Regularization Algorithms for Solving Ill-Conditioned Matrix Problems in Inverse Problem Solving

Regularization algorithms are essential methods for addressing ill-conditioned matrix problems encountered in inverse problem solving. These techniques involve matrix modification and constraint implementation to ensure numerical stability and invertibility.