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Fractional palettes for random blow-ups
This page preserves an earlier research route and its local standing. The completed threshold proof is in the solution note.
The formulas below express color counts for the specified finite-template families. The threshold question becomes whether the resulting inequality holds for every weighted template.
Templates and two conflict graphs
Let be a fixed finite undirected graph allowing loops, with positive weights summing to one. Its adjacency matrix is the zero-one matrix . Powers of are used only to test existence of walks. The edge type has capacity
A loop in a complete blow-up denotes a clique bag. Write
- for the graph on types with , joining two distinct types if a closed seven-step walk contains them in nonconsecutive edge positions;
- for the graph on types having at least one triangular endpoint, meaning or . Two distinct types belong to an edge of if they can be oriented as (uv,xy) with .
For a graph and nonnegative demands , define
Here all independent sets, including nonmaximal ones, may be used. The constraints can be made equalities without increasing the objective: replace surplus portions of a palette by .
The distinction between and is essential. For two disjoint marked edges in a seven-cycle, the complementary paths have lengths or . A complete blow-up forces the direct edge in a construction. A random blow-up does not, and its coloring can deliberately avoid all such direct cross-edges.
Complete blow-ups
If is the complete blow-up of , with bag sizes , then
Here the notation on the left is for this fixed graph, not a minimum over all graphs of the same order and size.
First, a type has two disjoint physical copies on a common seven-cycle precisely when . To check the forward implication, remove its two nonconsecutive occurrences from the template walk. The complementary positive lengths sum to five. If the two remaining walks both join to , the even one has length two or four, and can be padded to four by a backtrack. Otherwise they are closed walks at and ; the odd one has length one or three. Appending , and padding if necessary, gives a four-walk from to . Conversely, a four-walk gives the closed seven-walk
All repeated types can be realized by distinct physical vertices.
These active types also force adjacent physical pairs to have distinct colors. More generally a -edge forces compatibility of every physical pair of its types, even when the two edges share a vertex. For types (uv,uw), inspect the complementary walks in a witnessing seven-walk. In the parallel pairing they join to and to . If the latter is odd, pad it to length five; otherwise the former is an odd closed walk of length one or three, and putting the edges (vu,uw) around it gives an odd - walk of length at most five. In the crossed pairing, append an appropriate marked edge to the even complementary walk to obtain such an odd walk. Pad to five and use fresh bag vertices, avoiding the actual shared vertex. This gives the required cycle. The same argument covers two adjacent edges of a single active type.
Thus each color uses at most one physical edge of each active type, and its active types form an independent set of . This proves the lower bound in (2).
Every independent set of is in fact a matching of template types: an active type conflicts with every other type sharing a template endpoint. Append a two-step backtrack along the other type to a closed five-walk containing the active type; one of its occurrences is nonconsecutive to the marked active occurrence. Consequently a fractional palette in (1) can be realized by pairing arbitrary physical edges of its types: their endpoint bags are disjoint and no seven-cycle contains two of them. Rounding the finitely many palette allocations and the bag sizes costs colors. For each inactive type, use a separate proper edge coloring of its block with colors. A color then consists of a matching, and two disjoint edges of that type never lie on a seven-cycle. This proves the upper bound.
Random blow-ups: formula
Fix probabilities for every edge type of . Construct by putting each allowed physical edge in independently with its type probability; unsupported pairs have no edges. In particular a looped bag is now a random graph, not a complete graph. Then, with probability tending to one,
The edge density tends to . The following proof requires only elementary concentration and a cut-norm counting argument, not a hypergraph matching theorem.
Uniform path supply and the lower bound
With high probability the random host has the following properties. Every allowed pair has cut discrepancy from its constant probability. Every prescribed endpoint has the expected positive-linear degree into any of a fixed finite collection of positive-linear subchunks. Every endpoint pair has positive-linear common neighborhood in a subchunk whenever its two edge types are supported. The last two assertions follow by concentration and a union bound over endpoint choices. The discrepancy assertion follows by a union bound over all pairs of subsets: a fixed discrepancy of order has probability exponentially small in , whereas there are only exponentially many subsets in .
It follows that every supported template walk of any fixed length , between two distinct prescribed physical endpoints, has a simple realization avoiding any bounded forbidden set. For , use the common-neighbor property. For , assign the internal occurrences to disjoint positive-linear subchunks, take the large endpoint neighborhoods in the first and last subchunks, and use cut-discrepancy counting for the intervening path. There are only finitely many walk patterns needed here.
A type has a self-conflict using connectors exactly when one endpoint is triangular. In a parallel pairing the odd connector is a closed three-walk at one endpoint. In a crossed pairing the two-walk between the endpoints, together with their edge, is a triangle. Conversely, a closed three-walk at one endpoint and a two-step backtrack at the other give the self-conflict.
