In the recent book Optimization Stories edited by Martin Groetschel, Robert Mifflin and Claudia Sastizabal [4] describe how nonsmooth optimization arose through a rare collaboration between Soviet and Western scientists in the 1960s and 1970s. In the seemingly simple case of a convex objective function there exists everywhere a nonempty set of subgradients whose shortest element provides a dire...

We compare and evaluate howwell-known and novel networkwide objective functions for Traffic Engineering (TE) algorithms fulfil TE requirements. To compare the objective functions we model the TE problem as a linear program and solve it to optimality, thus finding for each objective function the best possible target of any heuristic TE algorithm. We show that all the objective functions are not ...

Impairments in executive functions (EF) in Parkinson's disease (PD) will have a negative influence on daily life. For the assessment objective and subjective measurement approaches are used. It is however unknown whether these approaches contribute in a different way to the assessment of EF in PD. Thirty-nine PD patients and 24 healthy participants completed the Dysexecutive questionnaire (DEX;...

i This paper describes a solution technique mr Linear Programming problems with multiple objective functions. In this type of problem it is often necessary to replace the concept of "optimgm" with that of "best compromise". In contrast with methods dealing with a priori weighted sums of the objective functions, the method described here involves a sequential exploration of solutions. This explo...

Solving constrained optimization problems by multi-objective evolutionary algorithms has scored tremendous achievements in the last decade. Standard multi-objective schemes usually aim at minimizing the objective function and also the degree of constraint violation simultaneously. This paper proposes a new multi-objective method for solving constrained optimization problems. The new method keep...

Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the...

This paper compares a family of methods for characterizing neural feature selectivity with natural stimuli in the framework of the linear-nonlinear model. In this model, the neural firing rate is a nonlinear function of a small number of relevant stimulus components. The relevant stimulus dimensions can be found by maximizing one of the family of objective functions, Rényi divergences of differ...

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemmin...

We consider the problem of finding routes in road networks that optimize a combination of travel time and additional time-invariant costs. These could be an approximation of energy consumption, distance, tolls, or other penalties. The resulting problem is NP-hard, but if the additional cost is proportional to driving distance we can solve it optimally on the German road network within 2.3 s usi...