An Assessment of the Prediction Quality of VPIN

VPIN is a tool designed to predict extreme events like flash crashes. Some concerns have been raised about its reliability. In this chapter we assess VPIN prediction quality (precision and recall rates) of extreme volatility events including its sensitivity to the starting point of computation in a given data set. We benchmark the results with the ones of a “naive classifier.” The test data used in this study contains 5.6 year’s worth of trading data of the five most liquid futures contracts of this time period. We found that VPIN has poor “flash crash” prediction power with the traditional 0.99 decision threshold. Increasing the decision threshold does not significantly improve overall prediction quality. Nevertheless we found VPIN has a more interesting predictive power for flash events of lower amplitude. Finally, we found that, for practice, the last bar price structure is the least sensitive to the starting point of computation

La technologie dans les campagnes électorales

Data are crucial to elections 2.0. They make electoral victories possible and benefit from a large media coverage. This is especially true of electoral targeting, a new technique whereby potential voters are geographically micro-targeted. It showcases the key characteristics of the technological turn taken by electoral campaigns: the use of big data and algorithms. However, electoral targeting remains a poorly known and scarcely documented. The technical dimensions of such political technology tend to be neglected which makes it difficult to reach an in-depth understanding of this phenomenon. In this paper, we identify and discuss four central issues in order to better understand the stakes of technological innovation in electoral campaigns, and, more broadly, of political technology.Les données sont centrales pour les élections 2.0 : elles permettraient les victoires électorales et bénéficient d’une très forte médiatisation. Cela est particulièrement valable pour la technique de micro-ciblage géographique des électeurs potentiels : le micro-ciblage électoral. Cette technique correspond à la caractéristique essentielle du tournant technologique des campagnes électorales mêlant professionnalisation des acteurs, croyance dans les big data et les algorithmes. Pourtant, le micro-ciblage électoral reste un objet encore largement méconnu et mal connu. À partir d’une revue de littérature critique, nous montrons que les dimensions techniques de la technologie politique sont fortement minorées dans les recherches de science politique relatives aux campagnes électorales qui se révèlent très hétérogènes et comportent des manques importants. Ceux-ci empêchent d’approfondir la connaissance du micro-ciblage, en particulier sur ses conséquences électorales. Nous identifions quatre enjeux pour appréhender les enjeux des innovations technologiques dans les campagnes électorales et, plus largement, la technologie politique

Differentiable Collision Detection: a Randomized Smoothing Approach

Collision detection appears as a canonical operation in a large range of robotics applications from robot control to simulation, including motion planning and estimation. While the seminal works on the topic date back to the 80s, it is only recently that the question of properly differentiating collision detection has emerged as a central issue, thanks notably to the ongoing and various efforts made by the scientific community around the topic of differentiable physics. Yet, very few solutions have been suggested so far, and only with a strong assumption on the nature of the shapes involved. In this work, we introduce a generic and efficient approach to compute the derivatives of collision detection for any pair of convex shapes, by notably leveraging randomized smoothing techniques which have shown to be particularly adapted to capture the derivatives of non-smooth problems. This approach is implemented in the HPP-FCL and Pinocchio ecosystems, and evaluated on classic datasets and problems of the robotics literature, demonstrating few micro-second timings to compute informative derivatives directly exploitable by many real robotic applications including differentiable simulation.Comment: 7 pages, 6 figures, 2 table

PROX-QP: Yet another Quadratic Programming Solver for Robotics and beyond

International audienceQuadratic programming (QP) has become a core modelling component in the modern engineering toolkit. This is particularly true for simulation, planning and control in robotics. Yet, modern numerical solvers have not reached the level of efficiency and reliability required in practical applications where speed, robustness, and accuracy are all necessary. In this work, we introduce a few variations of the well-established augmented Lagrangian method, specifically for solving QPs, which include heuristics for improving practical numerical performances. Those variants are embedded within an open-source software which includes an efficient C++ implementation, a modular API, as well as best-performing heuristics for our test-bed. Relying on this framework, we present a benchmark studying the practical performances of modern optimization solvers for convex QPs on generic and complex problems of the literature as well as on common robotic scenarios. This benchmark notably highlights that this approach outperforms modern solvers in terms of efficiency, accuracy and robustness for small to medium-sized problems, while remaining competitive for higher dimensions

ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond

Workshop paper at the 6th Legged Robots Workshop, at the IEEE International Conference on Robotics and Automation (ICRA) 2022.International audienceMathematical optimization is the workhorse behind several aspects of modern robotics and control. In these applications, the focus is on constrained optimization, and the ability to work on manifolds (such as the classical matrix Lie groups), along with a specific requirement for robustness and speed. In recent years, augmented Lagrangian methods have seen a resurgence due to their robustness and flexibility, their connections to (inexact) proximal-point methods, and their interoperability with Newton or semismooth Newton methods. In the sequel, we present primal-dual augmented Lagrangian method for inequality-constrained problems on manifolds, which we introduced in our recent work, as well as an efficient C++ implementation suitable for use in robotics applications and beyond

