A Stochastic Subgradient Method for Distributionally Robust Non-convex and Non-smooth Learning

Journal of Optimization Theory and Applications - We consider a distributionally robust formulation of stochastic optimization problems arising in statistical learning, where robustness is with...     

Dichlorocarbene and analogs: discovery,properties and reactions

Russian Chemical Bulletin - The discovery and studies of chemistry of dichlorocarbene and other electron deficient carbenes are discussed.     

Space-Time Directional Lyapunov Exponents for Cellular Automata

Space-time directional Lyapunov exponents are introduced. They describe the maximal velocity of propagation to the right or to the left of fronts of perturbations in a frame moving with a given velocity. The continuity of these exponents as function of the velocity and an inequality relating them to the directional entropy is proved.     

Primal cutting plane algorithms revisited

Dual fractional cutting plane algorithms, in which cutting planes are used to iteratively tighten a linear relaxation of an integer program, are well-known and form the basis of the highly successful branch-and-cut method. It is rather less well-known that various primal cutting plane algorithms were developed in the 1960s, for example by Young. In a primal algorithm, the main role of the cutting planes is to enable a feasible solution to the original problem to be improved. Research on these algorithms has been almost non-existent.  In this paper we argue for a re-examination of these primal methods. We describe a new primal algorithm for pure 0-1 problems based on strong valid inequalities and give some encouraging computational results. Possible extensions to the case of general mixed-integer programs are also discussed.     

Konvergente und asymptotische Darstellungen für die Lösungen linearer Differentialgleichungen,deren Koeffizienten Dirichletsche Reihen oder Exponentialpolynome mit komplexen Exponenten sind

Ohne ZusammenfassungDie vorliegende Arbeit wurde von der Mathematisch-naturwissenschaftlichen Fakultät der Universität Jena als Dissertation (D 27) angenommen. nachdem sie vorher einen Fakultätspreis erhalten hatte. Referent war Herr Prof. Hermann Schmidt. Ich möchte ihm auch hier meinen Dank für die Förderung aussprechen, die er mir durch seine vielfachen persönlichen Anregungen zuteil werden ließ.     

Regularization of Discrete Ill-Posed Problems

The paper concerns conditioning aspects of finite-dimensional problems arising when the Tikhonov regularization is applied to discrete ill-posed problems. A relation between the regularization parameter and the sensitivity of the regularized solution is investigated. The main conclusion is that the condition number can be decreased only to the square root of that for the nonregularized problem. The convergence of solutions of regularized discrete problems to the exact generalized solution is analyzed just in the case when the regularization corresponds to the minimal condition number. The convergence theorem is proved under the assumption of the suitable relation between the discretization level and the data error. As an example the method of truncated singular value decomposition with regularization is considered. This revised version was published online in July 2006 with corrections to the Cover Date.