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Julia

469
616
+ 1
156
MATLAB

758
655
+ 1
34
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Julia vs MATLAB: What are the differences?

Julia: A high-level, high-performance dynamic programming language for technical computing. Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library; MATLAB: A high-level language and interactive environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.

Julia and MATLAB can be categorized as "Languages" tools.

"Lisp-like Macros" is the top reason why over 7 developers like Julia, while over 8 developers mention "Simulink" as the leading cause for choosing MATLAB.

Julia is an open source tool with 22.7K GitHub stars and 3.43K GitHub forks. Here's a link to Julia's open source repository on GitHub.

According to the StackShare community, MATLAB has a broader approval, being mentioned in 12 company stacks & 23 developers stacks; compared to Julia, which is listed in 5 company stacks and 5 developer stacks.

Decisions about Julia and MATLAB
Alexander Nozik
Senior researcher at MIPT · | 3 upvotes · 137.3K views
Migrated
from
JuliaJulia
to
KotlinKotlin

After writing a project in Julia we decided to stick with Kotlin. Julia is a nice language and has superb REPL support, but poor tooling and the lack of reproducibility of the program runs makes it too expensive to work with. Kotlin on the other hand now has nice Jupyter support, which mostly covers REPL requirements.

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Pros of Julia
Pros of MATLAB
  • 21
    Fast Performance and Easy Experimentation
  • 21
    Designed for parallelism and distributed computation
  • 17
    Free and Open Source
  • 16
    Lisp-like Macros
  • 16
    Calling C functions directly
  • 16
    Dynamic Type System
  • 15
    Multiple Dispatch
  • 9
    Powerful Shell-like Capabilities
  • 7
    REPL
  • 7
    Jupyter notebook integration
  • 4
    String handling
  • 4
    Emojis as variable names
  • 3
    Interoperability
  • 18
    Simulink
  • 5
    Functions, statements, plots, directory navigation easy
  • 4
    Model based software development
  • 3
    S-Functions
  • 2
    REPL
  • 1
    Simple variabel control
  • 1
    Solve invertible matrix

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Cons of Julia
Cons of MATLAB
  • 5
    Immature library management system
  • 4
    Slow program start
  • 3
    JIT compiler is very slow
  • 3
    Poor backwards compatibility
  • 2
    Bad tooling
  • 2
    No static compilation
  • 1
    Parameter-value pairs syntax to pass arguments clunky
  • 0
    Does not support named function arguments
  • 0
    Doesn't allow unpacking tuples/arguments lists with *

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- No public GitHub repository available -

What is Julia?

Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.

What is MATLAB?

Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or traditional programming languages, such as C/C++ or Java.

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What companies use Julia?
What companies use MATLAB?
See which teams inside your own company are using Julia or MATLAB.
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What tools integrate with Julia?
What tools integrate with MATLAB?

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What are some alternatives to Julia and MATLAB?
Python
Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
R Language
R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.
Rust
Rust is a systems programming language that combines strong compile-time correctness guarantees with fast performance. It improves upon the ideas of other systems languages like C++ by providing guaranteed memory safety (no crashes, no data races) and complete control over the lifecycle of memory.
Golang
Go is expressive, concise, clean, and efficient. Its concurrency mechanisms make it easy to write programs that get the most out of multicore and networked machines, while its novel type system enables flexible and modular program construction. Go compiles quickly to machine code yet has the convenience of garbage collection and the power of run-time reflection. It's a fast, statically typed, compiled language that feels like a dynamically typed, interpreted language.
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
See all alternatives