IBM ILOG CPLEX Optimization Studio vs. NVIDIA RAPIDS

In my opinon, if the problem is less than 5000 variables, one should try to solve with free available solver rather than directly going for a commercial license of IBM CPLEX Optimization Studio. In my opinion, if the priority is not in terms of solving time with higher number of variables, even then one can go for free solvers like CBC, IPOPT, SCIP. In my opinion, if priority is solving time and number of variables is also high, only in that case one should prefer going for a commercial license.

NVIDIA RAPIDS is great for integrated and planned machine learning and deep learning journey. It is excellent if you have big data with defined processes to be improved and monitored. It is less effective if the project is continuously changed and the data are to be prepared and cleaned a lot and [in] many different ways.

Incentivized

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

• Visualization
• Deep learning pipeline
• State of the art libraries

Incentivized

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

• Its not flexible and cost effective for all sizes of organizations.
• I appreciate it has hassle-free integration.

Incentivized

It's nice to use and with good optimization.

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

Compared with MATLAB, CPLEX is a more user-friendly and simpler structure for writing models. This one also has a good return on investment.

RAPIDS GPU accelerates machine learning to make the entire data science and analytics workflows run faster, also helps build databases and machine learning applications effectively. It also allows faster model deployment and iterations to increase machine learning model accuracy. The great value of money.

Incentivized

• Better for price.
• Many model parameters/features.
• No visualization.

• Hassle free integration.
• Top model accuracy.
• Reduce training time.

Incentivized