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Two-dimensional scaled averaging energy bound
Statement
Assume the model of Scalar finite-element substructuring model, all subdomain vertices primal, and the coefficient-scaled average of Coefficient-scaled averaging and BDDC. Let be the complementary jump projection on , and let be the energy norm induced by the partially assembled Schur operator. Then
where depends only on the polygon/mesh shape bounds and bounded side count. It is independent of coefficient jumps, mesh size, subdomain diameter, and subdomain count.
Facts & Assumptions
Given: Positive coefficients constant on each subdomain; matching quasiuniform meshes; all polygon vertices primal; and the partial Schur energy.
Coefficient-scaled averaging and BDDC: on an edge , the average weights are and .
Primal interface spaces and jump convention: every open 2D interface side has two incident subdomains, and vanishes at primal vertices.
Discrete Schur energy and the fractional trace norm: local Schur energy is equivalent to the squared trace seminorm modulo constants.
Two-dimensional logarithmic edge cutoff estimates: the edge cutoff has squared norm , and its product with a mean-zero trace costs at most times the local energy.
Proof
Fix an open edge and let , be the local discrete harmonic extensions. Write and similarly for , by harmonic minimality and the definition of . The -copy of has nodal values at interior edge nodes and zero at its primal endpoints, so it is ; the -copy has the complementary coefficient . The norm controls the full boundary seminorm of an edge trace extended by zero off : the cross-edge integrals are bounded by the endpoint weight in that norm, with constants controlled by the model's scaled Lipschitz boundary-chart bounds, polygon angles and side count. Thus the trace-Schur upper bound [F3] applies to these supported traces.
Set , , and . At a primal endpoint , , so . Applying the discrete Sobolev estimate [F4] to and , and noting that a boundary mean differs from the domain mean by at most the same deviation, gives The factors and appear because [F4] is unweighted while includes .
Write and on , and set . The multiplier estimate [F4] bounds the squared norm of each by . Also [F4] bounds by . For the -copy use and the triangle inequality in the squared norm. After multiplication by its Schur coefficient , the mean-zero terms are bounded by and the constant term has the same bound by step 1.2. Since the -side contribution is at most . Interchanging gives the same estimate for the -side contribution, using .
Sum over all interface sides. Each polygon has uniformly bounded side count, so each local energy occurs a bounded number of times. On each subdomain the complementary trace is the sum of these edge-supported traces; the squared norm of that sum is bounded by the side count times the sum of the squared norms. The resulting bound is , with no coefficient-contrast or subdomain-count factor.
Depends on
Used by
Dependency tree · two levels
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Sources
- Olof B. Widlund, Introduction to the Design and Theory of Domain Decomposition Algorithms, DD20 Tutorial, pp. 94–97 (standard reference, not scraped)
- Oliver Rheinbach, Parallel Scalable Iterative Substructuring, Chapter 2 §2.4, pp. 39–44 (standard reference, not scraped)