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Skew 2-Dyck Paths: Kernel Method Analysis

Updated 21 December 2025
  • Skew 2-Dyck paths are lattice walks defined by U, D, and L steps that avoid immediate UL and LU patterns, ensuring unique combinatorial behavior.
  • The kernel method is applied to derive functional equations and an algebraic generating function that captures the enumeration and asymptotic growth of these paths.
  • An automaton model and structured recurrences provide insights into state transitions and forbidden patterns, with extensions to generalized skew t-Dyck paths.

A skew 2-Dyck path is a lattice walk in Z2\mathbb{Z}^2 of length $3n$, starting at (0,0)(0,0), never traversing below the xx-axis, and composed of three types of steps: U=(1,+1)U=(1,+1) (up), D=(2,2)D=(2,-2) (down), and L=(2,2)L=(2,-2) (left-down in "red," i.e., distinguished syntactically from DD). The local forbidden patterns ULUL and LULU (no up step immediately followed by a left-down, and vice versa) are enforced. Enumeration and structural results for these paths have recently been systematized via the kernel method, providing sharp algebraic and asymptotic statements as well as automata for pattern avoidance (Prodinger, 14 Dec 2025).

1. Combinatorial Definition and Local Constraints

A skew 2-Dyck path consists of steps U=(1,+1)U=(1,+1), D=(2,2)D=(2,-2), and L=(2,2)L=(2,-2), with LL being notationally distinct from DD. The walk starts at (0,0)(0,0), remains weakly above the xx-axis, and never contains the immediate step sequences ULUL or LULU. This syntactic restriction ensures specific path behaviors, crucial for distinguishing skew 2-Dyck paths from ordinary Dyck or Motzkin-type paths.

In this setting, DD and LL have identical geometric vectors but constitute separate syntactic tokens. The length of a path is $3n$ to maintain the endpoint constraint at height zero: up-steps add 1, both DD and LL decrease height by 2, and forward displacements always total $3n$.

2. Automaton and State Model

The set of skew 2-Dyck paths can be captured by a finite automaton that records both the current height and the class of the last step taken. The automaton is trilevel, reflecting the last step being UU, DD, or LL:

  • Layer 1: Arrived by UU; from here, only UU or DD steps are allowed.
  • Layer 2: Arrived by DD; all three steps are permitted.
  • Layer 3: Arrived by LL; only DD or LL are allowed (no UU).

Every transition adjusts height: UU increases by +1+1; DD and LL decrease by 2-2. This automaton ensures that ULUL and LULU are globally avoided. The originating state at (0,0)(0,0) is in Layer 1, and valid paths of interest return to height $0$ in any layer.

3. Functional Equations and Generating Functions

Let fi(z)f_i(z), gi(z)g_i(z), hi(z)h_i(z) denote the generating functions for prefixes ending at height ii in layers corresponding to UU, DD, LL, respectively, with zz marking steps. The generating functions F(u,z)F(u,z), G(u,z)G(u,z), H(u,z)H(u,z) are then

F(u,z)=i0fi(z)ui,G(u,z)=i0gi(z)ui,H(u,z)=i0hi(z)ui.F(u,z) = \sum_{i\geq0} f_i(z)u^i,\quad G(u,z) = \sum_{i\geq0} g_i(z)u^i,\quad H(u,z) = \sum_{i\geq0} h_i(z)u^i.

These satisfy the recurrences:

  • f0=1f_0 = 1, fi+1=zfi+zgif_{i+1} = z f_i + z g_i
  • gi=zfi+2+zgi+2+zhi+2g_i = z f_{i+2} + z g_{i+2} + z h_{i+2}
  • hi=zgi+2+zhi+2h_i = z g_{i+2} + z h_{i+2}

Translating to functional equations:

  • (I)  F(u,z)1=zu[F(u,z)+G(u,z)](I)\ \ F(u,z) - 1 = z u [F(u,z) + G(u,z)]
  • (II)  u2G(u,z)=z[F(u,z)f0uf1]+z[G(u,z)g0ug1]+z[H(u,z)h0uh1](II)\ \ u^2G(u,z) = z[F(u,z) - f_0 - u f_1] + z[G(u,z) - g_0 - u g_1] + z[H(u,z) - h_0 - u h_1]
  • (III)  u2H(u,z)=z[G(u,z)g0ug1]+z[H(u,z)h0uh1](III)\ \ u^2 H(u,z) = z[G(u,z) - g_0 - u g_1] + z[H(u,z) - h_0 - u h_1]

These coupled equations form the basis for applying the kernel method (Prodinger, 14 Dec 2025).

