A Faster Quantum Algorithm for Semidefinite Programming via Robust IPM Framework

This paper studies a fundamental problem in convex optimization, which is to solve semidefinite programming (SDP) with high accuracy. This paper follows from existing robust SDP-based interior point method analysis due to [Huang, Jiang, Song, Tao and Zhang, FOCS 2022]. While, the previous work only provides an efficient implementation in the classical setting. This work provides a novel quantum implementation. We give a quantum second-order algorithm with high-accuracy in both the optimality and the feasibility of its output, and its running time depending on $\log(1/\epsilon)$ on well-conditioned instances. Due to the limitation of quantum itself or first-order method, all the existing quantum SDP solvers either have polynomial error dependence or low-accuracy in the feasibility

Thermal expansion behavior of co-extruded wood-plastic composites with glass-fiber reinforced shells

Coextruded wood-plastic composites (WPCs) with glass-fiber (GF) filled shells were manufactured, and their thermal expansion behavior was studied. A three-dimensional finite element model (FEM) considering differential properties of both shell and core layers was developed to predict the linear coefficient of thermal expansion (LCTE) of the material. It was shown that the LCTE values varied with composite structure and composition (i.e., core-shell thicknesses and materials). The use of GF-filled shells helped lower overall composite LCTE values. The imbalance of shell and core LCTE, and their moduli led to complex stress fields within a given composite system. The FEM predicted a trend of LCTE change with varying composite structures, which was in good agreement with the experimental data. This study provides for the first time a finite element modeling technique to optimize raw material composition and composite structure for optimizing thermal expansion behavior of co-extruded WPCs