Abstract This paper proposes a new efficient method for a sparse Gaussian graphical model with hidden clustering structures by extending a dual spectral projected gradient (DSPG) method proposed by Nakagaki et al. (Comput Opt Appl, 76(1):33–68, 2020). We establish the global convergence of the proposed method to an optimal solution, and we show that the projection onto the feasible region can be solved with low computational complexity by using the pool-adjacent-violators algorithm. Numerical experiments on synthetic data and real data demonstrate the efficiency of the proposed method. The proposed method takes 0.91 s to achieve a similar solution to the direct application of the DSPG method which takes 4361 s.

Abstract In this work, we propose Model-and-Search (MAS), a novel local-search derivative-free optimization algorithm, and show that it is convergent to a Karush-Kuhn-Tucker point. MAS aims to optimize a deterministic function over a box-bounded domain and is designed to work well within a confined budget of function evaluations. In MAS, the search is oriented to improve the value of the incumbent by combining a set of techniques, including gradient estimation and quadratic model building and optimization. We propose a novel sensitivity-based approach to construct an incomplete quadratic model when points are not enough to build a complete quadratic surrogate model of the true function. The surrogate model is then used to guide the search. We present extensive computational results on a collection of 501 publicly available test problems with varying dimensions and complexity. The computational results demonstrate that MAS performs well regardless of problem convexity and smoothness.

We revisit and adapt the extended sequential quadratic method (ESQM) in Auslender (J Optim Theory Appl 156:183–212, 2013) for solving a class of difference-of-convex optimization problems whose constraints are defined as the intersection of level sets of Lipschitz differentiable functions and a simple compact convex set. Particularly, for this class of problems, we develop a variant of ESQM, called ESQM with extrapolation (ESQMe), which incorporates Nesterov’s extrapolation techniques for empirical acceleration. Under standard constraint qualifications, we show that the sequence generated by ESQMe clusters at a critical point if the extrapolation parameters are uniformly bounded above by a certain threshold. Convergence of the whole sequence and the convergence rate are established by assuming Kurdyka-Łojasiewicz (KL) property of a suitable potential function and imposing additional differentiability assumptions on the objective and constraint functions. In addition, when the objective and constraint functions are all convex, we show that linear convergence can be established if a certain exact penalty function is known to be a KL function with exponent 12; we also discuss how the KL exponent of such an exact penalty function can be deduced from that of the original extended objective (i.e., sum of the objective and the indicator function of the constraint set). Finally, we perform numerical experiments to demonstrate the empirical acceleration of ESQMe over a basic version of ESQM, and illustrate its effectiveness by comparing with the natural competing algorithm SCPls from Yu et al. (SIAM J Optim 31:2024–2054, 2021).

In the last few years, Total Generalized Variation (TGV) regularization has proved to be a valuable tool to remove blur and noise from an image while avoiding the staircase effect typical of the Total Variation (TV) and preserving the sharp edges. The TGV-regularized model depends on two regularization parameters whose values must be appropriately selected to obtain good-quality restored images. In this work, we propose the use of the Balancing Principle (BP) to formulate the TGV-based image restoration problem as a constrained minimization problem whose objective is an implicit function of the two regularization parameters depending on the image to be restored. The values of the regularization parameters, and the corresponding restored image, satisfying the optimality condition of the formulated problem guarantee that the data fidelity and regularization terms are balanced. We introduce a Scaled Gradient Projection (SGP) method specifically tailored to the BP-based optimization problem and test its effectiveness against the fixed-point iteration schemes proposed in the literature. The numerical results performed on real-life images, affected by both Gaussian and Poisson noise, show that the proposed approach can effectively restore input images corrupted by several kinds of noise and outperform the fixed-point strategies for the realization of the Balancing Principle.