Every two disjoint edges whose types form a -edge are therefore on a common seven-cycle, using the uniform two- and three-path supply. Adjacent physical pairs are also compatible. For types (uv,uw), a parallel witness either supplies a three-walk from to , which can be padded to five, or supplies a closed three-walk at , which can be enclosed by (vu,uw) to give a five-walk. In the crossed pairing, append a marked edge to the two-walk to obtain a three-walk between (v,w), then pad to five. Fresh internal vertices avoid the actual shared vertex. This reasoning also covers active self-pairs. Thus active blocks are rainbow and each color has -independent type support, proving the lower bound in (3).
Uniform induced-matching transversals
Here is the construction lemma used for the upper bound. Fix a list of supported types, allowing repetitions. In each type take an arbitrary pool of at least actual edges, for fixed . The pools may depend arbitrarily on the random host. For all sufficiently large , they contain a transversal that is an induced matching in the whole host.
Orient each pool consistently, arbitrarily for loop types. Count choices of one oriented edge by the product of their pool indicators and the indicators of all required cross nonedges between different selected edges. Use one factor for each unordered cross pair whose type is supported. Telescope, replacing each cross nonedge factor by its constant expectation . After fixing all other variables, every other factor depends on at most one endpoint of that cross pair. Their product consequently factors into a bounded function of its first endpoint and a bounded function of its second endpoint. The cut-discrepancy estimate applies uniformly, even to adversarial pools. Hence the count is
Choices with a repeated physical vertex contribute only . The remaining count is positive. The error is uniform over all pools, so the statement remains true after arbitrary previous choices have been deleted.
Packing palettes
Take an optimal fractional palette allocation in (1), with equality demands . Partition the physical edges of each active type among the palettes containing it, up to rounding and edge-count errors. For a palette , its pools can be taken to have the same size . Greedily extract induced-matching transversals until fewer than edges remain in each pool. Color each transversal with its own color and all residual edges separately. Let after .
Each such monochromatic induced matching is safe. Two of its edges on a seven-cycle cannot have complementary paths, because the one-edge path would be a forbidden cross-edge. The alternative would give a -conflict between their types, which is excluded by independence of .
For an inactive type, split its edges into equal pools and apply the same packing lemma. Induced matchings of this type are safe, since there is no self-conflict of the kind. They use at most colors. Taking arbitrarily large fixed shows that all inactive types together cost colors. This proves (3).
Consequences and limitations
A fixed template and probabilities with
would disprove the requested asymptotic. Formula (3) supplies an unbounded sequence of valid colorings; delete edges to leave exactly . Probabilities equal to zero or one in a numerical optimization can be perturbed into if both margins in (4) are fixed and strict. The finite-dimensional LP is continuous in its demand vector.
More generally , with , suffices after isolate padding.
For fixed weights, the search for (4) permits a further linear program. Inactive types have zero leading color cost. For active types choose and palettes , maximize
subject to and . A value strictly above would suffice, after perturbation.
Neither formula is asserted as the color cost of an arbitrary graph sequence. Regular pairs alone still do not give common neighbors for every pair of prescribed endpoints.
A different, proved general reduction supersedes this route: clean the original colored graph until distinct same-colored edges cannot co-occur in a closed seven-walk, retain its actual vertices as the fine template, and use a small degree-biased reweighting to recover strict super-Turan density. This proves that the original threshold conjecture is equivalent to the absence of any finite weighted counterexample .
Since has fewer active types and fewer conflicts, projection of palettes gives . Uniform thinning to probabilities just below one preserves a strict counterexample. Consequently the absence of a strict random-template counterexample is also equivalent to the original theorem. Equivalently, it suffices to prove the universal inequality whenever . The reduction supplies neither this inequality nor a counterexample, and there is no bounded order at which testing templates suffices.
A simple complete-template dual
For reference, there is an elementary valid dual for loopless complete templates. For an active edge , put
For a -independent palette, the sets are pairwise disjoint. Its edge types form a matching. An endpoint lying in another edge's common neighborhood gives a triangle incident with both marked edges, hence a closed seven-walk. If a vertex is a common neighbor of the endpoints of both and , use
Consequently is feasible in the fractional-coloring dual:
It does not give the universal half-edge bound. Replacing this weight by a minimum or sum of endpoint degrees is not valid; see the endpoint-packing counterexample.
Template examples
The searches in evidence/util/c7_template_lp_search.py, evidence/util/c7_template_lp_search_23.py, and evidence/util/c7_template_lp_density_23.py found no violation of the threshold inequality in their finite template samples. For variable type densities with , one candidate had density , close to the bipartite boundary. Here in the full-density calculation. The limiting one-component example has the gap . These observations suggest a boundary geometry to analyze, while the universal inequality requires an argument for arbitrary templates.
Further tools for the threshold inequality
The density-budget dual and stationarity equations describe joint optimization over demands and vertex weights, including dual ties. They do not justify ordinary endpoint-pushing symmetrization or force the host adjacency matrix to be regular.
The categorical-product allocation improves the ordinary two-orientation cost to , where is palette weight using only active edges that themselves lie in no triangle. Its strict amplification criterion would produce a counterexample, but no instance meeting the criterion was obtained.