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The Eclipse IDE has code completion tools that come packaged with the program. [15] [16] It includes notable support for Java, C++, and JavaScript code authoring. The Code Recommenders Eclipse project used to provide powerful intelligent completion, [17] but due to lack of resources, was dropped in Eclipse 2018–12, and then archived in July 2019.
Arm MAP, a performance profiler supporting Linux platforms.; AppDynamics, an application performance management solution [buzzword] for C/C++ applications via SDK.; AQtime Pro, a performance profiler and memory allocation debugger that can be integrated into Microsoft Visual Studio, and Embarcadero RAD Studio, or can run as a stand-alone application.
Dead code is normally considered dead unconditionally. Therefore, it is reasonable attempting to remove dead code through dead-code elimination at compile time. However, in practice it is also common for code sections to represent dead or unreachable code only under certain conditions, which may not be known at the time of compilation or assembly.
Optimization during code development using macros takes on different forms in different languages. In some procedural languages, such as C and C++, macros are implemented using token substitution. Nowadays, inline functions can be used as a type safe alternative in many cases.
The campaign also funded Zadrozny's creation of LiClipse, a paid closed source fork of Eclipse which bundles PyDev by default. [ 5 ] PyDev received improvements to type inference and a notable increase in contributions to code base when version 2.8 was released in July 2013. [ 6 ]
The CPLEX Optimizer has a modeling layer called Concert that provides interfaces to the C++, C#, and Java languages. There is a Python language interface based on the C interface. Finally, a stand-alone Interactive Optimizer executable is provided for debugging and other purposes.
Written in C++ and published under an MIT license, HiGHS provides programming interfaces to C, Python, Julia, Rust, R, JavaScript, Fortran, and C#. It has no external dependencies. A convenient thin wrapper to Python is available via the highspy PyPI package. Although generally single-threaded, some solver components can utilize multi-core ...
IPOPT can be called from various modeling environments: C, C++, Fortran, Java, R, Python, and others. [2] IPOPT is part of the COIN-OR project. IPOPT is designed to exploit 1st derivative and 2nd derivative information if provided (usually via automatic differentiation routines in modeling environments such as AMPL).