Abstract: Traditionally, the projection matrix in compressive sensing (CS) is chosen as a random matrix. In recent years, we have seen that the performance of CS systems can be improved by using a ...

This project applies reinforcement learning (RL) to discover efficient algorithms for matrix multiplication. By simulating a learning environment for matrix operations, RL agents are trained to ...

Abstract: Complex matrix derivatives play an important role in matrix optimization, since they form a theoretical basis for the Karush-Kuhn-Tucker (KKT) conditions associated with matrix variables. We ...

This repository demonstrates a powerful, classical linear algebra technique—low-rank approximation via Singular Value Decomposition (SVD)—to dramatically accelerate common matrix operations like GEMM ...

A coupled resonator diplexer has been designed, fabricated, and tested. The design is based on synthesis of coupling matrix of a 3-port coupled resonator circuit using optimization. Unlike ...