… , even efficiently computing the best fixed action in hindsight is not possible, and thus,
minimizing regret via an efficient algorithm does not seem likely (given an approximation algorithm …

… are not guided by a formal notion of approximation. We present a framework for evolutionary
multi-objective optimization that allows to work with a formal notion of approximation. This …

… Of course, the efficiency of the optimization depends rather directly on how faithful the …
following general framework for an optimization method that uses approximation models. Later we …

… In recent years, some different kinds of approximate proper efficiency have … optimization
problem. In this paper, we first summarize some known concepts of approximate proper efficiency…

… efficiency for convex multicriteria programs is developed in Chapter 3. After a discrete
approximation to the set of efficient … of these points to finish the optimization process. In case the …

For problems when decisions are taken prior to observing the realization of underlying random
events, probabilistic constraints are an important modeling tool if reliability is a concern. A …

This paper presents a systematic approach for minimization of a wide class of non-differentiable
functions. The technique is based on approximation of the nondifferentiable function by …

… efficiency optimization problem in downlink multi-input multioutput multi-cell systems, where
… , and we argue that pseudoconvex optimization plays a fundamental role in designing novel …

… This article focuses on the case where such an approximation is going to be used as a …
gradient approximation at each iteration. A later section of this article will discuss this efficiency …

… on the expected approximation … approximation guarantees. Motivated by this fact, we
develop a new non-adaptive IM method, EPIC, that provides an attractive expected approximation …