New results for molecular formation under pairwise potential minimization

We establish new lower bounds on the distance between two points of a minimum energy configuration of N points in ℝ³ interacting according to a pairwise potential function. For the Lennard-Jones case, this bound is 0.67985 (and 0.7633 in the planar case). A similar argument yields an estimate for the minimal distance in Morse clusters, which improves previously known lower bounds. Moreover, we prove that the optimal configuration cannot be two-dimensional, and establish an upper bound for the distance to the nearest neighbour of every particle, which depends on the position of this particle. On the boundary of the optimal configuration polytope, this is unity while in the interior, this bound depends on the potential function. In the Lennard-Jones case, we get the value . Also, denoting by V _N the global minimum in an N point minimum energy configuration, we prove in Lennard-Jones clusters for all N≥2, while asymptotically holds (as opposed to in the planar case, confirming non-planarity for large N).     

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Book reviews

     

Dynamical aspects of evolutionary stability

Selection is often viewed as a process that maximizes the average fitness of a population. However, there are often constraints even on the phenotypic level which may prevent fitness optimization. Consequently, in evolutionary game theory, models of frequency dependent selection are investigated, which focus on equilibrium states that are characterized by stability (or uninvadability) rather than by optimality. The aim of this article is to relate these stability notions with asymptotic stability in the so-called replicator dynamics, by generalizing results, which are well-known for elementary situations, to a fairly general setting applicable, e.g. to complex populations. Moreover, a purely dynamical characterization of evolutionary stability and uninvadability is presented.     

A first-order interior-point method for linearly constrained smooth optimization

We propose a first-order interior-point method for linearly constrained smooth optimization that unifies and extends first-order affine-scaling method and replicator dynamics method for standard quadratic programming. Global convergence and, in the case of quadratic program, (sub)linear convergence rate and iterate convergence results are derived. Numerical experience on simplex constrained problems with 1000 variables is reported.     

Modular material system for the microfabrication of biocompatible hydrogels based on thiol–ene‐modified poly(vinyl alcohol)

Novel modifications of the synthetic polymer poly(vinyl alcohol) (PVA) were developed for application in the field of biomedical engineering. PVA was modified with allyl succinic anhydride, norbornene anhydride as well as with γ‐thiobutyrolactone to produce macromers with reactive ene and thiol groups, respectively. Cytotoxicity studies have shown that the material exhibits almost no cell‐toxicity, when used in concentrations of 1 and 0.1 wt % for 24 h. The obtained macromers were photocrosslinked via thiol–ene chemistry. Storage stability of the macromer mixtures with different concentrations of pyrogallol as stabilizer were investigated. Photorheometry was employed to optimize mixtures concerning reactivity based on their thiol‐to‐ene ratio, photoinitiator concentration, and macromer content. The crosslinked hydrogels were studied concerning their swellability. To form hydrogels with cellular structure two‐photon‐polymerization (2PP) was employed. Processing windows for 2PP of selected mixtures were determined. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2016 , 54, 2060–2070     

On Standard Quadratic Optimization Problems

A standard quadratic optimization problem (QP) consists of finding (global) maximizers of a quadratic form over the standard simplex. Standard QPs arise quite naturally in copositivity-based procedures which enable an escape from local solutions. Furthermore, several important applications yield optimization problems which can be cast into a standard QP in a straightforward way. As an example, a new continuous reformulation of the maximum weight clique problem in undirected graphs is presented which considerably improves previous attacks both as numerical stability and interpretation of the results are concerned. Apparently also for the first time, an equivalence between standard QPs and QPs on the positive orthant is established. Also, a recently presented global optimization procedure (GENF - genetical engineering via negative fitness) is shortly reviewed.     

The combinatorics of pivoting for the maximum weight clique

In this paper we prove the equivalence between a pivoting-based heuristic (PBH) for the maximum weight clique problem and a combinatorial greedy heuristic. It is also proved that PBH always returns a local solution although this is not always guaranteed for Lemke's method, on which PBH is based.     

Perron–Frobenius property of copositive matrices, and a block copositivity criterion

Haynsworth and Hoffman proved in 1969 that the spectral radius of a symmetric copositive matrix is an eigenvalue of this matrix. This note investigates conditions which guarantee that an eigenvector corresponding to this dominant eigenvalue has no negative coordinates, i.e., whether the Perron–Frobenius property holds. Also a block copositivity criterion using the Schur complement is specified which may be helpful to reduce dimension in copositivity checks and which generalizes results proposed by Andersson et al. in 1995, and Johnson and Reams in 2005. Apparently, the latter five researchers were unaware of the more general results by the author precedingly published in 1987 and 1996, respectively.     

Solving Standard Quadratic Optimization Problems via Linear, Semidefinite and Copositive Programming

The problem of minimizing a (non-convex) quadratic function over the simplex (the standard quadratic optimization problem) has an exact convex reformulation as a copositive programming problem. In this paper we show how to approximate the optimal solution by approximating the cone of copositive matrices via systems of linear inequalities, and, more refined, linear matrix inequalities (LMI's). In particular, we show that our approach leads to a polynomial-time approximation scheme for the standard quadratic optimzation problem. This is an improvement on the previous complexity result by Nesterov who showed that a 2/3-approximation is always possible. Numerical examples from various applications are provided to illustrate our approach.