The IBM Decision Optimization CPLEX Modeling for Python
Project description
Welcome to the IBM® Decision Optimization CPLEX Modeling for Python. Licensed under the Apache License v2.0.
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:
IBM® Decision Optimization CPLEX Optimizer Modeling for Python - with namespace docplex.mp
IBM® Decision Optimization CP Optimizer Modeling for Python - with namespace docplex.cp
Solving with CPLEX locally requires that IBM® ILOG CPLEX Optimization Studio V12.6.3 or V12.7.0 is installed on your machine.
Solving with the IBM® Decision Optimization on Cloud service requires that you register for an account and get the API key.
This library is numpy friendly.
Installation
pip install docplex
Changelog
Changed in 2.2.36:
Methods docplex.cp.model.export_model() and docplex.cp.model.import_model() have been added to respectively generate or parse a model in CPO format.
Methods docplex.cp.model.minimize() and docplex.cp.model.maximize() have been added to directly indicate an objective at model level.
Notebook example scheduling_tuto.ipynb contains an extensive tutorial to solve scheduling problems with CP.
Modeling methof sum() now supports sum of cumul expressions.
Methods docplex.cp.model.start_search() allows to start a new search sequence directly from the model object.
When setting context.solver.auto_publish is set, and using the CPLEX engine, KPIs and current objective are automatically published when the script is run on DOcplexcloud Python worker.
When setting context.solver.auto_publish is set, and using the CP engine, current objective is automatically published when the script is run on DOcplexcloud Python worker.
docplex.util.environment.Environment.set_stop_callback and docplex.util.environment.Environment.get_stop_callback are added so that you can add a callback when the DOcplexcloud job is aborted.
Changed in 2.1.28:
New methods Model.logical_or() and Model.logical_and() handle logical operations on binary variables.
DOcplex now supports CPLEX 12.7.1 and Benders decomposition. Set annotations on constraints and variables using the benders_annotation property and use the proper CPLEX parameters governing Benders decomposition.
CPLEX tutorials: in the documentation and as notebooks in the examples.
Fixed a bug in docplex.mp.solution.SolveSolution.display() and in docplex.mp.solution.Model.report_kpi() when using unicode variable names.
There’s now a simple command line interface for DOcplexcloud. It can be run in a terminal. python -m docplex.cli help for more info. That command line reads your DOcplexcloud credentials in your cplex_config.py file. It allows you to submit, list, delete jobs on DOcplexcloud. The cli is available in notebooks too, using the %docplex_cli magics. %docplex_cli help for some help. In a notebook, credentials can be passed using %docplex_url and %docplex_key magics.
Removing constraints in 1 call
Bug fixes when editing an existing model.
Bug fix in the relaxation mechanism when using docplexcloud.
Changed in 2.0.15:
Piecewise linear (PWL) functions are now supported. An API is now available on docplex.mp.model to create PWL functions and to create constraints using these PWL functions. PWL functions may be defined with breakpoints (default API) or by using slopes. Some simple arithmetic is also available to build new PWL functions by adding, subtracting, or scaling existing PWL functions.
DOcplex has undergone a significant overhaul effort that has resulted in an average of 30-50% improvement of modeling run-time performance. All parts of the API benefit from the performance improvements: creation of variables and constraints, removal of constraints, computation of sums of variables, and so on.
Constraints are now fully editable: the expressions of a constraint can be modified. Similarly, the objective expression can also be modified. This allows for complex workflows in which the model is modified after a solve and then solved again.
docplex is now available on Anaconda cloud and can be installed via the conda installation packager. See the IBM Anaconda home CPLEX Community Edition for Python is also provided on Anaconda Cloud to get free local solving capabilities with limitations.
Support of ~/.docplexrc configuration files for docplex.mp.context.Context is now dropped. This feature has been deprecated since 1.0.0.
Known incompatibility: class docplex.mp.model.AbstractModel moved to docplex.mp.absmodel.AbstractModel. Samples using this class have been updated.
Changed in 1.0.630:
Added support for CPLEX 12.7 and Python 3.5.
Upgraded the DOcplexcloud client to version 1.0.202.
Module docplex.mp.advmodel is now officially supported. This module provides support for efficient, specialized aggregator methods for large models.
When solving on DOcplexcloud, proxies can now be specified with the context.solver.docloud.proxies property.
When two constraints are defined with the same name, issue a warning instead of a fatal exception. The last constraint defined will take over the first one in the name directory.
Fix ValueError when passing a pandas DataFrame as variable keys (using DataFrame indexes).
Solution.get_values() returns a collection of variable values in one call.
docplex.mp.model no longer imports docloud.status. Any status previously initialized as JobSolveStatus.UNKNOWN is now initialized as None.
Minor improvements to notebooks and examples.
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