omega #

This is an implementation of the omega algorithm, currently without "dark" and "grey" shadows, although the framework should be compatible with adding that part of the algorithm later.

The implementation closely follows William Pugh's "The omega test: a fast and practical integer programming algorithm for dependence analysis"

The MetaM level omega tactic takes a List Expr of facts, and tries to discharge the goal by proving False.

The user-facing omega tactic first calls false_or_by_contra, and then invokes the omega tactic on all hypotheses.

Pre-processing #

In the false_or_by_contra step, we:

The omega tactic pre-processes the hypotheses in the following ways:

After this preprocessing stage, we have a collection of linear inequalities (all using rather than <) and equalities in some set of atoms.

TODO: We should identify atoms up to associativity and commutativity, so that omega can handle problems such as a * b < 0 && b * a > 0 → False. This should be a relatively easy modification of the lookup function in OmegaM. After doing so, we could allow the preprocessor to distribute multiplication over addition.

Normalization #

Throughout the remainder of the algorithm, we apply the following normalization steps to all linear constraints:

Solving equalities #

The next step is to solve all equalities.

We first solve any equalities that have a ±1 coefficient; these allow us to eliminate that variable.

After this, there may be equalities remaining with all coefficients having absolute value greater than one. We select an equality c₀ + ∑ cᵢ * xᵢ = 0 with smallest minimal absolute value of the cᵢ, breaking ties by preferring equalities with smallest maximal absolute value. We let m = ∣cⱼ| + 1 where cⱼ is the coefficient with smallest absolute value.. We then add the new equality (bmod c₀ m) + ∑ (bmod cᵢ m) xᵢ = m α with α being a new variable. Here bmod is "balanced mod", taking values in [- m/2, (m - 1)/2]. This equality holds (for some value of α) because the left hand side differs from the left hand side of the original equality by a multiple of m. Moreover, in this equality the coefficient of xⱼ is ±1, so we can solve and eliminate xⱼ.

So far this isn't progress: we've introduced a new variable and eliminated a variable. However, this process terminates, as the pair (c, C) lexicographically decreases, where c is the smallest minimal absolute value and C is the smallest maximal absolute value amongst those equalities with minimal absolute value c. (Happily because we're running this in metaprogramming code, we don't actually need to prove this termination! If we later want to upgrade to a decision procedure, or to produce counterexamples we would need to do this. It's certainly possible, and existed in an earlier prototype version.)

Solving inequalities #

After solving all equalities, we turn to the inequalities.

We need to select a variable to eliminate; this choice is discussed below.

Shadows #

The omega algorithm indicates we should consider three subproblems, called the "real", "dark", and "grey" shadows. (In fact the "grey" shadow is a disjunction of potentially many problems.) Our problem is satisfiable if and only if the real shadow is satisfiable and either the dark or grey shadow is satisfiable.

Currently we do not implement either the dark or grey shadows, and thus if the real shadow is satisfiable we must fail, and report that we couldn't find a contradiction, even though the problem may be unsatisfiable.

In practical problems, it appears to be relatively rare that we fail because of not handling the dark and grey shadows.

Fortunately, in many cases it is possible to choose a variable to eliminate such that the real and dark shadows coincide, and the grey shadows are empty. In this situation we don't lose anything by ignoring the dark and grey shadows. We call this situation an exact elimination. A sufficient condition for exactness is that either all upper bounds on xᵢ have unit coefficient, or all lower bounds on xᵢ have unit coefficient. We always prefer to select the value of i so that this condition holds, if possible. We break ties by preferring to select a value of i that minimizes the number of new constraints introduced in the real shadow.

The real shadow: Fourier-Motzkin elimination #

The real shadow for a variable i is just the Fourier-Motzkin elimination.

We begin by discarding all inequalities involving the variable i.

Then, for each pair of constraints f ≤ c * xᵢ and c' * xᵢ ≤ f' with both c and c' positive (i.e. for each pair of an lower and upper bound on xᵢ) we introduce the new constraint c * f' - c' * f ≥ 0.

(Note that if there are only upper bounds on xᵢ, or only lower bounds on xᵢ this step simply discards inequalities.)

The dark and grey shadows #

For each such new constraint c' * f - c * f' ≤ 0, we either have the strengthening c * f' - c' * f ≥ c * c' - c - c' + 1 or we do not, i.e. c * f' - c' * f ≤ c * c' - c - c'. In the latter case, combining this inequality with f' ≥ c' * xᵢ we obtain c' * (c * xᵢ - f) ≤ c * c' - c - c' and as we already have c * xᵢ - f ≥ 0, we conclude that c * xᵢ - f = j for some j = 0, 1, ..., (c * c' - c - c')/c' (with, as usual the division rounded down).

Note that the largest possible value of j occurs with c' is as large as possible.

Thus the "dark" shadow is the variant of the real shadow where we replace each new inequality with its strengthening. The "grey" shadows are the union of the problems obtained by taking a lower bound f ≤ c * xᵢ for xᵢ and some j = 0, 1, ..., (c * m - c - m)/m, where m is the largest coefficient c' appearing in an upper bound c' * xᵢ ≤ f' for xᵢ, and adding to the original problem (i.e. without doing any Fourier-Motzkin elimination) the single new equation c * xᵢ - f = j, and the inequalities c * xᵢ - f > (c * m - c - m)/m for each previously considered lower bound.

As stated above, the satisfiability of the original problem is in fact equivalent to the satisfiability of the real shadow, and the satisfiability of either the dark shadow, or at least one of the grey shadows.

TODO: implement the dark and grey shadows!

Disjunctions #

The omega algorithm as described above accumulates various disjunctions, either coming from natural subtraction, or from the dark and grey shadows.

When we encounter such a disjunction, we store it in a list of disjunctions, but do not immediately split it.

Instead we first try to find a contradiction (i.e. by eliminating equalities and inequalities) without the disjunctive hypothesis. If this fails, we then retrieve the first disjunction from the list, split it, and try to find a contradiction in both branches.

(Note that we make no attempt to optimize the order in which we split disjunctions: it's currently on a first in first out basis.)

The work done eliminating equalities can be reused when splitting disjunctions, but we need to redo all the work eliminating inequalities in each branch.

Future work #