MLIR  17.0.0git
Public Member Functions | List of all members
mlir::presburger::LexSimplex Class Reference

A class for lexicographic optimization without any symbols. More...

#include "mlir/Analysis/Presburger/Simplex.h"

+ Inheritance diagram for mlir::presburger::LexSimplex:
+ Collaboration diagram for mlir::presburger::LexSimplex:

Public Member Functions

 LexSimplex (unsigned nVar)
 
 LexSimplex (const IntegerRelation &constraints)
 
MaybeOptimum< SmallVector< Fraction, 8 > > findRationalLexMin ()
 Return the lexicographically minimum rational solution to the constraints. More...
 
MaybeOptimum< SmallVector< MPInt, 8 > > findIntegerLexMin ()
 Return the lexicographically minimum integer solution to the constraints. More...
 
bool isSeparateInequality (ArrayRef< MPInt > coeffs)
 Return whether the specified inequality is redundant/separate for the polytope. More...
 
bool isRedundantInequality (ArrayRef< MPInt > coeffs)
 
- Public Member Functions inherited from mlir::presburger::LexSimplexBase
 ~LexSimplexBase () override=default
 
void addInequality (ArrayRef< MPInt > coeffs) final
 Add an inequality to the tableau. More...
 
unsigned getSnapshot ()
 Get a snapshot of the current state. This is used for rolling back. More...
 
- Public Member Functions inherited from mlir::presburger::SimplexBase
 SimplexBase ()=delete
 
virtual ~SimplexBase ()=default
 
bool isEmpty () const
 Returns true if the tableau is empty (has conflicting constraints), false otherwise. More...
 
unsigned getNumVariables () const
 Returns the number of variables in the tableau. More...
 
unsigned getNumConstraints () const
 Returns the number of constraints in the tableau. More...
 
void addEquality (ArrayRef< MPInt > coeffs)
 Add an equality to the tableau. More...
 
void appendVariable (unsigned count=1)
 Add new variables to the end of the list of variables. More...
 
void addDivisionVariable (ArrayRef< MPInt > coeffs, const MPInt &denom)
 Append a new variable to the simplex and constrain it such that its only integer value is the floor div of coeffs and denom. More...
 
void markEmpty ()
 Mark the tableau as being empty. More...
 
unsigned getSnapshot () const
 Get a snapshot of the current state. More...
 
unsigned getSnapshotBasis ()
 Get a snapshot of the current state including the basis. More...
 
void rollback (unsigned snapshot)
 Rollback to a snapshot. This invalidates all later snapshots. More...
 
void intersectIntegerRelation (const IntegerRelation &rel)
 Add all the constraints from the given IntegerRelation. More...
 
void print (raw_ostream &os) const
 Print the tableau's internal state. More...
 
void dump () const
 

Additional Inherited Members

- Protected Types inherited from mlir::presburger::SimplexBase
enum class  Orientation { Row , Column }
 
enum class  UndoLogEntry {
  RemoveLastConstraint , RemoveLastVariable , UnmarkEmpty , UnmarkLastRedundant ,
  RestoreBasis
}
 Enum to denote operations that need to be undone during rollback. More...
 
- Protected Member Functions inherited from mlir::presburger::LexSimplexBase
 LexSimplexBase (unsigned nVar)
 
 LexSimplexBase (unsigned nVar, const llvm::SmallBitVector &isSymbol)
 
 LexSimplexBase (const IntegerRelation &constraints)
 
 LexSimplexBase (const IntegerRelation &constraints, const llvm::SmallBitVector &isSymbol)
 
void appendSymbol ()
 Add new symbolic variables to the end of the list of variables. More...
 
LogicalResult moveRowUnknownToColumn (unsigned row)
 Try to move the specified row to column orientation while preserving the lexicopositivity of the basis transform. More...
 
LogicalResult addCut (unsigned row)
 Given a row that has a non-integer sample value, add an inequality to cut away this fractional sample value from the polytope without removing any integer points. More...
 
void undoLastConstraint () final
 Undo the addition of the last constraint. More...
 
unsigned getLexMinPivotColumn (unsigned row, unsigned colA, unsigned colB) const
 Given two potential pivot columns for a row, return the one that results in the lexicographically smallest sample vector. More...
 
- Protected Member Functions inherited from mlir::presburger::SimplexBase
 SimplexBase (unsigned nVar, bool mustUseBigM)
 Construct a SimplexBase with the specified number of variables and fixed columns. More...
 
 SimplexBase (unsigned nVar, bool mustUseBigM, const llvm::SmallBitVector &isSymbol)
 
std::optional< unsigned > findAnyPivotRow (unsigned col)
 Return any row that this column can be pivoted with, ignoring tableau consistency. More...
 
void swapRowWithCol (unsigned row, unsigned col)
 Swap the row with the column in the tableau's data structures but not the tableau itself. More...
 
void pivot (unsigned row, unsigned col)
 Pivot the row with the column. More...
 
void pivot (Pivot pair)
 
const UnknownunknownFromIndex (int index) const
 Returns the unknown associated with index. More...
 
const UnknownunknownFromColumn (unsigned col) const
 Returns the unknown associated with col. More...
 
const UnknownunknownFromRow (unsigned row) const
 Returns the unknown associated with row. More...
 
UnknownunknownFromIndex (int index)
 Returns the unknown associated with index. More...
 
UnknownunknownFromColumn (unsigned col)
 Returns the unknown associated with col. More...
 
