Capture
发表于 2025-3-25 04:41:26
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FATAL
发表于 2025-3-25 10:23:20
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Addictive
发表于 2025-3-25 14:52:27
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pus840
发表于 2025-3-25 17:50:52
978-3-642-09925-0Springer-Verlag Berlin Heidelberg 2009
掺和
发表于 2025-3-25 23:29:18
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Increment
发表于 2025-3-26 03:52:26
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lipoatrophy
发表于 2025-3-26 07:31:36
Conjugate Direction Methods for Quadratic Problems,tion. Consider the problem of finding . ∈ . satisfying ., where . ∈ ., . ∈ . and . is symmetric positive definite. The solution to this problem is also a solution of the optimization problem (.): .. Consider the point x̄ such that .. We can show that (1.2) are the necessary optimality conditions for problem (1.1).
agenda
发表于 2025-3-26 10:43:17
Conjugate Gradient Methods for Nonconvex Problems,us chapter a conjugate gradient algorithm is an iterative process which requires at each iteration the current gradient and the previous direction. The simple scheme for calculating the current direction was easy to extend to a nonquadratic problem ..
真实的你
发表于 2025-3-26 14:58:06
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nonplus
发表于 2025-3-26 19:28:55
https://doi.org/10.1007/3-540-36416-1inear Hestenes-Stiefel algorithm provided that the directional minimization is exact. Having that in mind and the fact that Hager and Zhang do not stipulate condition (2.68) in Theorem 2.14 their main convergence result is remarkable.