iain Duff duff@cerfacs.fr
Date: November 22, 2019
Subject: Sparse Days at Cerfacs 2019, France, Jun 2020


The Annual Sparse Days meeting will be held at CERFACS in Toulouse on
11 and 12 June 2020.

As usual, there is no registration fee but interested parties should
nevertheless register using the website
https://sparsedays.cerfacs.fr/en. The deadline for doing this is 10
May 2020, but it well help our organization greatly if you could do
this as soon as possible. When you register, you should indicate
whether you want to give a talk and whether you want to attend our
traditional conference dinner on the evening of Thursday 11th.

The normal length for the talk and questions is 30 minutes but this is
open to negotiation in either direction subject to the fact that as
usual we will not be having parallel sessions.



From: Sonia Cafieri sonia@recherche.enac.fr
Date: November 19, 2019
Subject: Advances in Continuous Optimization, France, July 2020


We are pleased to announce the 18th EUROPT Workshop on Advances in
Continuous Optimization (EUROPT 2020), to be held in Toulouse, France,
on July 1-3, 2020: http://europt2020.recherche.enac.fr/

The EUROPT Workshop on Advances in Continuous Optimization is the
annual event of the EUROPT continuous optimization working group of
EURO (The Association of European Operational Research
Societies). This 18th edition will take place one week after the IFORS
2020 conference and will celebrate 20 years of activity of the working
group.

The Program Committee is announced on the website:
http://europt2020.recherche.enac.fr/

Authors are invited to submit contributions on all aspects of
continuous optimization. The scope of the workshop includes (but may
not be restricted to) the topics mentioned on the website.

Call for Contributions: The online abstract submission system will be
opened mid December 2019. Researchers interested in organizing a
session (3 or 4 talks) are invited to contact the Program Chair at
sonia.cafieri@enac.fr

Important Dates
- Abstract Submission: March 1, 2020
- Abstract Acceptance: March 15, 2020
- Registration deadline: April 15, 2020

More detailed information about the Workshop will be disseminated via
the website http://europt2020.recherche.enac.fr/.



From: Gabriel Lord gabriel.lord@ru.nl
Date: November 18, 2019
Subject: Faculty Positions, Radboud Univ, Nijmegen, NL


Radboud University

We seek to make two appointments to broaden and strengthen our
expertise in the broad area of Applied Mathematics (including
modelling, data analysis, applied analysis and computation).

We welcome applications at all levels and expect to make one tenure
track appointment and one appointment at any level. We particularly
encourage female applicants and applicants from diverse backgrounds to
apply.

For more details and to apply please see :
https://www.ru.nl/english/working-at/vacature/details-vacature/?
recid=1072034&pad=%2fenglish&doel=embed&t\
aal=uk

The deadline for applications is 12 January 2020.


From: Per-Gunnar Martinsson pgm@oden.utexas.edu
Date: November 21, 2019
Subject: Tenure Track Position, Interfaces of Data Science and Computational Science


The Oden Institute for Computational Engineering and Sciences at the
University of Texas at Austin invites applications for a tenure-track
faculty position at the interfaces of Data Science and Computational
Science. Areas of interest include, but are not limited to,
computational statistics, machine learning, graph theory, randomized
linear algebra, and tensor methods. Candidates should have a strong
connection to challenging applications in science, engineering, and/or
medicine. Candidates should be committed to establishing an
interdisciplinary research program at the intersection of advanced
mathematical, statistical, and computational techniques,
high-performance computing, and target applications.

The Oden Institute is an interdisciplinary research and education
institute working to transform science, engineering, and medicine
through computation. Oden Institute research is building the
mathematical and statistical foundations for predictive science, data
science, and machine learning; developing next-generation
computational science on advanced computing platforms; and addressing
grand challenges in science, engineering, and medicine through
computational modeling, scalable algorithms, and high-performance
computing.

For details on how to apply, see:
https://faculty.utexas.edu/career/53997

Applications received by November 30, 2019, will be assured full
consideration.



From: Oleg Burdakov oleg.burdakov@liu.se
Date: November 24, 2019
Subject: Tenure Track Position, Optimization for ML, Linkoping Univ


LINKOPING UNIVERSITY (LiU) hereby advertises a position as Research
fellow/Assistant professor in Optimization for Machine Learning at the
Division of Optimization of the Department of Mathematics. The
application deadline is December 9, 2019. Starting date: by agreement.

