IBM ILOG CPLEX Optimization Studio vs. Python IDLE

It is well suited for solving large-sized, mixed-integer, and integer programming problems. Now, the new version supports for Multi-Objective optimization along with some new algorithms such as Benders Decomposition. It is less appropriate for quadratic programming problems where the objective function is the product of multiple variables. However, it's very easy to code any problem.

IDLE is a good option to run small scripts directly on the console, and that's it. It is a good exit when you don't want or need to open a proper IDE like Pycharm.

Incentivized

• Linear Programming
• Mixed-Integer Linear Programming
• Non-Linear Convex-Optimization
• Visualization
• Shadow Price Analysis
• Parameter Tuning

• Firstly, I would say Python IDLE interface is user friendly.
• Easy to learn for the beginners.
• Syntax highlighting is nice features.
• Smart indent helps a lot.

Incentivized

• Data handling from different sources like Note Pad, etc.
• Large size of MILP problems.
• Various parameters to set.

• Too simplistic
• Could not find source revision management integration support
• Only basic debugging is available
• Does not have data-science-specific notebooks (but can be installed separately)

Incentivized

It's nice to use and with good optimization.

The IDE Python IDLE is a good place to start as it helps you become familiar with the way Python works and understand its syntax.

This IDE allows you to configure the environment, font, size, colors, .....

It also looks like any simple text editor for any operating system, I work with Windows or Linux interchangeably, and you don't have to learn to use the IDE before programming.

Once the IDE is executed you can start programming directly in it.

Incentivized

Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.

Python IDLE support is what the community can give you. As it is free software, it does not have support provided by the manufacturer or by third-parties.

In any case, for most of the problems that normal users can find, the solution, or alternatives, can be found quickly online.

As this IDE is made in Python, the support is the same group of Python developers.

Incentivized

IBM CPLEX Optimization Studio covers wide range of problems in comparison to Gurobi and also offers a number of visualization tools for results analysis. It has better customization and parameter tuning options in comparison to Gurobi. It offers various API integrations such as Python, Java and C++ which is not the case with Gurobi.

It's easy to set up and run quick analysis in Python IDLE on my local machine. The output is direct and easy to read. But sometimes I prefer Jupyter Notebook when the datasets are large, since it would take too long to run on my local machine. It is easier to run Jupyter Notebook on my cloud desktop

Incentivized

• Faster computation leading to better internal customer relations
• Able to solve high variable problems with ease
• Anomaly detection became easier within business

• In a short time, we were able to develop several ML models for various teams to make accurate decisions.
• Beginners can easily understand and adapt to GUI.
• We could automate several manual validation tasks and so could reduce human intervention.

Incentivized