Stephen Boyd

Dixon and Amelie Boyd were Belfast medical students. Later he was a Cambridge Professor and she an Academic's wife. They and their four sons, became a Cambridge mid-century fixture. Their copious letters (and memories of those sons, the authors) bring to light an unvarnished picture of that University at a time of dramatic change. Darlingest, It not only seems but it was a long time ago. We were half our present ages; the war was no more than a shadow of a man's hand in bright sunlight - possible but not very probable... In a wider field no atomic power or bombs; no television; unemployment and a servant class; no penicillin; no Russian imperialism and a well-established British Empire. We had not even dreamed of Cambridge, let alone three periods of residence and a Chair and Clare. Dixon Boyd to his wife on their 25th wedding anniversary 19 August 1958 On the Margins of War Jews, Blacks and Irish Sex and Psychoanalysis Who knew Who Scholarship, Science and Preferment Clare College as Family This volume tells us what they then thought and how they then acted.

A Simple Method for Predicting Covariance Matrices of Financial Returns makes three contributions. First, it proposes a new method for predicting the time-varying covariance matrix of a vector of financial returns, building on a specific covariance estimator suggested by Engle in 2002. The second contribution proposes a new method for evaluating a covariance predictor, by considering the regret of the log-likelihood over some time period such as a quarter. The third contribution is an extensive empirical study of covariance predictors. The authors compare their method to other popular predictors, including rolling window, exponentially weighted moving average (EWMA) and generalized autoregressive conditional heteroscedastic (GARCH) type methods. After an introduction, Section 2 describes some common predictors, including the one that this method builds on. Section 3 introduces the proposed covariance predictor. Section 4 discusses methods for validating covariance predictors that measure both overall performance and reactivity to market changes. Section 5 describes the data used in the authors’ first empirical studies and the results are provided in Section 6. The authors then discuss some extensions of and variations on the method, including realized covariance prediction (Section 7), handling large universes via factor models (Section 8), obtaining smooth covariance estimates (Section 9), and using the authors’ covariance model to generate simulated returns (Section 10).

Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a machine learning practitioner might use vector representations of words as features for a classification task. In this monograph the authors present a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general. It includes a wide variety of specific embedding methods, including spectral embedding, principal component analysis, multidimensional scaling, Euclidean distance problems, etc. The authors provide a detailed description of minimum-distortion embedding problem and describe the theory behind creating solutions to all aspects. They also give describe in detail algorithms for computing minimum-distortion embeddings. Finally, they provide examples on how to approximately solve many MDE problems involving real datasets, including images, co-authorship networks, United States county demographics, population genetics, and single-cell mRNA transcriptomes. An accompanying open-source software package, PyMDE, makes it easy for practitioners to experiment with different embeddings via different choices of distortion functions and constraint sets. The theory and techniques described and illustrated in this book will be of interest to researchers and practitioners working on modern-day systems that look to adopt cutting-edge artificial intelligence.

This book showcases the research of established and younger colleagues from Great Britain and Ireland on artifice and invention in the Spanish Golden Age. It falls into four sections, in each of which works on particular authors are examined in detail: prose, poetry, drama, and colonial writing.

This groundbreaking textbook combines straightforward explanations with a wealth of practical examples to offer an innovative approach to teaching linear algebra. Requiring no prior knowledge of the subject, it covers the aspects of linear algebra - vectors, matrices, and least squares - that are needed for engineering applications, discussing examples across data science, machine learning and artificial intelligence, signal and image processing, tomography, navigation, control, and finance. The numerous practical exercises throughout allow students to test their understanding and translate their knowledge into solving real-world problems, with lecture slides, additional computational exercises in Julia and MATLAB®, and data sets accompanying the book online. Suitable for both one-semester and one-quarter courses, as well as self-study, this self-contained text provides beginning students with the foundation they need to progress to more advanced study.

Multi-Period Trading via Convex Optimization considers a basic model of multi-period trading, which can be used to evaluate the performance of a trading strategy. It describes a framework for single-period optimization, where the trades in each period are found by solving a convex optimization problem that trades off expected return, risk, transaction cost and holding cost such as the borrowing cost for shorting assets. It then describes a multiperiod version of the trading method, where optimization is used to plan a sequence of trades, with only the first one executed, using estimates of future quantities that are unknown when the trades are chosen. The single period method traces back to Markowitz; the multiperiod methods trace back to model predictive control. This monograph addresses the single-period and multi-period methods in one simple framework, giving a clear description of the development and the approximations made. The methods described can be thought of as good ways to exploit predictions, no matter how they are made. We have also developed a companion open-source software library that implements many of the ideas and methods described in the paper. Multi-Period Trading via Convex Optimization collects in one place the basic definitions, a careful description of the model, and discussion of how convex optimization can be used in multi-period trading, all in a common notation and framework. It provides the reader with a unified, self-contained treatment, focusing on the practical issues that arise in multi-period trading. It will benefit anyone interested in the study of these methods and is also an ideal reference for a quantitative trader, or someone who works with or for, or employs, one.