UnknownunknownFromRow (unsigned row)
 Returns the unknown associated with row. More...
 
unsigned addZeroRow (bool makeRestricted=false)
 Add a new row to the tableau and the associated data structures. More...
 
unsigned addRow (ArrayRef< MPInt > coeffs, bool makeRestricted=false)
 Add a new row to the tableau and the associated data structures. More...
 
void swapRows (unsigned i, unsigned j)
 Swap the two rows/columns in the tableau and associated data structures. More...
 
void swapColumns (unsigned i, unsigned j)
 
void removeLastConstraintRowOrientation ()
 Remove the last constraint, which must be in row orientation. More...
 
void undo (UndoLogEntry entry)
 Undo the operation represented by the log entry. More...
 
unsigned getNumFixedCols () const
 Return the number of fixed columns, as described in the constructor above, this is the number of columns beyond those for the variables in var. More...
 
unsigned getNumRows () const
 
unsigned getNumColumns () const
 
- Protected Attributes inherited from mlir::presburger::SimplexBase
bool usingBigM
 Stores whether or not a big M column is present in the tableau. More...
 
unsigned nRedundant
 The number of redundant rows in the tableau. More...
 
unsigned nSymbol
 The number of parameters. More...
 
Matrix tableau
 The matrix representing the tableau. More...
 
bool empty
 This is true if the tableau has been detected to be empty, false otherwise. More...
 
SmallVector< UndoLogEntry, 8 > undoLog
 Holds a log of operations, used for rolling back to a previous state. More...
 
SmallVector< SmallVector< int, 8 >, 8 > savedBases
 Holds a vector of bases. More...
 
SmallVector< int, 8 > rowUnknown
 These hold the indexes of the unknown at a given row or column position. More...
 
SmallVector< int, 8 > colUnknown
 
SmallVector< Unknown, 8 > con
 These hold information about each unknown. More...
 
SmallVector< Unknown, 8 > var
 

Detailed Description

A class for lexicographic optimization without any symbols.

This also provides support for integer-exact redundancy and separateness checks.

Definition at line 482 of file Simplex.h.

Constructor & Destructor Documentation

◆ LexSimplex() [1/2]

mlir::presburger::LexSimplex::LexSimplex ( unsigned  nVar)
inlineexplicit

Definition at line 484 of file Simplex.h.

◆ LexSimplex() [2/2]

mlir::presburger::LexSimplex::LexSimplex ( const IntegerRelation constraints)
inlineexplicit

Definition at line 488 of file Simplex.h.

Member Function Documentation

◆ findIntegerLexMin()

MaybeOptimum< SmallVector< MPInt, 8 > > LexSimplex::findIntegerLexMin ( )

◆ findRationalLexMin()

MaybeOptimum< SmallVector< Fraction, 8 > > LexSimplex::findRationalLexMin ( )

Return the lexicographically minimum rational solution to the constraints.

We simply make the tableau consistent while maintaining a lexicopositive basis transform, and then return the sample value.

If the tableau becomes empty, we return empty.

Let the variables be x = (x_1, ... x_n). Let the basis unknowns be y = (y_1, ... y_n). We have that x = A*y + b for some n x n matrix A and n x 1 column vector b.

As we will show below, A*y is either zero or lexicopositive. Adding a lexicopositive vector to b will make it lexicographically greater, so A*y + b is always equal to or lexicographically greater than b. Thus, since we can attain x = b, that is the lexicographic minimum.

We have that that every column in A is lexicopositive, i.e., has at least one non-zero element, with the first such element being positive. Since for the tableau to be consistent we must have non-negative sample values not only for the constraints but also for the variables, we also have x >= 0 and y >= 0, by which we mean every element in these vectors is non-negative.

Proof that if every column in A is lexicopositive, and y >= 0, then A*y is zero or lexicopositive. Begin by considering A_1, the first row of A. If this row is all zeros, then (A*y)_1 = (A_1)*y = 0; proceed to the next row. If we run out of rows, A*y is zero and we are done; otherwise, we encounter some row A_i that has a non-zero element. Every column is lexicopositive and so has some positive element before any negative elements occur, so the element in this row for any column, if non-zero, must be positive. Consider (A*y)_i = (A_i)*y. All the elements in both vectors are non-negative, so if this is non-zero then it must be positive. Then the first non-zero element of A*y is positive so A*y is lexicopositive.

Otherwise, if (A_i)*y is zero, then for every column j that had a non-zero element in A_i, y_j is zero. Thus these columns have no contribution to A*y and we can completely ignore these columns of A. We now continue downwards, looking for rows of A that have a non-zero element other than in the ignored columns. If we find one, say A_k, once again these elements must be positive since they are the first non-zero element in each of these columns, so if (A_k)*y is not zero then we have that A*y is lexicopositive and if not we add these to the set of ignored columns and continue to the next row. If we run out of rows, then A*y is zero and we are done.

Definition at line 217 of file Simplex.cpp.

References mlir::presburger::Empty, mlir::failed(), and mlir::presburger::SimplexBase::markEmpty().

Referenced by mlir::presburger::IntegerRelation::findRationalLexMin().

◆ isRedundantInequality()

bool LexSimplex::isRedundantInequality ( ArrayRef< MPInt coeffs)

◆ isSeparateInequality()

bool LexSimplex::isSeparateInequality ( ArrayRef< MPInt coeffs)

Return whether the specified inequality is redundant/separate for the polytope.

Redundant means every point satisfies the given inequality, and separate means no point satisfies it.

These checks are integer-exact.

Definition at line 319 of file Simplex.cpp.

References mlir::presburger::LexSimplexBase::addInequality(), and findIntegerLexMin().

Referenced by mlir::presburger::SymbolicLexSimplex::computeSymbolicIntegerLexMin(), and isRedundantInequality().


The documentation for this class was generated from the following files: