Constraint Systems. Lab 06 - Customizing Search

Transcription

1 Constraint Systems Lab 06 - Customizing Search

2 CP Search in Google or-tools Search in or-tools is implemented via two main entities: A DecisionBuilderobject A stack of Decisionobjects The DecisionBuilderobject has one main method Next, which returns a Decisionobject For example: Calling Nextmay return a Decisionobject......that encode the choice point: x = v or x v

3 CP Search in Google or-tools Search in or-tools is implemented via two main entities: A DecisionBuilderobject A stack of Decisionobjects The Decisionobject has two main methods: Applythat enforces the decision (e.g. x = v) Refutethat enforces its opposite (e.g. x v) This is done by posting constraints or directly pruning the domains

4 CP Search in Google or-tools Whenever a feasible fix-point is reached: The Nextmethod is called to obtain a new decision The new decision is pushed on the stack Its Acceptmethod is called In case of a fail/domain wipe-out: The Refutemethod of the top-most decision is called The top-most decision is removed from the stack If the Nextmethod returns no decision, then a solution has been found

5 This Lab Session In this lab session: We will customize search for the server consolidation problem

6 This Lab Session In this lab session: We will customize search for the server consolidation problem A start-kit is available on the course web site The file vm-reassignment.pycontains: A base model (with symmetry breaking constraints) A template for a DecisionBuilderobject A few utility functions Decisionobjects cannot be customized using the python API You need to use the C++ API for that

7 A few words about python classes Class definition: class ClassName(<parent class, if any>): <block> Methods are functions with a mandatory first argument: def MethodName(self, <parameters>): <block> The selfargument is the object whose method is invoked It can be used to access properties Property xof the current object is accessed as self.x

8 A few words about python classes A constructor is a function with a special name: def init (self, <parameters>): <block> The constructor is invoked when an instance is created The constructor typically assigns one or more instance properties: An example: def init (self, x): self.x = x y = ClassName(10) # self.x = 10 in the instance

9 DecisionBuilder in the or-tools Python API A decision builder must inherit from PyDecisionBuilder class CustomDecisionBuilder(pywrapcp.PyDecisionBuilder): <block> No special notes about the constructor Only one mandatory method: def Next(self, solver): <block> Nextmust return a Decisionobject......or Noneif all variables have been assigned

10 DecisionBuilder in the or-tools Python API Methods of the solver can be used to build Decisionobjects Classical binary choice points ( x = v or x v) def AssignVariableValue(self, <variable>, <value>): Domain splitting ( x v or x > v) def SplitVariableDomain(self, <var>, <val>, <bool - start_with_lower_half>): Diving ( x = v) def AssignVariableValueOrFail(self, <var>, <val>):

11 A Few Final Notes about Customizing Search Customizing search in python for or-tools is not always a good idea It is easy (this is why we do it in the lab) But it is also very inefficient For practical problems: It is better to write DecisionBuilderclasses in C++ Swig wrappers can be used to obtain a python API Two important consequences (for fairness of comparison): In this session, we will use only python decision builders In this session, we will only look at the number of branches

12 Some Suggestions Some things you could try: Use specialized information to select the branching variable Use specialized information to select the branching value Use a different branching scheme (e.g. domain splitting) Dynamic symmetry breaking! Try to identify the most complex task in our problem: Finding a solution? Finding the optimal solution? Proving optimality? Use this insight to design your heuristic