The major task for this position is fundamental research in the area
of mathematical optimization theory and novel techniques for machine
learning. Examples of research topics are: sparse optimization, saddle
point search, multi-objective learning, and stochastic quasi-Newton
methods. Although the expertise should primarily be in optimization,
the applicant should ideally also have experience in machine
learning. Duties include research, teaching, supervision and some
administrative duties. The position is for 5 years, of which 80% is
for research and 20% for pedagogical qualification/teaching.

The financing of the position, established through WASP (Wallenberg
AI, Autonomous Systems and Software Program, https://wasp-sweden.org),
includes support for not only the position holder's own salary, but
also a research group with two postdocs (2 years each) and one PhD
student (4 years).

Being a researcher at LiU will allow you to choose living on the
countryside while commuting to work in only 15 min. Furthermore,
working at LiU, you will own your entire research results and
Intellectual Property Rights yourself, enabling own startups and other
commercial activities that are even supported by the university.

For further details, including how to apply, see:
https://web103.reachmee.com/ext/I011/853/job?
site=7&lang=UK&validator=d7a66c13be778ef950c393a904293789&re\
f=https%3A%2F%2Fliu.se%2Fen%2Fwork-at-
liu&ihelper=https%3A%2F%2Fliu.se%2Fen%2Fwork-at-liu%2Fvacancies&job_id=12518


From: Lindon Roberts lindon.roberts@anu.edu.au
Date: November 22, 2019
Subject: Multiple Positions, Australian National Univ


The Mathematical Sciences Institute of the Australian National
University is advertising several ongoing & fixed-term positions at a
variety of levels, open to all areas of mathematics. The MSI is ranked
1st in Australia/31st globally for mathematics (QS rankings 2019), and
has a strong & growing computational mathematics group.

The following positions are available (up to 3 positions of each
type):
- MSI Fellow: 4-year fixed term research fellowships
- MSI JISC Fellows: 3-year fixed term positions, including teaching at
Shandong University, China
- Lecturer/Senior Lecturer: tenured/tenure-track continuing positions

All positions are at levels B or C (Assistant or Associate Professor
in the US system). Applications are through MathJobs.org, closing 17
December.

https://www.mathjobs.org/jobs?joblist-0-15249---40-s--


From: Maya Neytcheva maya.neytcheva@it.uu.se
Date: November 18, 2019
Subject: Open Positions, Dept of Information Technology, Uppsala Univ


The Division of Scientific Computing (DSC) within the Department of
Information Technology, Uppsala University, Sweden, announces two
positions:
-- Assistant Professor in Scientific Computing (limited to six years)
-- Associate Professor in Scientific Computing with specialization in
Data Science (permanent full-time position)

The research and education activities at DSC span mathematical
modeling, numerical analysis, algorithms, software development,
high-performance computing and computational models for data analysis
based on observations in many different application areas. The
applicants are expected to have expertise and experience in one or
more of our core areas and to be engaged in research and teaching, as
well as to be open to collaboration at department and faculty level.

The details regarding the positions are to be found at
https://uu.se/en/about-uu/join-us/details/?positionId=298708 and
https://uu.se/en/about-uu/join-us/details/?positionId=298728.
For more information about the department, visit http://www.it.uu.se/.


From: Lorenzo Tamellini tamellini@imati.cnr.it
Date: November 19, 2019
Subject: Research Assistant/Associate Position, UQ, CNR-IMATI/Pavia


A call for a research assistant/associate position on Scientific
Computing and Uncertainty Quantification for PDE with random data at
CNR-IMATI/Pavia is now open.

The deadline for the call is December 12, and the activity should
begin in the first semester 2020 (if possible, as early as March
2020). Holding a PhD title is not a requirement, therefore the call
is open also to students who just got their M.Sc. or close to
completing their Ph.D. Previous knowledge of Uncertainty
Quantification is welcome but not requested.

More information and a link to the official call are available at
https://sites.google.com/view/research-position-uq-imati

Please forward this announcement to any person who might be
interested.



From: Markus Bachmayr bachmayr@uni-mainz.de
Date: November 23, 2019
Subject: Postdoc Position, Adaptive solvers for eigenvalue problems, Univ Mainz


Applications are invited for a postdoctoral position in the research
group of Markus Bachmayr at Johannes Gutenberg-Universitat Mainz,
Germany. The position is available immediately, with an initial
contract for one year without teaching obligations and the possibility
of an extension. It is funded by the Collaborative Research Centre
1060 of the University of Bonn in the project "Adaptive methods for
high-dimensional eigenvalue problems and their computational
complexity". The project aims at the development and analysis of
adaptive solvers using low-rank tensor decompositions, and at their
application to quantum-physical problems. For a summary, see
https://sfb1060.iam.uni-bonn.de/project-groups/project-group-c/c09/

Candidates should hold a doctoral degree in mathematics, with a solid
background in numerical mathematics. Previous experience in adaptive
methods for PDEs or numerical linear algebra is advantageous.

