non-convex optimization

基本解釋

Many scientific, engineering and economic areas involve the optimization of complex, nonlinear and possibly non-convex problems.
科學(xué)領(lǐng)域，工程領(lǐng)域和經(jīng)濟(jì)領(lǐng)域都涉及到很多復(fù)雜的、非線性的甚至非凸形式的最優(yōu)化問題。
When a non-conic convex optimization problem or its dual was infeasible, we considered the feasibility for the new system by introducing a concept of feasible distance.
當(dāng)一般的非錐凸最優(yōu)化問題或其對偶不可行時，通過引入可行距離這一概念，討論新系統(tǒng)的可行性，并考察了在新系統(tǒng)中可行距離的性質(zhì)，得到了與其等價的可計算的優(yōu)化形式。
Due to the non-convex of the prior function and hyper-parameters, we use the dynamic posterior simulation rather than the general optimization methods to get reconstruction image.
由于采用的先驗函數(shù)是非凸的并包含超驗參數(shù)，一般的優(yōu)化方法難以處理，采用動態(tài)后驗?zāi)M的方法可以很好地解決這些問題。