Timorous: adj, 1) shy, not bold 2) easily frightened. Economics: n, social science concerned with the production and consumption of goods and services. What is the best way to run a country? How long should a person be obliged to work every day? What will the economy look like after Brexit? In this new take on the Scottish economy, experts Trebeck, Boyd and Kerevan address how our economy can serve us, as opposed to the people serving the economy. They believe that current economic policies are not aligned with what we as people need in these times of rampant inequality and inequitable distribution, advocating an increased focus on the quality of Scotland’s economy. Using Scotland as an example for the economic workings of any country, Tackling Timorous Economics shows a better way of how economics could work for us.

Principal components analysis (PCA) is a well-known technique for approximating a tabular data set by a low rank matrix. Here, the authors extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well-known techniques in data analysis, such as non-negative matrix factorization, matrix completion, sparse and robust PCA, k-means, k-SVD, and maximum margin matrix factorization. The method handles heterogeneous data sets, and leads to coherent schemes for compressing, denoising, and imputing missing entries across all data types simultaneously. It also admits a number of interesting interpretations of the low rank factors, which allow clustering of examples or of features. The authors propose several parallel algorithms for fitting generalized low rank models, and describe implementations and numerical results.

This book showcases the research of established and younger colleagues from Great Britain and Ireland on artifice and invention in the Spanish Golden Age. It falls into four sections, in each of which works on particular authors are examined in detail: prose, poetry, drama, and colonial writing.

Proximal Algorithms discusses proximal operators and proximal algorithms, and illustrates their applicability to standard and distributed convex optimization in general and many applications of recent interest in particular. Much like Newton's method is a standard tool for solving unconstrained smooth optimization problems of modest size, proximal algorithms can be viewed as an analogous tool for nonsmooth, constrained, large-scale, or distributed versions of these problems. They are very generally applicable, but are especially well-suited to problems of substantial recent interest involving large or high-dimensional datasets. Proximal methods sit at a higher level of abstraction than classical algorithms like Newton's method: the base operation is evaluating the proximal operator of a function, which itself involves solving a small convex optimization problem. These subproblems, which generalize the problem of projecting a point onto a convex set, often admit closed-form solutions or can be solved very quickly with standard or simple specialized methods. Proximal Algorithms discusses different interpretations of proximal operators and algorithms, looks at their connections to many other topics in optimization and applied mathematics, surveys some popular algorithms, and provides a large number of examples of proximal operators that commonly arise in practice.

Examines dynamic trading of a portfolio of assets in discrete periods over a finite time horizon, with arbitrary time-varying distribution of asset returns. The goal is to maximize the total expected revenue from the portfolio, while respecting constraints on the portfolio like a required terminal portfolio and leverage and risk limits. The revenue takes into account the gross cash generated in trades, transaction costs, and costs associated with the positions, such as fees for holding short positions. The model that is presented takes the form of a stochastic control problem with linear dynamics and convex cost function and constraints. While this problem can be tractably solved in several special cases - for example, when all costs are convex quadratic, or when there are no transaction costs - the focus is on the more general case, with nonquadratic cost terms and transaction costs. Performance Bounds and Suboptimal Policies for Multi-Period Investment shows how to use linear matrix inequality techniques and semidefinite programming to produce a quadratic bound on the value function, which in turn gives a bound on the optimal performance. This performance bound can be used to judge the performance obtained by any suboptimal policy. As a by-product of the performance bound computation, an approximate dynamic programming policy is obtained that requires the solution of a convex optimization problem, often a quadratic program, to determine the trades to carry out in each step.

Presents a fully decentralized method for dynamic network energy management based on message passing between devices. It considers a network of devices, such as generators, fixed loads, deferrable loads, and storage devices, each with its own dynamic constraints and objective, connected by AC and DC lines. The problem is to minimize the total network objective subject to the device and line constraints, over a given time horizon. This is a large optimization problem, with variables for consumption or generation for each device, power flow for each line, and voltage phase angles at AC buses, in each time period. This text develops a decentralized method for solving this problem called proximal message passing. The method is iterative: at each step, each device exchanges simple messages with its neighbors in the network and then solves its own optimization problem, minimizing its own objective function, augmented by a term determined by the messages it has received. It is shown that this message passing method converges to a solution when the device objective and constraints are convex. The method is completely decentralized, and needs no global coordination other than synchronizing iterations; the problems to be solved by each device can typically be solved extremely efficiently and in parallel. The method is fast enough that even a serial implementation can solve substantial problems in reasonable time frames. Results for several numerical experiments are reported, demonstrating the method's speed and scaling, including the solution of a problem instance with over ten million variables in under fifty minutes for a serial implementation; with decentralized computing, the solve time would be less than one second.

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. This book argues that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas-Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for ?1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, it discusses applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. It also discusses general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.

A comprehensive review of the main issues and concepts related to heritage tourism. Illustrated throughout the text via examples and boxed case studies, this book is a resource for educators, students and practitioners in the field of heritage tourism.