Recent Advances on the Development of Protein-Based Adhesives for Wood Composite Materials—A Review

The industrial market depends intensely on wood-based composites for buildings, furniture, and construction, involving significant developments in wood glues since 80% of wood-based products use adhesives. Although biobased glues have been used for many years, notably proteins, they were replaced by synthetic ones at the beginning of the 20th century, mainly due to their better moisture resistance. Currently, most wood adhesives are based on petroleum-derived products, especially formaldehyde resins commonly used in the particleboard industry due to their high adhesive performance. However, formaldehyde has been subjected to strong regulation, and projections aim for further restrictions within wood-based panels from the European market, due to its harmful emissions. From this perspective, concerns about environmental footprint and the toxicity of these formulations have prompted researchers to re-investigate the utilization of biobased materials to formulate safer alternatives. In this regard, proteins have sparked a new and growing interest in the potential development of industrial adhesives for wood due to their advantages, such as lower toxicity, renewable sourcing, and reduced environmental footprint. This work presents the recent developments in the use of proteins to formulate new wood adhesives. Herein, it includes the historical development of wood adhesives, adhesion mechanism, and the current hotspots and recent progress of potential proteinaceous feedstock resources for adhesive preparation.     

Geometrical inverse matrix approximation for least-squares problems and acceleration strategies

Numerical Algorithms - We extend the geometrical inverse approximation approach to the linear least-squares scenario. For that, we focus on the minimization of $1-\cos \limits (X(A^{T}A),I)$ ,...     

Unconstrained Optimization Techniques for the Acceleration of Alternating Projection Methods

Alternating projection methods have been extensively used to find the closest point, to a given point, in the intersection of several given sets that belong to a Hilbert space. One of the characteristics of these schemes is the slow convergence that can be observed in practical applications. To overcome this difficulty, several techniques, based on different ideas, have been developed to accelerate their convergence. Recently, a successful acceleration scheme was developed specially for Cimmino's method when applied to the solution of large-scale saddle point problems. This specialized acceleration scheme is based on the use of the well-known conjugate gradient method for minimizing a related convex quadratic map. In this work, we extend and further analyze this optimization approach for several alternating projection methods on different scenarios. In particular, we include a specialized analysis and treatment for the acceleration of von Neumann-Halperin's method and Cimmino's method on subspaces, and Kaczmarz method on linear varieties. For some specific applications we illustrate the advantages of our acceleration schemes with encouraging numerical experiments.     

Inexact spectral projected gradient methods on convex sets

A new method is introduced for large-scale convex constrainedoptimization. The general model algorithm involves, at eachiteration, the approximate minimization of a convex quadraticon the feasible set of the original problem and global convergenceis obtained by means of nonmonotone line searches. A specificalgorithm, the Inexact Spectral Projected Gradient method (ISPG),is implemented using inexact projections computed by Dykstra'salternating projection method and generates interior iterates.The ISPG method is a generalization of the Spectral ProjectedGradient method (SPG), but can be used when projections aredifficult to compute. Numerical results for constrained least-squaresrectangular matrix problems are presented.     

Spectral Variants of Krylov Subspace Methods

Krylov iterative methods usually solve an optimization problem, per iteration, to obtain a vector whose components are the step lengths associated with the previous search directions. This vector can be viewed as the solution of a multiparameter optimization problem. In that sense, Krylov methods can be combined with the spectral choice of step length that has recently been developed to accelerate descent methods in optimization. In this work, we discuss different spectral variants of Krylov methods and present encouraging preliminary numerical experiments, with and without preconditioning.     

Conditional Gradient Method for Double-Convex Fractional Programming Matrix Problems

We consider the problem of optimizing the ratio of two convex functions over a closed and convex set in the space of matrices. This problem appears in several applications and can be classified as a double-convex fractional programming problem. In general, the objective function is nonconvex but, nevertheless, the problem has some special features. Taking advantage of these features, a conditional gradient method is proposed and analyzed, which is suitable for matrix problems. The proposed scheme is applied to two different specific problems, including the well-known trace ratio optimization problem which arises in many engineering and data processing applications. Preliminary numerical experiments are presented to illustrate the properties of the proposed scheme.     

Spectral residual method without gradient information for solving large-scale nonlinear systems of equations

A fully derivative-free spectral residual method for solving large-scale nonlinear systems of equations is presented. It uses in a systematic way the residual vector as a search direction, a spectral steplength that produces a nonmonotone process and a globalization strategy that allows for this nonmonotone behavior. The global convergence analysis of the combined scheme is presented. An extensive set of numerical experiments that indicate that the new combination is competitive and frequently better than well-known Newton-Krylov methods for large-scale problems is also presented.

     

Spectral Gradient Methods for Linearly Constrained Optimization

Linearly constrained optimization problems with simple bounds are considered in the present work. First, a preconditioned spectral gradient method is defined for the case in which no simple bounds are present. This algorithm can be viewed as a quasi-Newton method in which the approximate Hessians satisfy a weak secant equation. The spectral choice of steplength is embedded into the Hessian approximation and the whole process is combined with a nonmonotone line search strategy. The simple bounds are then taken into account by placing them in an exponential penalty term that modifies the objective function. The exponential penalty scheme defines the outer iterations of the process. Each outer iteration involves the application of the previously defined preconditioned spectral gradient method for linear equality constrained problems. Therefore, an equality constrained convex quadratic programming problem needs to be solved at every inner iteration. The associated extended KKT matrix remains constant unless the process is reinitiated. In ordinary inner iterations, only the right-hand side of the KKT system changes. Therefore, suitable sparse factorization techniques can be applied and exploited effectively. Encouraging numerical experiments are presented.This research was supported by FAPESP Grant 2001-04597-4 and Grant 903724-6, FINEP and FAEP-UNICAMP, and the Scientific Computing Center of UCV. The authors thank two anonymous referees whose comments helped us to improve the final version of this paper.     

Preconditioners for distance matrix algorithms

A recent gradient algorithm in nonlinear optimization uses a novel idea that avoids line searches. This so-called spectral gradient algorithm works well when the spectrum of the Hessian of the function to be minimized has a small range or is clustered. In this article, we find a general preconditioning method for this algorithm. The preconditioning method is applied to the stress function, which arises in many applications of distance geometry, from statistics to finding molecular conformations. The Hessian of stress is shown to have a nice block structure. This structure yields a preconditioner which decreases the amount of computation needed to minimize stress by the spectral gradient algorithm. © 1994 by John Wiley & Sons, Inc.