Hexaly Optimizer¶

Object-oriented APIs are provided for Python, allowing a full integration of Hexaly Optimizer in your Python business applications. Hexaly’s APIs are lightweight, with only a few classes to manipulate. Note that Hexaly Optimizer is a model & run math programming solver: having instantiated the model, no additional code has to be written in order to run the solver.

Build your model

First, you have to create a LocalSolver environment. It is the main class of the Hexaly Optimizer library. Then, use the methods of the class LSModel to build your model with expressions. Expressions are a particularly important concept in Hexaly Optimizer. In fact, every aspect of a model is an expression: variables, objectives and even constraints are LSExpression. There are 3 different ways to create these LSExpressions with the class LSModel:

You can use the available shortcut methods like LSModel.sum(), LSModel.eq(), LSModel.bool() or LSModel.sqrt().
You can also use the more generic version of these operators with the method LSModel.create_expression(). It takes the type of the expression to add as first argument, then the list of the operands of the expression. It is also possible to add operands one-by-one with the method LSExpression.add_operand(). See LSOperator for the complete list of available operators.
Finally, you can use the overloaded operators for common operations: +, -, *, /, <=, >=, ==, !=, >, <, **, [], &, ^, ~, |, ().

Most of these methods accept LSExpressions as arguments but also integers or double constants. If you prefer, you can also create constants explicitly with LSModel.create_constant().