Search results for "optimality"
showing 10 items of 60 documents
Data-Based Forest Management with Uncertainties and Multiple Objectives
2016
In this paper, we present an approach of employing multiobjective optimization to support decision making in forest management planning. The planning is based on data representing so-called stands, each consisting of homogeneous parts of the forest, and simulations of how the trees grow in the stands under different treatment options. Forest planning concerns future decisions to be made that include uncertainty. We employ as objective functions both the expected values of incomes and biodiversity as well as the value at risk for both of these objectives. In addition, we minimize the risk level for both the income value and the biodiversity value. There is a tradeoff between the expected val…
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
2017
Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, also referred to as surrogates or metamodels are commonly used in the literature to reduce the computation time. This paper presents a survey of 45 different recent algorithms proposed in the literature between 2008 and 2016 to handle computationally expensive multiobjective optimization problems. Several algorithms are discussed based on what kind of an approximation such as problem, function or fitness approximation they use. Most emphasis is given to function approxim…
Controlled polyhedral sweeping processes: existence, stability, and optimality conditions
2021
This paper is mainly devoted to the study of controlled sweeping processes with polyhedral moving sets in Hilbert spaces. Based on a detailed analysis of truncated Hausdorff distances between moving polyhedra, we derive new existence and uniqueness theorems for sweeping trajectories corresponding to various classes of control functions acting in moving sets. Then we establish quantitative stability results, which provide efficient estimates on the sweeping trajectory dependence on controls and initial values. Our final topic, accomplished in finite-dimensional state spaces, is deriving new necessary optimality and suboptimality conditions for sweeping control systems with endpoint constrain…
On incorporating the paradigms of discretization and Bayesian estimation to create a new family of pursuit learning automata
2013
Published version of an article in the journal: Applied Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/s10489-013-0424-x There are currently two fundamental paradigms that have been used to enhance the convergence speed of Learning Automata (LA). The first involves the concept of utilizing the estimates of the reward probabilities, while the second involves discretizing the probability space in which the LA operates. This paper demonstrates how both of these can be simultaneously utilized, and in particular, by using the family of Bayesian estimates that have been proven to have distinct advantages over their maximum likelihood counterparts. The success of LA-…
Una procedura sequenziale di stima di punto di cambiamento
2008
In this paper we propose a sequential procedure for the estimation of a change-point when a change has occurred in the distribution that governs the process which generates the observations. The procedure applies whether the distribution functions involved are completely specified or they contain unknown parameters to be estimated. The procedure is based on the Kolmogorov-Smirnov test of goodness of fit, or an appropriate different test such as the chi-square test, and satisfies the optimality condition defined by the maximization of the sum of the p-values involved.
Survey of methods to visualize alternatives in multiple criteria decision making problems
2012
When solving decision problems where multiple conflicting criteria are to be considered simultaneously, decision makers must compare several different alternatives and select the most preferred one. The task of comparing multidimensional vectors is very demanding for the decision maker without any support. Different graphical visualization tools can be used to support and help the decision maker in understanding similarities and differences between the alternatives and graphical illustration is a very important part of decision support systems that are used in solving multiple criteria decision making problems. The visualization task is by no means trivial because, on the one hand, the grap…
A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions
2014
Published version of an article from the journal: Applied Intelligence. Also available on Springerlink: http://dx.doi.org/10.1007/s10489-014-0541-1 The most difficult part in the design and analysis of Learning Automata (LA) consists of the formal proofs of their convergence accuracies. The mathematical techniques used for the different families (Fixed Structure, Variable Structure, Discretized etc.) are quite distinct. Among the families of LA, Estimator Algorithms (EAs) are certainly the fastest, and within this family, the set of Pursuit algorithms have been considered to be the pioneering schemes. Informally, if the environment is stationary, their ε-optimality is defined as their abili…
Optimization of Long-Run Average-Flow Cost in Networks With Time-Varying Unknown Demand
2010
We consider continuous-time robust network flows with capacity constraints and unknown but bounded time-varying demand. The problem of interest is to design a control strategy off-line with no knowledge of the demand realization. Such a control strategy regulates the flow on-line as a function of the realized demand. We address both the case of systems without and with buffers. The main novelty in this work is that we consider a convex cost which is a function of the long-run average-flow and average-demand. We distinguish a worst-case scenario where the demand is the worst-one from a deterministic scenario where the demand has a neutral behavior. The resulting strategies are called min-max…
A Sequential Quadratic Programming Method for Volatility Estimation in Option Pricing
2006
Our goal is to identify the volatility function in Dupire's equation from given option prices. Following an optimal control approach in a Lagrangian framework, we propose a globalized sequential quadratic programming (SQP) algorithm with a modified Hessian - to ensure that every SQP step is a descent direction - and implement a line search strategy. In each level of the SQP method a linear-quadratic optimal control problem with box constraints is solved by a primal-dual active set strategy. This guarantees L^1 constraints for the volatility, in particular assuring its positivity. The proposed algorithm is founded on a thorough first- and second-order optimality analysis. We prove the existe…
Optimal reciprocals in German Sign Language
2003
Unlike most spoken languages, German Sign Language (DGS) does not have a single means of reciprocal marking. Rather, different strategies are used, which crucially depend on phonological (one-handed sign vs. two-handed sign) and morphosyntactic (plain verb vs. agreement verb) properties of the underlying verb. Moreover, with plain verbs DGS shows dialectal variation. Altogether there are four different ways of realizing reciprocal marking in DGS. In this paper, we compare a rule-based analysis for the reciprocal data (based on Brentari’s 1998 feature hierarchy) to an optimality-theoretic analysis. We argue that an OT-account allows for a more straightforward explanation of the facts. In par…