This paper proposes a novel general framework of Riemannian conjugate gradient methods, that is, methods on manifolds. The are important first-order optimization algorithms both in Euclidean spaces and While various types studied spaces, there have been fewer studies those In each iteration the previous search direction must be transported to current tangent space so it can added negative objec...

Krylov methods are a key way of solving large sparse linear systems equations but suffer from poor strong scalability on distributed memory machines. This is due to high synchronization costs numbers collective communication calls alongside low computational workload. Enlarged address this issue by decreasing the total iterations convergence, an artifact splitting initial residual and resulting...

Electromagnetic tomography technology is a new process tomography technology. The aim of this study is to develop a new image reconstruction algorithm suitable to electromagnetic tomography and verify its convergence. The advantages and development of electromagnetic tomography technology and image reconstruction algorithms are introduced briefly. Based on conjugate gradient algorithm, modified...

At the beginning of this century, which is characterized by huge flows emerging data, Dai and Liao proposed a pervasive conjugacy condition that triggered interest many optimization scholars. Recognized as sophisticated conjugate gradient (CG) algorithm after about two decades, here we share our visions thoughts on method in framework review study. In regard, first discuss modified Dai–Liao met...