We consider resource allocation with separable objective functions defined over subranges of the integers. While it is well known that (the maximization version of) this problem can be solved efficiently if the objective functions are concave, the general problem of resource allocation with non-concave functions is difficult. In this article we show that for fairly well-shaped non-concave objec...

A key drawback of the current generation of artificial decision-makers is that they do not adapt well to changes in unexpected situations. This paper addresses the situation in which an AI for aerial dog fighting, with tunable parameters that govern its behavior, will optimize behavior with respect to an objective function that must be evaluated and learned through simulations. Once this object...

Today, telecommunication networks host a wide range of heterogeneous services. Some demand strict delay minima, while others only need a best-effort kind of service. To achieve service differentiation, network traffic is partitioned in several classes which is then transmitted according to a flexible and fair scheduling mechanism. Telecommunication networks can, for instance, use an implementat...

In this paper we investigate the exact optimization of BDDs with respect to path-related objective functions. We aim at a deeper understanding of the computational effort of exact methods targeting the new objective functions. This is achieved by an approach based on Dynamic Programming which generalizes the framework of Friedman and Supowit. A prime reason for the computational complexity can ...

In this paper we describe a solution framework for a number of scheduling problems in which the goal is to find a feasible schedule that minimizes some objective function of the minimax type on a set of parallel, identical machines, subject to release dates, deadlines, and/or generalized precedence constraints. We determine a lower bound on the objective function in the following way. We first ...

Inverse treatment planning for intensity-modulated radiotherapy may include time consuming, multiple minimizations of an objective function. In this paper, methods are presented to speed up the process of (repeated) minimization of the well-known quadratic dose objective function, extended with a smoothing term that ensures generation of clinically acceptable beam profiles. In between two subse...

We present a method to implement probabilistic treatment planning of intensity-modulated radiation therapy using custom software plugins in a commercial treatment planning system. Our method avoids the definition of safety-margins by directly including the effect of geometrical uncertainties during optimization when objective functions are evaluated. Because the shape of the resulting dose dist...

Chung, S.-J., H.W. Hamacher, F. Maffioli and K.G. Murty, Note on combinatorial optimization with max-linear objective functions, Discrete Applied Mathematics 42 (1993) 139-145. We consider combinatorial optimization problems with a feasible solution set SC(O,I )” specified by a system of linear constraints in O-l variables. Additionally, several cost functions c,,., ,,c~ are given. The max-line...

This note investigates the boundary between polynomially-solvable Max Cut and NP Hard Max Cut instances when they are classified only on the basis of the sign pattern of the objective function coefficients, i.e., of the orthant containing the objective function vector. It turns out that the matching number of the subgraph induced by the positive edges is the key parameter that allows us to diff...

Assume that A and B are non-void subsets of a metric space, and that S : A −→ B and T : A −→ B are given non-self mappings. In light of the fact that S and T are non-self mappings, it may happen that the equations Sx = x and Tx = x have no common solution, named a common fixed point of the mappings S and T . Subsequently, in the event that there is no common solution of the preceding equations,...