Today, due to the advancement of technology, it is possible to store and process high-dimensional data. The main feature of high-dimensional data is the large number of variables for each individual. The variance-covariance matrix is the simplest tool for measuring the dependence between several variables. This matrix contains information about the pairwise relationship between the components of a random vector. The characteristic of this matrix is that it is negative-definite, which is a constraint on all elements of the matrix. Increasing the number of variables rapidly increases the dimension of this matrix, making it difficult to estimate. Thus, the sparse estimation of this matrix has been considered. On the other hand, estimating the inverse of the variance-covariance matrix, also known as the accuracy matrix, because it represents the conditional dependencies and conditional independences between each pair of variables conditional on other variables has been considered in many fields. Estimation of this matrix is useful in obtaining joint distribution of variables in the high dimension, in interpreting graphical models and also in the causal inference.