IBM® Decision Optimization CPLEX® Modeling for Python¶
With this library, you can quickly and easily add the power of optimization to your application. You can model your problems by using the Python API and solve them on the cloud with the IBM Decision Optimization on Cloud service or on your computer with IBM ILOG® CPLEX Optimization Studio.
This library is composed of 2 modules:
This API is licensed under the Apache License, Version 2.0, and is numpy/pandas friendly. It is available from various sources:
What is Decision Optimization aka Prescriptive Analytics?¶
Prescriptive analytics technology recommends actions based on desired outcomes, taking into account specific scenarios, resources, and knowledge of past and current events. This insight can help your organization make better decisions and have greater control of business outcomes.
Prescriptive analytics is the next step on the path to insight-based actions. It creates value through synergy with predictive analytics, which analyzes data to predict future outcomes. Prescriptive analytics takes that insight to the next level by suggesting the optimal way to handle that future situation. Organizations that can act fast in dynamic conditions and make superior decisions in uncertain environments gain a strong competitive advantage.
- For example:
- Automate complex decisions and trade-offs to better manage limited resources.
- Take advantage of a future opportunity or mitigate a future risk.
- Proactively update recommendations based on changing events.
- Meet operational goals, increase customer loyalty, prevent threats and fraud, and optimize business processes.
Discovering the IBM Decision Optimization technologies¶
If you are new to optimization technologies, these topics present an overview of the algorithms, their specific application domains, and a list of books and free online trainings.
Getting started with DOcplex¶
Developing with DOcplex¶
Mathematical Programming Modeling for Python using docplex.mp (DOcplex.MP)¶
Constraint Programming Modeling for Python using docplex.cp (DOcplex.CP)¶
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