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April 19, 20260 citationsOpen Access

Semidefinite relaxations for nonlinear elasticity with energies convex in the Cauchy-Green strain tensor

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DHDidier HenrionMKMilan KordaMKMartin Kružík

Key Points

  • The aim is to explore the minimization of stored energy density in nonlinear elasticity and address relaxation gaps in non-convex formulations.
  • Utilized the Le Dret-Raoult semidefinite projection for quasiconvex envelope calculations.
  • Analyzed the Lasserre moment-sum-of-squares hierarchy for convergence.
  • Explored conditions under which the first relaxation of the Lasserre hierarchy is exact.
  • No relaxation gap is found between the non-convex problem and its linear moment formulation.
  • The method provides a computationally efficient, mesh-free alternative to finite element methods.
  • Under specific conditions, computing the quasiconvex envelope simplifies to a small convex optimization problem.

Abstract

In nonlinear elasticity, finding the deformation of a material which minimizes a given stored energy density is a challenging calculus of variations problem which may fail to have minimizers: the energy optimal material forms infinitely fine microstructures (wrinkles) rather than deforming smoothly. In the case where the energy function is non-convex but frame indifferent and convex with respect to the Cauchy-Green strain tensor, we use the standard Le Dret-Raoult semidefinite projection formula for the quasiconvex envelope of the energy function to prove that there is no relaxation gap between the original non-convex calculus of variations problem and its linear moment formulation based on occupation measures. This implies convergence of the Lasserre moment-sum-of-squares (SOS) hierarchy and provides a computationally efficient, mesh-free numerical method that, unlike the finite element method, avoids undesirable mesh-dependent artifacts. Under the additional condition that the boundary condition is linear and the function is SOS convex in the strain tensor, we show that the first relaxation of the Lasserre hierarchy is exact. In other words, computing the quasiconvex envelope at a point boils down to solving a small convex semidefinite optimization problem.

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Cite This Study

Henrion et al. (2026) studied this question.

synapsesocial.com/papers/69e4713b010ef96374d8dd77https://doi.org/10.48550/arxiv.2604.13566
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