Informal inquiries and applications should be sent to Prof. Markus
Bachmayr at the following e-mail address: bachmayr@uni-mainz.de.
Applications (in German or English) should contain CV and publication
list, scans of certificates/transcripts, a brief statement of research
interests, as well as contact details of two references. Evaluation of
applications will begin immediately. Applications will be considered
until the position is filled.


From: Adrianna Gillman adrianna.gillman@colorado.edu
Date: November 19, 2019
Subject: Postdoc Position, Applied Math, CU Boulder


UPDATED POSTING
Application deadline: Dec. 1
Start date: Summer 2020 or sooner

There is an opening for a postdoctoral researcher with experience in
parallel programming and/or numerical partial differential equations
in the Applied Mathematics Department at the University of Colorado
Boulder. Knowledge of programming for many-core architectures is a
plus. A strong mathematics background is required. This project is
an industrial collaboration with the ultimate goal of producing and
efficient and robust high frequency Helmholtz solver.

Successful candidate:
- Will have received a PhD within 18 months preceding the date of
appointment in Applied/Computational Mathematics, Computational
Science and Engineering or a related field.
- Should have a good background and record of accomplishment in one or
more of the following areas: numerical PDEs, high performance
computing, inverse problems or seismic inversion, and highly
oscillatory waves.

For additional information, please see
https://jobs.colorado.edu/jobs/JobDetail/?jobId=20065&emailCampaignId=168.
Informal inquiries to adrianna.gillman@colorado.edu are welcome.

Applications should be submitted through the following link:
https://jobs.colorado.edu/jobs/JobDetail/?jobId=20065&emailCampaignId=168



From: Michel Denault michel.denault@hec.ca
Date: November 18, 2019
Subject: Postdoc Position, Reinforcement Learning, Montreal, Canada


A post-doctoral fellowship is offered in the field of reinforcement
learning applied to hydropower optimization. The position is fully
funded by an IVADO research grant. The fellow will work under the
guidance of Pascal te, RioTinto; Michel Denault, HEC Montreal; and
Dominique Orban, Polytechnique Montreal.

All details on
https://talents.ivado.ca/en/poste/postdoctorat-apprentissage-par-renforcement-et-
production-hydro-electri\
que-postdoctoral-fellowship-on-reinforcement-learning-for-hydropower/


From: Michele Benzi michele.benzi@sns.it
Date: November 23, 2019
Subject: Postdoc Positions, Pisa Junior Visiting Positions, CRM


CALL FOR APPLICATIONS
(deadline: 7th January 2020 AT 11:59 PM Italian time)

The Centro di Ricerca Matematica "Ennio De Giorgi" of the Scuola
Normale Superiore (Pisa, Italy) invites applications for 5 two-year
post-doc research positions named "Junior Visiting Positions" covering
the following research areas:

Position n. 1: Algebraic Geometry and/or Number Theory
Position n. 2: Topology, Differential Geometry and/or Geometric Analysis
Position n. 3: Partial Differential Equations and/or Probability
Position n. 4: Numerical Analysis and/or Financial Mathematics
Position n. 5: Dynamical Systems

For additional information (including salary and travel allowance) and
application instructions, see http://crm.sns.it/grant/36/



From: Tim Palmer tim.palmer@physics.ox.ac.uk
Date: November 23, 2019
Subject: Postdoc Positions, Reduced-Precision Weather/Climate Modelling, Oxford Univ


Please see:
https://my.corehr.com/pls/uoxrecruit/erq_jobspec_details_form.jobspec?p_id=143955

Tim Palmer
Department of Physics
University of Oxford


From: J. Lu jianlu1979@163.com
Date: November 18, 2019
Subject: Postdoc/Ph.D. Positions, Wavelet frames/Optimization/Time-Frequency Analysis, SZU


Job Type: Full-Time
Duration: 2 years
Number of Position for Postdoc: 2 Positions
Number of Position for Ph.D.: 2 Positions

Closing Date: Open Until Filled

Description: We are looking for Ph.D./Postdoctoral Researchers in
Wavelet frames, Theory and Appl./Optimization/ Time-Frequency
Analysis/Computer Vision, etc.

Requirements:


From: Nail Yamaleev nyamalee@odu.edu
Date: November 21, 2019
Subject: PhD Position, Computational Mathematics, Old Dominion Univ


Applications are invited for a PhD student position in the Department
of Mathematics and Statistics at Old Dominion University (Norfolk, VA,
USA). This position will provide a unique opportunity to work on a
cutting-edge project in Dr. Yamaleev's group in close collaboration
with research scientists of NASA Langley Research Center. Current
research in the group focuses on the development of new entropy stable
schemes for the Navier-Stokes equations, adjoint-based methods for
PDE-constrained opt optimization problems, and grid adaptation methods
based on error minimization. We are looking for an enthusiastic and
highly motivated PhD can candidate with a M.S. or B.S. degree in
Mathematics or a closely related fie field. A solid background in
numerical methods, excellent programming ski skills, and good
communication skills (written/spoken English) are req required.

Interested candidates should apply for a graduate assistantship in c
computational and applied mathematics at:
https://www.odu.edu/admission/graduate .

Further details on how to apply can be found at:
http://catalog.odu.edu/graduate/collegeofsciences/mathematicsstatistics/#doctorofphilosop
hy-computational\
andappliedmathematics
For more information, please contact Dr. Yamaleev at nyamalee@odu.edu.


From: Markus Bachmayr bachmayr@uni-mainz.de
Date: November 19, 2019
Subject: PhD Positions, Multiscale Modeling and UQ, Univ Mainz, Germany


The Mainz Institute of Multiscale Modeling invites applications for
several PhD positions in interdisciplinary projects in mathematics,
computer science, geosciences, atmospheric physics, statistical
physics, and biology. Many of the projects have a strong component in
computational methods for uncertainty quantification. For further
details on projects and on the application procedure, see:
https://model.uni-mainz.de/phd-program/

The application deadline is November 30, 2019, but later applications
may be considered.


From: Dongbin Xiu xiu.16@osu.edu
Date: November 22, 2019
Subject: Call for Submission, Journal of Machine Learning for Modeling and Computing


Publication Date: June, 2020
Submission Deadline: January 31, 2020
Decision Expected by: April 30, 2020

Submission site: http://www.j-mlmc.com

The Journal of Machine Learning for Modeling and Computing (JMLMC),
published by Begell House, is now accepting submissions for June 2020
issue.

Aims and Scope: The Journal of Machine Learning for Modeling and
Computing (JMLMC) focuses on the study of machine learning methods for
modeling and scientific computing. The scope of the journal includes,
but is not limited to, research of the following types: (1) the use of
machine learning techniques to model real-world problems such as
physical systems, social sciences, biology, etc.; (2) the development
of novel numerical strategies, in conjunction of machine learning
methods, to facilitate practical computation; and (3) the fundamental
mathematical and numerical analysis for understanding machine learning
methods.

JMLMC is operated on deadlines. For each publishing issue there is a
submission deadline. Papers submitted before the deadline will be
considered for the current issues (otherwise for the next issue). All
submitted papers will be receive the official decision prior to the
decision deadline, which is set as 3 months after the submission
deadline. This is to ensure the fast transition for the fast growing
and fast paced community.



From: Edward B. Saff constr.approx@vanderbilt.edu
Date: November 19, 2019
Subject: Contents, Constructive Approximation, 50 (3)


Constructive Approximation
Volume 50, Issue 3, December 2019
Table of Contents

Obituary: Stephan Ruscheweyh

Condensers with Touching Plates and Constrained Minimum Riesz and
Green Energy Problems, P. D. Dragnev, B. Fuglede, D. P. Hardin,
E. B. Saff, N. Zorii

Learning General Sparse Additive Models from Point Queries in High
Dimensions, Hemant Tyagi, Jan Vybiral

Convergence and Normal Continuity Analysis of Nonstationary
Subdivision Schemes Near Extraordinary Vertices and Faces, Costanza
Conti, Marco Donatelli, Lucia Romani, Paola Novara

On the Totik-Widom Property for a Quasidisk, V. Andrievskii,
F. Nazarov

Analysis of Decimation on Finite Frames with Sigma-Delta Quantization,
Kung-Ching Lin

Sharp Constants of Approximation Theory. II. Invariance Theorems and
Certain Multivariate Inequalities of Different Metrics, Michael
I. Ganzburg

Constructive Approximation
An International Journal for Approximations and Expansions
Published by Springer
http://link.springer.com/journal/365



