Effort Maximization in Asymmetric N-Person Contest Games

This paper provides existence and characterization of the optimal contest success function under the condition that the objective of the contest designer is total effort maximization among n heterogeneous players. Heterogeneity of players makes active participation of a player in equilibrium endogenous with respect to the specific contest success function adopted by the contest designer. Hence, the aim of effort maximization implies the identification of those players who should be excluded from making positive efforts. We give a general proof for the existence of an optimal contest success function and provide an algorithm for the determination of the set of actively participating players. This is turn allows to determine optimal efforts in closed form. An important general feature of the solution is that maximization of total effort requires at least three players to be active.effort maximization, existence of solution, asymmetric contests, participation constraints

Effort Maximization in Asymmetric N-person Contest Games

This paper provides existence and characterization of the optimal contest success function under the condition that the objective of the contest designer is total effort maximization among n heterogeneous players. Heterogeneity of players makes active participation of a player in equilibrium endogenous with respect to the specific contest success function adopted by the contest designer. Hence, the aim of effort maximization implies the identification of those players who should be excluded from making positive efforts.We give a general proof for the existence of an optimal contest success function and provide an algorithm for the determination of the set of actively participating players.This is turn allows to determine optimal efforts in closed form.An important general feature of the solution is that maximization of total effort requires at least three players to be active.Effort maximization, existence of solution, asymmetric contests, participation constraints

Regularization of Limited Memory Quasi-Newton Methods for Large-Scale Nonconvex Minimization

This paper deals with regularized Newton methods, a flexible class of unconstrained optimization algorithms that is competitive with line search and trust region methods and potentially combines attractive elements of both. The particular focus is on combining regularization with limited memory quasi-Newton methods by exploiting the special structure of limited memory algorithms. Global convergence of regularization methods is shown under mild assumptions and the details of regularized limited memory quasi-Newton updates are discussed including their compact representations. Numerical results using all large-scale test problems from the CUTEst collection indicate that our regularized version of L-BFGS is competitive with state-of-the-art line search and trust-region L-BFGS algorithms and previous attempts at combining L-BFGS with regularization, while potentially outperforming some of them, especially when nonmonotonicity is involved.Comment: 23 pages, 4 figure

Convergence Properties of Monotone and Nonmonotone Proximal Gradient Methods Revisited

Composite optimization problems, where the sum of a smooth and a merely lower semicontinuous function has to be minimized, are often tackled numerically by means of proximal gradient methods as soon as the lower semicontinuous part of the objective function is of simple enough structure. The available convergence theory associated with these methods (mostly) requires the derivative of the smooth part of the objective function to be (globally) Lipschitz continuous, and this might be a restrictive assumption in some practically relevant scenarios. In this paper, we readdress this classical topic and provide convergence results for the classical (monotone) proximal gradient method and one of its nonmonotone extensions which are applicable in the absence of (strong) Lipschitz assumptions. This is possible since, for the price of forgoing convergence rates, we omit the use of descent-type lemmas in our analysis.Comment: 23 page

Необходимость изучения экологической медицины в высших медицинских учебных заведениях

Optimization problems with cardinality constraints are very difficult mathematical programs which are typically solved by global techniques from discreteoptimization. Here we introduce a mixed-integer formulation whose standard relaxation still has the same solutions (in the sense of global minima) as the underlying cardinality-constrained problem; the relation between thelocal minima is also discussed in detail. Since our reformulation is a mini-mization problem in continuous variables, it allows to apply ideas from thatfield to cardinality-constrained problems. Here, in particular, we therefore also derive suitable stationarity conditions and suggest an appropriate regularization method for the solution of optimization problems with cardinalityconstraints. This regularization method is shown to be globally convergentto a Mordukhovich-stationary point. Extensive numerical results are given to illustrate the behavior of this method