Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The goal of Nonnegative Matrix Factorization (NMF) is to represent a large nonnegative matrix in an approximate way as a product of two significantly smaller nonnegative matrices.  shows in detail how an NMF algorithm based on Newton iteration can be derived using the general Karush-Kuhn-Tucker (KKT) conditions for first-order optimality. This algorithm is suited for parallel execution on systems with shared memory and also with message passing. Both versions re implemented and tested, delivering satisfactory speedup results.
['Markus Flatz', 'Marián Vajteršic']
I am writing a research paper on a idea Parallel Nonnegative Matrix Factorization via Newton Iteration
[{"from": "human", "value": "I am writing a research paper on a idea Parallel Nonnegative Matrix Factorization via Newton Iteration"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The goal of Nonnegative Matrix Factorization (NMF) is to represent a large nonnegative matrix in an approximate way as a product of two significantly smaller nonnegative matrices.  shows in detail how an NMF algorithm based on Newton iteration can be derived using the general Karush-Kuhn-Tucker (KKT) conditions for first-order optimality. This algorithm is suited for parallel execution on systems with shared memory and also with message passing. Both versions re implemented and tested, delivering satisfactory speedup results."}]