QPLayer: efficient differentiation of convex quadratic optimization

Optimization layers within neural network architectures have become increasingly popular for their ability to solve a wide range of machine learning tasks and to model domain-specific knowledge. However, designing optimization layers requires careful consideration as the underlying optimization problems might be infeasible during training. Motivated by applications in learning, control, and robotics, this work focuses on convex quadratic programming (QP) layers. The specific structure of this type of optimization layer can be efficiently exploited for faster computations while still allowing rich modeling capabilities. We leverage primal-dual augmented Lagrangian techniques for computing derivatives of both feasible and infeasible QPs. Not requiring feasibility allows, as a byproduct, for more flexibility in the QP to be learned. The effectiveness of our approach is demonstrated in a few standard learning experiments, obtaining three to ten times faster computations than alternative state-of-the-art methods while being more accurate and numerically robust. Along with these contributions, we provide an open-source C++ software package called QPLayer for efficiently differentiating convex QPs and which can be interfaced with modern learning frameworks

ProxNLP: a primal-dual augmented Lagrangian solver for nonlinear programming in Robotics and beyond

Workshop paper at the 6th Legged Robots Workshop, at the IEEE International Conference on Robotics and Automation (ICRA) 2022.International audienceMathematical optimization is the workhorse behind several aspects of modern robotics and control. In these applications, the focus is on constrained optimization, and the ability to work on manifolds (such as the classical matrix Lie groups), along with a specific requirement for robustness and speed. In recent years, augmented Lagrangian methods have seen a resurgence due to their robustness and flexibility, their connections to (inexact) proximal-point methods, and their interoperability with Newton or semismooth Newton methods. In the sequel, we present primal-dual augmented Lagrangian method for inequality-constrained problems on manifolds, which we introduced in our recent work, as well as an efficient C++ implementation suitable for use in robotics applications and beyond

Constrained Differential Dynamic Programming: A primal-dual augmented Lagrangian approach

International audienceTrajectory optimization is an efficient approach for solving optimal control problems for complex robotic systems. It relies on two key components: first the transcription into a sparse nonlinear program, and second the corresponding solver to iteratively compute its solution. On one hand, differential dynamic programming (DDP) provides an efficient approach to transcribe the optimal control problem into a finite-dimensional problem while optimally exploiting the sparsity induced by time. On the other hand, augmented Lagrangian methods make it possible to formulate efficient algorithms with advanced constraint-satisfaction strategies. In this paper, we propose to combine these two approaches into an efficient optimal control algorithm accepting both equality and inequality constraints. Based on the augmented Lagrangian literature, we first derive a generic primal-dual augmented Lagrangian strategy for nonlinear problems with equality and inequality constraints. We then apply it to the dynamic programming principle to solve the value-greedy optimization problems inherent to the backward pass of DDP, which we combine with a dedicated globalization strategy, resulting in a Newton-like algorithm for solving constrained trajectory optimization problems. Contrary to previous attempts of formulating an augmented Lagrangian version of DDP, our approach exhibits adequate convergence properties without any switch in strategies. We empirically demonstrate its interest with several case-studies from the robotics literature

Constrained Differential Dynamic Programming: A primal-dual augmented Lagrangian approach

International audienceTrajectory optimization is an efficient approach for solving optimal control problems for complex robotic systems. It relies on two key components: first the transcription into a sparse nonlinear program, and second the corresponding solver to iteratively compute its solution. On one hand, differential dynamic programming (DDP) provides an efficient approach to transcribe the optimal control problem into a finite-dimensional problem while optimally exploiting the sparsity induced by time. On the other hand, augmented Lagrangian methods make it possible to formulate efficient algorithms with advanced constraint-satisfaction strategies. In this paper, we propose to combine these two approaches into an efficient optimal control algorithm accepting both equality and inequality constraints. Based on the augmented Lagrangian literature, we first derive a generic primal-dual augmented Lagrangian strategy for nonlinear problems with equality and inequality constraints. We then apply it to the dynamic programming principle to solve the value-greedy optimization problems inherent to the backward pass of DDP, which we combine with a dedicated globalization strategy, resulting in a Newton-like algorithm for solving constrained trajectory optimization problems. Contrary to previous attempts of formulating an augmented Lagrangian version of DDP, our approach exhibits adequate convergence properties without any switch in strategies. We empirically demonstrate its interest with several case-studies from the robotics literature