Speeding up IP-based algorithms for constrained quadratic 0–1 optimization

In many practical applications, the task is to optimize a non-linear objective function over the vertices of a well-studied polytope as, e.g., the matching polytope or the travelling salesman polytope (TSP). Prominent examples are the quadratic assignment problem and the quadratic knapsack problem; further applications occur in various areas such as production planning or automatic graph drawing. In order to apply branch-and-cut methods for the exact solution of such problems, the objective function has to be linearized. However, the standard linearization usually leads to very weak relaxations. On the other hand, problem-specific polyhedral studies are often time-consuming. Our goal is the design of general separation routines that can replace detailed polyhedral studies of the resulting polytope and that can be used as a black box. As unconstrained binary quadratic optimization is equivalent to the maximum-cut problem, knowledge about cut polytopes can be used in our setting. Other separation routines are inspired by the local cuts that have been developed by Applegate, Bixby, Chvátal and Cook for faster solution of large-scale traveling salesman instances. Finally, we apply quadratic reformulations of the linear constraints as proposed by Helmberg, Rendl and Weismantel for the quadratic knapsack problem. By extensive experiments, we show that a suitable combination of these methods leads to a drastic speedup in the solution of constrained quadratic 0–1 problems. We also discuss possible generalizations of these methods to arbitrary non-linear objective functions.     

Dissecting entropic coiling and poor solvent effects in protein collapse

The early events in protein collapse and folding are guided by the protein's elasticity. The contributions of entropic coiling and poor solvent effects like hydrophobic forces to the elastic response of proteins are currently unknown. Using molecular simulations of stretched ubiquitin in comparison with models of proteins as entropic chains, we find a surprisingly high stiffness of the protein backbone, reflected by a persistence length of 1.2 nm, which is significantly reduced by hydrophobic forces acting between protein side chains to an apparent persistence length of 0.3-0.6 nm frequently observed in single-molecule stretching experiments. Thus, the poor solvent conditions of a protein in water lead to a protein compaction much beyond the coiling of an entropic chain and thereby allow a protein to appear softer than when using good solvents.