that the problem of probability model selection can be formulated as an optimization problem with linear (in)equality constraints and a non-linear objective function. An algorithm for model discovery/selection based on a primal – dual formulation similar to that of the interior point method is presented

Abstract—An optimization criterion is presented for discriminant analysis. The criterion extends the optimization criteria of the classical Linear Discriminant Analysis (LDA) through the use of the pseudoinverse when the scatter matrices are singular. It is applicable regardless of the relative

Uncertainty of measuring complex-valued physical quantities can be described by complex sets. These sets can have complicated shapes, so we would like to find a good approximating family of sets. Which approximating family is the best? We reduce the corresponding optimization problem to a geometric

An alternative proof is given to the theorem of L-optimality criterion on the basis of a property of an L\-convex function labelled (L\-APR[Z]). Theorem 1 (L-optimality criterion [1, Th.5.12], [2, Th.7.14]) For an L\-convex function g ∈ L\[Z → R] and p ∈ dom g, we have g(p) ≤ g(q) ( ∀ q ∈ ZV

if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an e cient algorithm for feature selection which computes an approximation to the optimal feature selection criterion

conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result

Presented in this paper

In this paper we explore the Vs-optimality criterion that was proposed in Ginsburg and Ben-Gal (IIE Trans. 2006; 38:445– 461) as a new design-of-experiment (DOE) alphabetic optimality criterion. The Vs-optimality criterion seeks to minimize the variance of the optimal solution of an empirically

SUMMARY. Uniform randomization of a design that is optimal under an ideal model, with respect to a general optimality criterion, is shown to be minimax under a larger model. This generalizes a result due to Hooper (1989). 1.

This expository paper sets out the principal results in H∞ control theory in the context of continuous-time linear systems. The focus is on the mathematical theory rather than computational methods.