. This paper contributes to an ongoing effort to construct a calculus for deriving programs for optimisation problems. The calculus is built around the notion of initial data types and catamorphisms which are homomorphisms on initial data types. It is shown how certain optimisation problems, which

We consider some simple optimisation problems and employ a non-traditional method to solve them. We try to model both the problem and solution domains as algebraic structures, attempting to characterise the join operations on these domains. In each of the examples chosen, these structures turn out

. Recent work on phase transition has detected apparently interesting phenomena in the distribution of hard optimisation problems (find, on some measure, the least m such that the given instance x has a solution of value m) and their corresponding decision problems (determine, for a given bound m

Abstract. We consider the multilevel paradigm and its potential to aid the solution of combinatorial optimisation problems. The multilevel paradigm is a simple one, which involves recursive coarsening to create a hierarchy of approximations to the original problem. An initial solution is found

of potential solutions. This paper extends the UCSP model to account for optimisation criteria, by defining the uncertain CSOP. The challenge is to combine optimisation (preferences over individual solutions) with a closure of a certain type (preference over sets of solutions) to a UCSOP. Unlike traditional

Advantages of combining gamma scanning techniques and 3D dose simulation

. More generally there may be simple lower and upper bounds # i # x i # u i i = 1, . . . , n (1.1) on the weights (e.g. the fraction of the portfolio in property must be between 5% and 20%), and general linear constraints # n+i # a T i x # u n+i i = 1, . . . , m. (e.g. the total fraction of the portfolio allocated to all international assets must not exceed 40%). Writing the constant vectors a i # R n as the rows of the matrix A # R mn defined by A T = [a 1 am ] the linear constraints are # # # x Ax # # u, (1.2) where # #<F11.

In this work, a conceptual software model of optimisation problems is developed. Problem-specific aspects are clearly identified as such. To achieve the desired separation between problems and solvers, the details of the problem are encapsulated, and mechanisms capable of supporting

Abstract: Hierarchical approach is applied for solving portfolio optimisation problem. It is solved without subject determination of the preferable coefficient. Here it is determined as a decision of optimisation problem, solved at the upper level of an hierarchical system. This methodology allows

The particular strength of CBR is normally considered to be its use in weak theory domains where solution quality is compiled into cases and is reusable. In this paper we explore an alternative use of CBR in optimisation problems where cases represent highly optimised structures in a huge highly