Metric (mathematics)

About: Metric (mathematics) is a research topic. Over the lifetime, 42617 publications have been published within this topic receiving 836571 citations. The topic is also known as: distance function & metric.

Hybrid Variable Fidelity Optimization by Using a Kriging-Based Scaling Function

Abstract: Solving design problems that rely on very complex and computationally expensive calculations using standard optimization methods might not be feasible given design cycle time constraints. Variable fidelity methods address this issue by using lower-fidelity models and a scaling function to approximate the higher-fidelity models in a provably convergent framework. In the past, scaling functions have mainly been either first-order multiplicative or additive corrections. These are being extended to second order. In this investigation variable metric approaches for calculating second-order scaling information are developed. A kriging-based scaling function is introduced to better approximate the high-fidelity response on a more global level. An adaptive hybrid method is also developed in this investigation. The adaptive hybrid method combines the additive and multiplicative approaches so that the designer does not have to determine which is more suitable prior to optimization. The methodologies developed in this research are compared to existing methods using two demonstration problems. The first problem is analytic, whereas the second involves the design of a supercritical high-lift airfoil. The results demonstrate that the krigingbased scaling methods improve computational expense by lowering the number of high-fidelity function calls required for convergence. The results also indicate the hybrid method is both robust and effective.

Gromov hyperbolicity and the Kobayashi metric on strictly pseudoconvex domains

Abstract: We give an estimate for the distance function related to the Kobayashi metric on a bounded strictly pseudoconvex domain with C 2-smooth boundary. Our formula relates the distance function on the domain with the Carnot-Caratheodory metric on the boundary. The estimate is precise up to a bounded additive term. As a corollary we conclude that the domain equipped with this distance function is hyperbolic in the sense of Gromov.

Simulation vector generation from HDL descriptions for observability-enhanced statement coverage

Abstract: Validation of RTL circuits remains the primary bottleneck in improving design turnaround time, and simulation remains the primary methodology for validation. Simulation-based validation has suffered from a disconnect between the metrics used to measure the error coverage of a set of simulation vectors, and the vector generation process. This disconnect has resulted in the simulation of virtually endless streams of vectors which achieve enhanced error coverage only infrequently. Another drawback has been that most error coverage metrics proposed have either been too simplistic or too inefficient to compute. Recently, an effective observability-based statement coverage metric was proposed along with a fast companion procedure for evaluating it. The contribution of our work is the development of a vector generation procedure targeting the observability-based statement coverage metric. Our method uses repeated coverage computation to minimize the number of vectors generated. For vector generation, we propose a novel technique to set up constraints based on the chosen coverage metric. Once the system of interacting arithmetic and Boolean constraints has been set up, it can be solved using hybrid linear programming and Boolean satisfiability methods. We present heuristics to control the size of the constraint system that needs to be solved. We present experimental results which show the viability of automatically generating vectors using our approach for industrial RTL circuits. We envision our system being used during the design process, as well as during post-design debugging.

A new metric on the manifold of kernel matrices with application to matrix geometric means

Abstract: Symmetric positive definite (spd) matrices pervade numerous scientific disciplines, including machine learning and optimization. We consider the key task of measuring distances between two spd matrices; a task that is often nontrivial whenever the distance function must respect the non-Euclidean geometry of spd matrices. Typical non-Euclidean distance measures such as the Riemannian metric δR(X, Y) = ||log(Y-1/2XY-1/2)||F, are computationally demanding and also complicated to use. To allay some of these difficulties, we introduce a new metric on spd matrices, which not only respects non-Euclidean geometry but also offers faster computation than δR while being less complicated to use. We support our claims theoretically by listing a set of theorems that relate our metric to δR(X, Y), and experimentally by studying the nonconvex problem of computing matrix geometric means based on squared distances.