Great User-friendly Software for Linear Programming
October 05, 2020

Great User-friendly Software for Linear Programming

Anonymous | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
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Overall Satisfaction with IBM CPLEX Optimization Studio

Our organization used IBM CPLEX Optimization Studio for solving large optimization problems in research and in demonstrations to students at the graduate level. We have more than 50 licenses for CPLEX and installed for Research and Laboratory Purposes. CPLEX addresses various business problems such as Integer Programming, Mixed Integer Programming, Quadratic Programming, etc.
  • Integer programming.
  • Mixed integer programming.
  • Quadratic programming.
  • Multi-objective optimization.
  • Data handling from different sources like Note Pad, etc.
  • Large size of MILP problems.
  • Various parameters to set.
  • Better for price.
  • Many model parameters/features.
  • No visualization.
Compared with MATLAB, CPLEX is a more user-friendly and simpler structure for writing models. This one also has a good return on investment.
Honestly, to say, I never contacted CPLEX but used its forum to know/clarify any issues I faced.
It's nice to use and with good optimization.

Do you think IBM CPLEX Optimization Studio delivers good value for the price?

Yes

Are you happy with IBM CPLEX Optimization Studio's feature set?

Yes

Did IBM CPLEX Optimization Studio live up to sales and marketing promises?

No

Did implementation of IBM CPLEX Optimization Studio go as expected?

Yes

Would you buy IBM CPLEX Optimization Studio again?

Yes

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.

IBM CPLEX Optimization Studio Feature Ratings

Connect to Multiple Data Sources
8
Extend Existing Data Sources
6
Automatic Data Format Detection
6
MDM Integration
5
Visualization
1
Interactive Data Analysis
4
Interactive Data Cleaning and Enrichment
5
Data Transformations
7
Data Encryption
7
Built-in Processors
2
Multiple Model Development Languages and Tools
6
Automated Machine Learning
5
Single platform for multiple model development
6
Flexible Model Publishing Options
8
Security, Governance, and Cost Controls
8