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

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

Inheritance 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< DynamicAPInt, 8 > >	findIntegerLexMin ()
	Return the lexicographically minimum integer solution to the constraints. More...

bool	isSeparateInequality (ArrayRef< DynamicAPInt > coeffs)
	Return whether the specified inequality is redundant/separate for the polytope. More...

bool	isRedundantInequality (ArrayRef< DynamicAPInt > coeffs)

Public Member Functions inherited from mlir::presburger::LexSimplexBase
	~LexSimplexBase () override=default

void	addInequality (ArrayRef< DynamicAPInt > 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< DynamicAPInt > 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< DynamicAPInt > coeffs, const DynamicAPInt &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 Unknown &	unknownFromIndex (int index) const
	Returns the unknown associated with index. More...

const Unknown &	unknownFromColumn (unsigned col) const
	Returns the unknown associated with col. More...

const Unknown &	unknownFromRow (unsigned row) const
	Returns the unknown associated with row. More...

Unknown &	unknownFromIndex (int index)
	Returns the unknown associated with index. More...

Unknown &	unknownFromColumn (unsigned col)
	Returns the unknown associated with col. More...

Unknown &	unknownFromRow (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< DynamicAPInt > 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...

IntMatrix	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 478 of file Simplex.h.

Constructor & Destructor Documentation

◆ LexSimplex() [1/2]

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

inlineexplicit

Definition at line 480 of file Simplex.h.

◆ LexSimplex() [2/2]

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

inlineexplicit

Definition at line 484 of file Simplex.h.

Member Function Documentation

◆ findIntegerLexMin()

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

Return the lexicographically minimum integer solution to the constraints.

Note: this should be used only when the lexmin is really needed. To obtain any integer sample, use Simplex::findIntegerSample as that is more robust.

Definition at line 306 of file Simplex.cpp.

References mlir::presburger::LexSimplexBase::addCut(), mlir::presburger::Empty, mlir::presburger::Fraction::getAsInteger(), mlir::presburger::MaybeOptimum< T >::isEmpty(), mlir::presburger::MaybeOptimum< T >::isUnbounded(), and mlir::presburger::Unbounded.

Referenced by mlir::presburger::SymbolicLexSimplex::computeSymbolicIntegerLexMin(), mlir::presburger::IntegerRelation::findIntegerLexMin(), and isSeparateInequality().

◆ 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 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 235 of file Simplex.cpp.

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

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