From: Yonghui Yu yyu@lsec.cc.ac.cn
Date: November 21, 2019
Subject: Contents, Journal of Computational Mathematics, 37 (6)


Journal of Computational Mathematics, Volume 37 (2019), Issue 6
http://www.global-sci.org/intro/articles_list/jcm/1544.html

Contents

Preface
Weimin Han, Ge Wang and Hongkai Zhao

A Unied Algorithmic Framework of Symmetric Gauss-Seidel Decomposition
Based Proximal Admms for Convex Composite Programming, Liang Chen,
Defeng Sun, Kim-Chuan Toh and Ning Zhang

Three-dimensional Gravity-magnetic Cross-gradient Joint Inversion
Based on Structural Coupling and A Fast Gradient Method, Yuanping
Zhang and Yanfei Wang

Proximal-proximal-gradient Method, Ernest K. Ryu and Wotao Yin

Regularized Two-stage Stochastic Variational Inequalities for Cournot-
Nash Equilibrium under Uncertainty, Jie Jiang, Yun Shi, Xiaozhou Wang
and Xiaojun Chen

A Robust Interior Point Method for Computing the Analytic Center of An
Ill-Conditioned Polytope with Errors, Zhouhong Wang, Yuhong Dai and
Fengmin Xu

Tackling Industrial-scale Supply Chain Problems by Mixed-integer
Programming, Gerald Gamrath, Ambros Gleixner, Thorsten Koch, Matthias
Miltenberger, Dimitri Kniasew, Dominik Schlogel, Alexander Martin and
Dieter Weninger

Block Algorithms with Augmented Rayleigh-Ritz Projections for Large-
scale Eigenpair Computation, Haoyang Liu, Zaiwen Wen, Chao Yang and
Yin Zhang

Stabilized Barzilai-Borwein Method, Oleg Burdakov, Yuhong Dai and Na
Huang



From: Claude Brezinski claude.brezinski@univ-lille.fr
Date: November 23, 2019
Subject: Contents, Numerical Algorithms, 82 (4)


Table of Contents
Numerical Algorithms, Vol. 82, No. 4

N. Alinia, M. Zarebnia, A numerical algorithm based on a new kind of
tension B-spline function for solving Burgers-Huxley equation.

Luca Fenzi, Wim Michiels, Polynomial (chaos) approximation of maximum
eigenvalue functions.

Songnian He, Tao Wu, Yeol Je Cho, Themistocles M. Rassias, Optimal
parameter selections for a general Halpern iteration.

A. Ortiz-Bernardin, C. Alvarez, N. Hitschfeld-Kahler, A. Russo,
R. Silva-Valenzuela, E. Olate-Sanzana, Veamy: an extensible
object-oriented C++ library for the virtual element method.

Hao Yu, Boying Wu, Dazhi Zhang, The Laguerre-Hermite spectral methods
for the time-fractional sub-diffusion equations on unbounded domains.

Zhong-Zhi Bai, Cun-Qiang Miao, Computing eigenpairs of Hermitian
matrices in perfect Krylov subspaces.

Y.S. Li, L.L. Sun, Z.Q. Zhang, T. Wei, Identification of the
time-dependent source term in a multi-term time-fractional diffusion
equation.

J.A. Ezquerro, M.A. Hernandez-Veron, Nonlinear Fredholm integral
equations and majorant functions.

Sakhi Zaman, Siraj-ul-Islam, On numerical evaluation of integrals
involving oscillatory Bessel and Hankel functions.

Saman Babaie-Kafaki, Zohre Aminifard, Two-parameter scaled memoryless
BFGS methods with a nonmonotone choice for the initial step length.

Charles Curry, Brynjulf Owren, Variable step size commutator free Lie
group integrators.

Yang Cao, An Wang, Two-step modulus-based matrix splitting iteration
methods for implicit complementarity problems.

Marija Milosevic, Divergence of the backward Euler method for ordinary
stochastic differential equations.

Xiaobo Yang, Weizhang Huang, Jianxian Qiu, Moving mesh finite
difference solution of non-equilibrium radiation diffusion equations.

Jure Ravnik, Jan Tibuat, Fast boundary-domain integral method for
unsteady convection-diffusion equation with variable diffusivity using
the modified Helmholtz fundamental solution.

T. Li, Y. Wang, F. Liu, I. Turner, Novel parameter estimation
techniques for a multi-term fractional dynamical epidemic model of
dengue fever.

End of Digest
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