The Bregman method is utilized to quantitatively improve the simultaneous reconstruction of absorption and scattering coefficients for both Jacobian-based and gradient-based methods in quantitative photoacoustic tomography with multiple optical excitations. It is the synergistic combination of Bregman method and the total-variation regularization in achieving the desired quantitative improvemen...

Recently several methods were proposed for sparse optimization which make careful use of second-order information [11, 34, 20, 4] to improve local convergence rates. These methods construct a composite quadratic approximation using Hessian information, optimize this approximation using a first-order method, such as coordinate descent and employ a line search to ensure sufficient descent. Here w...

Graph regularized nonnegative matrix factorization (GNMF) decomposes a nonnegative data matrix X[Symbol:see text]R(m x n) to the product of two lower-rank nonnegative factor matrices, i.e.,W[Symbol:see text]R(m x r) and H[Symbol:see text]R(r x n) (r < min {m,n}) and aims to preserve the local geometric structure of the dataset by minimizing squared Euclidean distance or Kullback-Leibler (KL) di...

We test the efficiency of three minimization algorithms as applied to models of peptides and proteins. These include: the limited memory quasiŽ . Ž . Newton L-BFGS of Liu and Nocedal; the truncated Newton TN with Ž . automatic preconditioner of Nash; and the nonlinear conjugate gradients CG of Shanno and Phua. The molecules are modeled by two energy functions, one is Ž . the GROMOS87 united ato...

Traditional transmission travel-time tomography hinges on ray tracing techniques. We propose a PDE-based Eulerian approach to travel-time tomography so that we can avoid the cumbersome ray-tracing. We start from the eikonal equation, define a mismatching functional and derive the gradient of the nonlinear functional by an adjoint state method. The resulting forward and adjoint problems can be e...

Shape optimization based on shape calculus has received a lot of attention in recent years, particularly regarding the development, analysis, and modification efficient algorithms. In this paper we propose investigate nonlinear conjugate gradient methods Steklov-Poincar\'e-type metrics for solution problems constrained by partial differential equations. We embed these into general algorithmic f...

A Hessian matrix in full waveform inversion (FWI) is difficult to compute directly because of high computational cost and an especially large memory requirement. Therefore, Newton-like methods are rarely feasible in realistic largesize FWI problems. We modify the BFGS method to use a projected Hessian matrix that reduces both the computational cost and memory required, thereby making a quasiNew...

The Covid-19 outbreak has hit all countries across the globe, including Indonesia, in which impact is detrimental and costly. We investigated 14 determinants that could spatially influence cases Central Kalimantan South provinces mid-2020 by using Geographically Weighted Negative Binomial Regression (GWNBR) Mixed (MGWNBR). This study conducted iterative Limited-memory Broyden-Fletcher-Goldfarb-...

Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively in both academia industry. Recent studies demonstrated that adversarial attacks against such can maintain their effectiveness even when on other than targeted by attacker. This major property is known as transferability, makes CNNs ill-suited for security applications. In this...