We consider a monopolist who sells a set of products over a time horizon of T periods. The seller initially does not know the parameters of the products’ linear demand curve, but can estimate them based on demand observations. We first assume that the seller knows nothing about the parameters of the demand curve, and then consider the case where the seller knows the expected demand under an inc...

The joint design of spatial channel assignment and power allocation in Multiple Input Multiple Output (MIMO) systems capable of Simultaneous Wireless Information and Power Transfer (SWIPT) is studied. Assuming availability of channel state information at both communications ends, we maximize the harvested energy at the multi-antenna receiver, while satisfying a minimum information rate requirem...

During the initial phase of space trajectory planning and optimization, it is common to have to solve large dimensional global optimization problems. In particular continuous low-thrust propulsion is computationally very intensive to obtain optimal solutions. In this work, we investigate the application of machine learning regressors to estimate the final spacecraft mass mf after an optimal low...

One of the most important requirements that RF and microwave power amplifiers designed for radiocommunication systems must meet is an optimum power added efficiency (PAE) or an optimal combination of PAE and linearity. A harmonic active load–pull system which allows the control of the first three harmonic frequencies of the signal coming out of the transistor under test is a very useful tool to...

This paper is concerned with the estimation and evaluation of wavelet coefficients of the composition F ◦u of two functions F and u from the wavelet coefficients of u. Our main objective is to show that certain sequence spaces that can be used to measure the sparsity of the arrays of wavelet coefficients are stable under a class of nonlinear mappings F that occur naturally, e.g., in nonlinear P...

A new approach to computation of optimal policies for MDP (Markov decision process) models is introduced. The main idea is to solve not one, but an entire family of MDPs, parameterized by a weighting factor ζ that appears in the one-step reward function. For an MDP with d states, the family of value functions {hζ : ζ ∈ R} is the solution to an ODE, d dζh ∗ ζ = V(hζ) where the vector field V : R...

We introduce a Markov decision process in continuous time for the optimal control of a simple symmetrical immigration-emigration process by the introduction of total catastrophes. It is proved that a particular control-limit policy is average cost optimal within the class of all stationary policies by verifying that the relative values of this policy are the solution of the corresponding optima...

We propose an optimal control approach to tackle large scale unconstrained optimization problems. Our discussion begins with the introduction of a new framework to describe and deene the structure of a general function with a large number of variables. Based on the new concepts, a suucient condition is established so that any large scale unconstrained optimization problem satisfying this suucie...

Abs t rac t The optimal design parameters of classifiers for omni-font machine-printed numeral recognition based on the minimum classification error (MCE) criterion are determined experimentall y. The design parameters that influence the accuracy of an optical character reader (OCR) are: similarity measure (or distance measure), kinds of features, dimension of the feature vector, method of trai...

Here we present a multi-objective optimization approach for the design of microchip based separation systems solved using distributed agents. We develop Pareto-optimal design curves and compare these to experimentally derived designs found in the literature.