hi, I'm kind of new to optimization theory, and I have to maximize a multi-dimensional problem where I know the exact gradient and hessian. In other words, techniques such as BFGS are not sufficient because I don't want to approximate the Hessian (with an initial guess for example of H=I), I have the exact (analytical) Hessian and want to optimize my problem up to second order(adsbygoogle = window.adsbygoogle || []).push({});

i.e.

f(x + δ) ≈ f(x) + gT δ +.5*δTHδ (2nd order Taylor expansiona around δ)

Could someone please suggest a technique? Thanks in advance!

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# Optimization problem using exact Hessian

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