A Model of Growth with Intertemporal Knowledge Externalities, Augmented with Contemporaneous Knowledge Externalities

The present model is essentially Romer’s (1990) model of endogenous growth with intertemporal knowledge externalities, augmented with contemporaneous knowledge externalities to give a richer explanation of the growth process. Both types of knowledge spillovers seem essential to capturing the features of knowledge in a model of growth. Introducing synchronic complementarities and knowledge externalities across inventive firms immediately creates the possibility of multiple equilibria and threshold effects in the present model. Another advantage of this theoretical formulation is that it allows for an analysis of the effects on steady-state growth of a variety of technology policies relying on changing knowledge complementarities parameters.Endogenous growth, innovation, knowledge complementarities, knowledge externalities, general equilibrium

Aghion And Howitt’s Basic Schumpeterian Model Of Growth Through Creative Destruction: A Geometric Interpretation

The present paper takes a geometric approach to characterize the competitive forces behind innovation and dynamic general equilibria determination in the model of growth through creative destruction constructed by Aghion and Howitt (1992). All can be comprehended intuitively from the geometric presentation. While Aghion and Howitt‘s original presentation of the basic model was essentially analytical, often with fairly intricate mathematics focusing on stationary equilibria with positive growth, the geometric presentation taken here has the benefit of making what in the original paper was a bundle of mathematical notation more comprehensible intuitively.Endogenous growth, innovation, creative destruction, general equilibrium

Restoration of Poissonian Images Using Alternating Direction Optimization

Much research has been devoted to the problem of restoring Poissonian images, namely for medical and astronomical applications. However, the restoration of these images using state-of-the-art regularizers (such as those based on multiscale representations or total variation) is still an active research area, since the associated optimization problems are quite challenging. In this paper, we propose an approach to deconvolving Poissonian images, which is based on an alternating direction optimization method. The standard regularization (or maximum a posteriori) restoration criterion, which combines the Poisson log-likelihood with a (non-smooth) convex regularizer (log-prior), leads to hard optimization problems: the log-likelihood is non-quadratic and non-separable, the regularizer is non-smooth, and there is a non-negativity constraint. Using standard convex analysis tools, we present sufficient conditions for existence and uniqueness of solutions of these optimization problems, for several types of regularizers: total-variation, frame-based analysis, and frame-based synthesis. We attack these problems with an instance of the alternating direction method of multipliers (ADMM), which belongs to the family of augmented Lagrangian algorithms. We study sufficient conditions for convergence and show that these are satisfied, either under total-variation or frame-based (analysis and synthesis) regularization. The resulting algorithms are shown to outperform alternative state-of-the-art methods, both in terms of speed and restoration accuracy.Comment: 12 pages, 12 figures, 2 tables. Submitted to the IEEE Transactions on Image Processin

Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization

Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: (1) the noise is multiplied by (rather than added to) the original image; (2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: (1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; (2) using variable splitting to obtain an equivalent constrained problem; and (3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance.Comment: 11 pages, 7 figures, 2 tables. To appear in the IEEE Transactions on Image Processing