Bøger af Jon Lee

In a world where the relentless hustle of urban life deafens the soul's whispers, this collection of tales offers a revitalizing escape. Step into a realm where nature's majesty reigns supreme, and humanity's true essence is laid bare against its vast canvas. Each narrative unveils the adventures of individuals who dared to step beyond civilization's boundaries, immersing themselves in the raw, untamed beauty of the wild.From the treacherous ascents of rugged mountains to the serene embrace of untouched forests, readers are taken on a transformative journey. They'll traverse icy landscapes, navigate roaring rapids, and huddle by roaring campfires beneath starlit skies. Along the way, they'll encounter characters who, while separated by circumstance, are united by a shared respect for nature and a burning desire to conquer their own internal frontiers.Yet, it's not just the external adventures that captivate but the internal odysseys that unfold. Moments of introspection, bonds forged in adversity, and profound realizations blossom in the wilderness's embrace. As the tales progress, the line between man and nature blurs, revealing a timeless dance of mutual respect and intertwined destinies.This isn't just a book; it's a compass guiding readers back to their primal roots, urging them to seek their own adventures and rediscover the wild spirit within.

This monograph presents a comprehensive treatment of the maximum-entropy sampling problem (MESP), which is a fascinating topic at the intersection of mathematical optimization and data science. The text situates MESP in information theory, as the algorithmic problem of calculating a sub-vector of pre-specificed size from a multivariate Gaussian random vector, so as to maximize Shannon's differential entropy. The text collects and expands on state-of-the-art algorithms for MESP, and addresses its application in the field of environmental monitoring. While MESP is a central optimization problem in the theory of statistical designs (particularly in the area of spatial monitoring), this book largely focuses on the unique challenges of its algorithmic side. From the perspective of mathematical-optimization methodology, MESP is rather unique (a 0/1 nonlinear program having a nonseparable objective function), and the algorithmic techniques employed are highly non-standard. In particular, successful techniques come from several disparate areas within the field of mathematical optimization; for example: convex optimization and duality, semidefinite programming, Lagrangian relaxation, dynamic programming, approximation algorithms, 0/1 optimization (e.g., branch-and-bound), extended formulation, and many aspects of matrix theory. The book is mainly aimed at graduate students and researchers in mathematical optimization and data analytics.