Interpolation linéaire | EPFL Graph Search

right|300px|thumb|Les points rouges correspondent aux points (xk,yk), et la courbe bleue représente la fonction d'interpolation, composée de segments de droite. L’interpolation linéaire est la méthode la plus simple pour estimer la valeur prise par une fonction continue entre deux points déterminés (interpolation). Elle consiste à utiliser pour cela la fonction affine (de la forme f(x) = m.x + b) passant par les deux points déterminés. Cette technique était d'un emploi systématique lorsque l'on ne disposait que de tables numériques pour le calcul avec les fonctions transcendantes : les tables comportaient d'ailleurs à cet effet en marge les « différences tabulaires », auxiliaire de calcul servant à l'interpolation linéaire. Enfin l'interpolation linéaire est la base de la technique de quadrature numérique par la méthode des trapèzes. Principe Supposons que l'on connaisse les valeurs prises par une fonction f en deux points x_a et x_b

On Linear Interpolation in the Latent Space of Deep Generative Models

Quentin Christian Becker, Mike Yan Michelis

The underlying geometrical structure of the latent space in deep generative models is in most cases not Euclidean, which may lead to biases when comparing interpolation capabilities of two models. Smoothness and plausibility of linear interpolations in latent space are associated with the quality of the underlying generative model. In this paper, we show that not all such interpolations are comparable as they can deviate arbitrarily from the shortest interpolation curve given by the geodesic. This deviation is revealed by computing curve lengths with the pull-back metric of the generative model, finding shorter curves than the straight line between endpoints, and measuring a non-zero relative length improvement on this straight line. This leads to a strategy to compare linear interpolations across two generative models. We also show the effect and importance of choosing an appropriate output space for computing shorter curves. For this computation we derive an extension of the pull-back metric.

2021

Numerical methods for option pricing: polynomial approximation and high dimensionality

Francesco Statti

Options are some of the most traded financial instruments and computing their price is a central task in financial mathematics and in practice. Consequently, the development of numerical algorithms for pricing options is an active field of research. In general, evaluating the price of a specific option relies on the properties of the stochastic model used for the underlying asset price. In this thesis we develop efficient and accurate numerical methods for option pricing in a specific class of models: polynomial models. They are a versatile tool for financial modeling and have useful properties that can be exploited for option pricing. Significant challenges arise when developing option pricing techniques. For instance, the underlying model might have a high-dimensional parameter space. Furthermore, treating multi-asset options yields high-dimensional pricing problems. Therefore, the pricing method should be able to handle high dimensionality. Another important aspect is the efficiency of the algorithm: in real-world applications, option prices need to be delivered within short periods of time, making the algorithmic complexity a potential bottleneck. In this thesis, we address these challenges by developing option pricing techniques that are able to handle low and high-dimensional problems, and we propose complexity reduction techniques. The thesis consists of four parts: First, we present a methodology for European and American option pricing. The method uses the moments of the underlying price process to produce monotone sequences of lower and upper bounds of the option price. The bounds are obtained by solving a sequence of polynomial optimization problems. As the order of the moments increases, the bounds become sharper and eventually converge to the exact price under appropriate assumptions. Second, we develop a fast algorithm for the incremental computation of nested block triangular matrix exponentials. This algorithm allows for an efficient incremental computation of the moment sequence of polynomial jump-diffusions. In other words, moments of order 0, 1, 2, 3... are computed sequentially until a dynamically evaluated criterion tells us to stop. The algorithm is based on the scaling and squaring technique and reduces the complexity of the pricing algorithms that require such an incremental moment computation. Third, we develop a complexity reduction technique for high-dimensional option pricing. To this end, we first consider the option price as a function of model and payoff parameters. Then, the tensorized Chebyshev interpolation is used on the parameter space to increase the efficiency in computing option prices, while maintaining the required accuracy. The high dimensionality of the problem is treated by expressing the tensorized interpolation in the tensor train format and by deriving an efficient way, which is based on tensor completion, to approximate the interpolation coefficients. Lastly, we propose a methodology for pricing single and multi-asset European options. The approach is a combination of Monte Carlo simulation and function approximation. We address the memory limitations that arise when treating very high-dimensional applications by combining the method with optimal sampling strategies and using a randomized algorithm to reduce the storage complexity of the approach. The obtained numerical results show the effectiveness of the algorithms developed in this thesis.

EPFL2019

Algebraic algorithms for network information flow

Javad Ebrahimi Boroojeni

Ahlswede et al. in the seminal paper [1] have shown that in data transfer over networks, processing the data at the nodes can significantly improve the throughput. As proved by Li et al. in [2], even with a simple type of operation, namely linear operation, the throughput can still be vastly improved. In [3], it is shown that the multicasting problem over networks can be translated to an algebraic question about a polynomial associated to the network called network polynomial. In this thesis, we start from the algorithm of [3] and extend it in several directions. First, we generalize the framework of [3] to include the case where the messages can also be vectors over some fixed finite field. We also show that in contrast to the original algorithm, ours can be used to reduce the field size for the case of sending finite field elements. In both vector network code algorithm and finite field minimization, we translate the network code design problems into an algebraic problem about network polynomials. Because of the importance of the network polynomials, we investigate more properties of them and we study the relationship between these objects and the topological properties of the network. Then, we extend the result of [3] to the deterministic models for wireless relay networks, a very important class of networks that has been introduced in [4] by Avestimehr, Diggavi and Tse. Finally, for another class of networks, called broadcast networks, we introduce the transfer matrix and using its properties, we show that in the absence of public messages, processing the information at the nodes will not improve the throughput.

EPFL2013

Numerical methods for option pricing: polynomial approximation and high dimensionality

Francesco Statti

EPFL2019