Abstract Uncertainty often plays an important role in dynamic flow problems. In this paper, we consider both, a stationary and model with uncertain boundary data on networks. We introduce two different ways how to compute the probability for random be feasible, discussing their advantages disadvantages. context, feasible means, that corresponding meets some box constraints at network junctions....

This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two attributes, asset allocation (AA) the selection effect (SE), constraints on weights. The test consists of stocks from Dow Jones Industrial Average index. Values attributes are established relative to benc...

The paper deals with the stability of reinforced embankments on soft clay foundations, and presents alternative practical and versatile procedures for easy implementation of the traditionally tedious and iterative limit equilibrium methods of slices, including Bishop’s simplified method and Spencer’s method of slices. The proposed implementation procedures combine the use of constrained optimiz...

Group decision making with preference information on alternatives has become a very active research field over the last decade. Especially, the investigation on the group decision making problems based on different preference formats has attracted great interests from researchers recently and some approaches have been developed for dealing with these problems. However, the existing approaches c...

In this paper we introduce a new method for bounding the solution to constraint optimization problems called semi-independent partitioning. We show that our method is a strict generalization of the mini buckets algorithm [1]. We demonstrate empirically that another specialization of SIP, called greedy SIP, generally produces a better answer than mini buckets in much less time.

Abstract Aiming at the constrained optimization problem where function evaluation is time-consuming, this paper proposed a novel algorithm called data-driven Harris Hawks (DHHCO). In DHHCO, Kriging models are utilized to prospect potentially optimal areas by leveraging computationally expensive historical data during optimization. Three powerful strategies are, respectively, embedded into diffe...

We describe the implementation of a hierarchical constrained Bayesian Optimization algorithm and it’s application to joint optimization of features, acoustic model structure and decoding parameters for deep neural network (DNN)-based large vocabulary continuous speech recognition (LVCSR) systems. Within our hierarchical optimization method we perform constrained Bayesian optimization jointly of...

Pareto optimization solves a constrained optimization task by reformulating the task as a bi-objective problem. Pareto optimization has been shown quite effective in applications; however, it has little theoretical support. This work theoretically compares Pareto optimization with a penalty approach, which is a common method transforming a constrained optimization into an unconstrained optimiza...