»ã±¨±êÌâ (Title)£ºHybrid Projection Methods for Solution Decomposition in Large-Scale Bayesian Inverse Problems£¨´ó³ß¶È·´ÎÊÌâµÄ»ìºÏͶӰ·¨£©

»ã±¨ÈË (Speaker)£º½ª¼Îæè ÖúÀí½ÌÊÚ£¨²®Ã÷º²´óѧ£©

»ã±¨¹¦·ò (Time)£º2024Äê4ÔÂ26ÈÕ (ÖÜÎå) 10:00

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿GJ403

Ô¼ÇëÈË(Inviter)£º¼ÍÀö½à

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ÌáÒª£ºWe develop hybrid projection methods for computing solutions to large-scale inverse problems, where the solution represents a sum of different stochastic components. Such scenarios arise in many imaging applications where the reconstructed solution can be represented as a combination of two or more components and each component contains different smoothness or stochastic properties. We focus on the scenario where the solution is a sum of a sparse solution and a smooth solution. For computing solution estimates, we develop hybrid projection methods for solution decomposition that are based on a combined flexible and generalized Golub¨CKahan process. Numerical results from photoacoustic tomography and atmospheric inverse modeling demonstrate the potential for these methods to be used for anomaly detection.