4. Kernel Method Solution

Application of the kernel method yields that FF, GG, HH can each be written with a denominator

Denominator(u)=zu4u3z2u2+2zuz3\text{Denominator}(u) = z u^4 - u^3 - z^2 u^2 + 2z u - z^3

with numerator and denominator sharing the roots usj(z)u - s_j(z). The cancellation of the "bad" branches—roots not expandable as power series in zz—ensures that only the root s4(z)s_4(z) (the small power series branch) leads to formal power series solutions. After appropriate residue calculations and matching initial conditions,

A(z)=1+g0(z)+h0(z)A(z) = 1 + g_0(z) + h_0(z)

where g0(z),h0(z)g_0(z), h_0(z) are determined in closed form:

g0(z)=1zs4(z)1,h0(z)=1zs4(z)zs4(z)21.g_0(z) = \frac{1}{z s_4(z)} - 1, \qquad h_0(z) = \frac{1}{z s_4(z)} - \frac{z}{s_4(z)^2} - 1.

The final algebraic generating function is

A(z)=16z+1318z+4z26zA(z) = \frac{1}{6z} + \frac{1}{3} - \frac{\sqrt{1 - 8z + 4z^2}}{6z}

which enumerates skew 2-Dyck paths of total length $3n$ (Prodinger, 14 Dec 2025).

5. Explicit Coefficient Formulas, Recurrence, and Asymptotics

The central enumerative result is the coefficient formula

an=[zn]A(z)=1ni=0n13i(ni)(ni+1),n1, a0=1a_n = [z^n]A(z) = \frac{1}{n} \sum_{i=0}^{n-1} 3^i \binom{n}{i} \binom{n}{i+1},\qquad n\ge 1, \ a_0=1

which provides integer counts of skew 2-Dyck paths of length $3n$.

A compact second-order recurrence is

(n+2)an+2=(8n+12)an+14(2n+1)an,a0=1, a1=1.(n+2)a_{n+2} = (8n+12)a_{n+1} - 4(2n+1)a_n, \qquad a_0=1, \ a_1=1.

Asymptotically,

anCρnn3/2a_n \sim C\,\rho^{-n} n^{-3/2}

where ρ=224\rho = \frac{2-\sqrt{2}}{4} is the dominant singularity, and C1.25C \approx 1.25 (Prodinger, 14 Dec 2025).

6. Generalization to Skew t-Dyck Paths

The analysis extends to "skew tt-Dyck paths" (where DD and LL become (t,t)(t,-t) steps), leading to functional equations with utu^t-shifts and a denominator of degree $2t$:

Δt(u)=zu2tu2t1z2u2t2+2zutz3\Delta_t(u) = z u^{2t} - u^{2t-1} - z^2 u^{2t-2} + 2z u^t - z^3

Unique algebraic generating functions of degree $2t$ enumerate skew tt-Dyck paths, recovering the t=2t=2 case above, and delivering similar singularity/bijection phenomena (Prodinger, 14 Dec 2025).

Comparable models have been studied for "decorated-Dyck" and "partial skew Dyck" paths, e.g., with color-distinguished down-steps or variants that allow additional southwest steps (1,1)(-1,-1) (subject to non-intersection constraints). The methodology—writing layered state recursions, translating to matrix-functional generating functions, and applying the kernel method—remains central and allows for the explicit enumeration of excursions, partial paths, and variations such as negative territory extension or step-weighted refinements (Prodinger, 2021).

The kernel method emerges as a powerful tool for resolving the algebraic structure of generating functions arising in these constrained lattice path models, providing residue-style cancellation of unwanted roots and yielding both exact and asymptotic enumerative formulae. Generalizations are systematic for other values of tt or for symmetric/dual models with interchange of up/down step structure.

These results bridge to classical problems in planar lattice path enumeration, algebraic generating functions, and k-ary tree bijections, and are of interest in combinatorics, statistical mechanics, and related fields.

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