Redmond, WA – Microsoft has added JuliaPro to Windows Data Science
Virtual Machine (DSVM), making it available on Microsoft Azure.

Julia is now available for the first time on the two largest cloud
environments, following the December 2016 launch of Julia on Amazon Web
Services.

According to Viral Shah, CEO of Julia
Computing, “We are thrilled to partner with
Microsoft to make JuliaPro available to Microsoft Azure users via
Windows Data Science Virtual Machine (DSVM). Now Julia users in finance,
engineering, manufacturing, biomedical research and other areas of data
science and scientific computing can access JuliaPro in both of the top
two public cloud computing environments: Amazon Web Services and
Microsoft Azure.”

This latest version of JuliaPro launched in December 2016, and includes
the Julia Compiler, Debugger, Profiler, Juno Integrated Development
Environment, more than 100 curated packages, data visualization and
plotting. Integration with Excel, customer support and indemnity are
available with JuliaPro Enterprise and JuliaFin. JuliaFin also includes
Bloomberg integration, advanced time series analytics and Miletus, a
custom Julia package for developing and executing complex trading
strategies.

About Julia Computing and Julia

Julia Computing (JuliaComputing.com) was
founded in 2015 by the co-creators of the Julia language to provide
support to businesses and researchers who use Julia.

Julia is the fastest modern high performance open source computing
language for data and analytics. It combines the functionality and ease
of use of R, Python, Matlab, SAS and Stata with the speed of Java and
C++. Julia delivers dramatic improvements in simplicity, speed, capacity
and productivity.

1. Julia is lightning fast. Julia provides speed improvements up to
   1,000x for insurance model estimation, 225x for parallel
   supercomputing image analysis and 11x for macroeconomic modeling.

2. Julia is easy to learn. Julia’s flexible syntax is familiar and
   comfortable for users of Python, R and Matlab.

3. Julia integrates well with existing code and platforms. Users of
   Python, R, Matlab and other languages can easily integrate their
   existing code into Julia.

4. Elegant code. Julia was built from the ground up for
   mathematical, scientific and statistical computing, and has advanced
   libraries that make coding simple and fast, and dramatically reduce
   the number of lines of code required – in some cases, by 90%
   or more.

5. Julia solves the two language problem. Because Julia combines
   the ease of use and familiar syntax of Python, R and Matlab with the
   speed of C, C++ or Java, programmers no longer need to estimate
   models in one language and reproduce them in a faster
   production language. This saves time and reduces error and cost.

Julia users and partners include: Amazon, IBM, Intel, Microsoft, DARPA,
Lawrence Berkeley National Laboratory, National Energy Research
Scientific Computing Center (NERSC), Federal Aviation Administration
(FAA), MIT Lincoln Labs, Moore Foundation, Nobel Laureate Thomas J.
Sargent, Federal Reserve Bank of New York (FRBNY), Brazilian National
Development Bank (BNDES), BlackRock, Conning, Berkery Noyes, BestX, Path
BioAnalytics, Invenia, Gambit, Augmedics, Tangent Works, Voxel8, UC
Berkeley Autonomous Race Car (BARC) and many of the world’s largest
investment banks, asset managers, fund managers, foreign exchange
analysts, insurers, hedge funds and regulators. Julia is being used to
analyze images of the universe and research dark matter, drive parallel
computing on supercomputers, diagnose medical conditions, provide
surgeons with real-time imagery using augmented reality, analyze cancer
genomes, manage 3D printers, pilot self-driving racecars, build drones,
improve air safety and provide analytics for foreign exchange trading,
insurance, regulatory compliance, macroeconomic modeling, sports
analytics, manufacturing and much, much more.

Employers looking to hire Julia programmers in 2017 include: Apple,
Amazon, Facebook, BlackRock, Ford, Oracle, Comcast, Massachusetts
General Hospital, Farmers Insurance, Los Alamos National Laboratory and
the National Renewable Energy Laboratory.

In the November 2016 issue of Wilmott
Magazine, Julia Computing’s Viral
Shah and Simon Byrne explain why Julia is taking the field of
quantitative finance by storm.

Why are so many quants from investment banking,
insurance,
asset
management,
fund
management,
foreign exchange
analytics,
commodity trading, energy trading, central
banking and risk
analysis switching to Julia?

Finance quants find Julia the optimal finance solution for a number of
reasons:

• Julia is the fastest modern language for financial, mathematical,
  statistical and scientific computing

• Julia delivers lightning fast speed – speed improvements up to 11x
  for macroeconomic
  modeling, 225x
  for parallel
  supercomputing
  and 1,000x for insurance risk model
  estimation

• Julia is the only modern financial, mathematical, statistical or
  scientific language that can handle massive datasets updated in real
  time, such as financial tick data

• Julia runs on your desktop, laptop, enterprise server, private or
  public cloud

• Julia is optimized for supercomputers with
  accelerators and
  parallel
  computing

• JuliaFin is fully integrated with Excel and Bloomberg

• JuliaFin includes Miletus, a custom finance package to design and
  execute real-time trading strategies

• Julia is easy to learn with flexible syntax that is familiar to
  users of Python, R and Matlab

• Julia integrates well with existing code and platforms

• Julia code is elegant – advanced libraries make coding simple and
  reduce the number of lines of code – in some cases, by 90% or more –
  resulting in a solution that is faster, easier to code, analyze and
  debug

• Julia solves the two language problem – because Julia combines the
  ease of use and familiar syntax of Python, R, Matlab, or Stata with
  the speed of C, C++ or Java, programmers no longer need to estimate
  models in one language and reproduce them in a faster
  production language. With Julia, these steps can be performed in a
  single high-level, high-capacity, high-speed environment.

No wonder users such as
BlackRock, the
Federal Reserve Bank of New
York, Nobel
Laureate Thomas J.
Sargent,
and the world’s largest investment banks,
insurers,
risk managers, fund
managers, asset
managers, foreign exchange
analysts, energy
traders, commodity traders and others are switching to Julia.

By: julia on Abel Soares Siqueira

Re-posted from: https://abelsiqueira.com/blog/2017-02-07-nlpmodelsjl-cutestjl-and-other-nonlinear-optimization-packages-on-julia/

A couple of weeks ago me and Professor Dominique Orban have finally made a release of
CUTEst.jl, a wrapper for the CUTEst repository of problems for nonlinear
optimization (which I’ve mentioned before).
Along with this release, we’ve done a release of NLPModels.jl, the underlying
package. I think it’s time I explain a little about these packages, others,
and how to use them together.
If you want to see the output of the commands, you can open
this ASCIInema
side by side.