As an implementation form of basis function, interpolation matrices (IMs) have a crucial impact on parametric level set method (PLSM)-based structural topology optimization (STO). However, there are few studies on compressing IM into triangular matrix (TM) with less storage and computation. Algorithm 1 using LU decomposition and Algorithm 2 using innovative asymmetric basis functions that transform the IMs of compactly supported radial basis functions (CSRBFs) into highly sparse TMs are proposed. Theoretical derivation and numerical experiments show that they effectively improve computational efficiency.
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