Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In solving a linear system with iterative methods, one is usually confronted with the dilemma of having to choose beten cheap, inefficient iterates over sparse search directions (e.g., coordinate descent), or expensive iterates in ll-chosen search directions (e.g., conjugate gradients). ,  propose to interpolate beten these two extremes, and show how to perform cheap iterations along non-sparse search directions, provided that these directions admit a new kind of sparse factorization. For example, if the search directions are the columns of a hierarchical matrix, then the cost of each iteration is typically logarithmic in the number of variables. Using some graph-theoretical results on low-stretch spanning trees,  deduce as a special case a nearly-linear time algorithm to approximate the minimal norm solution of a linear system $Bx= b$ where $B$ is the incidence matrix of a graph.  thereby can connect our results to recently proposed nearly-linear time solvers for Laplacian systems, which emerge here as a particular application of our sparse matrix factorization.
['Michael T. Schaub', 'Maguy Trefois', 'Paul Van Dooren', 'Jean-Charles Delvenne']
I am writing a research paper on a idea Sparse matrix factorizations for fast linear solvers with application to Laplacian systems
[{"from": "human", "value": "I am writing a research paper on a idea Sparse matrix factorizations for fast linear solvers with application to Laplacian systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In solving a linear system with iterative methods, one is usually confronted with the dilemma of having to choose beten cheap, inefficient iterates over sparse search directions (e.g., coordinate descent), or expensive iterates in ll-chosen search directions (e.g., conjugate gradients). ,  propose to interpolate beten these two extremes, and show how to perform cheap iterations along non-sparse search directions, provided that these directions admit a new kind of sparse factorization. For example, if the search directions are the columns of a hierarchical matrix, then the cost of each iteration is typically logarithmic in the number of variables. Using some graph-theoretical results on low-stretch spanning trees,  deduce as a special case a nearly-linear time algorithm to approximate the minimal norm solution of a linear system $Bx= b$ where $B$ is the incidence matrix of a graph.  thereby can connect our results to recently proposed nearly-linear time solvers for Laplacian systems, which emerge here as a particular application of our sparse matrix factorization."}]