Overriding Virtue

If you focus your charitable giving on global causes where it will do the most good, how should you feel about passing by the local soup kitchen? Would the ideally virtuous agent have their (local) empathy still activated, but simply overridden by the recognition that distant others are in even greater need, leaving the agent feeling torn? Or would their empathetic impulses be wholeheartedly redirected towards the greatest needs? This chapter suggests a way to revise an outdated conception of moral virtue to better meet the demands of a cosmopolitan moral outlook

Deontic Pluralism and the Right Amount of Good

Consequentialist views have traditionally taken a maximizing form, requiring agents to bring about the very best outcome that they can. But this maximizing function may be questioned. Satisficing views instead allow agents to bring about any outcome that exceeds a satisfactory threshold or qualifies as “good enough.” Scalar consequentialism, by contrast, eschews moral requirements altogether, instead evaluating acts in purely comparative terms, i.e., as better or worse than their alternatives. After surveying the main considerations for and against each of these three views, I argue that the core insights of each are not (despite appearances) in conflict. Consequentialists should be deontic pluralists and accept a maximizing account of the ought of most reason, a satisficing account of obligation, and a scalar account of the weight of reasons

GMRES-Accelerated ADMM for Quadratic Objectives

We consider the sequence acceleration problem for the alternating direction method-of-multipliers (ADMM) applied to a class of equality-constrained problems with strongly convex quadratic objectives, which frequently arise as the Newton subproblem of interior-point methods. Within this context, the ADMM update equations are linear, the iterates are confined within a Krylov subspace, and the General Minimum RESidual (GMRES) algorithm is optimal in its ability to accelerate convergence. The basic ADMM method solves a $\kappa$-conditioned problem in $O(\sqrt{\kappa})$ iterations. We give theoretical justification and numerical evidence that the GMRES-accelerated variant consistently solves the same problem in $O(\kappa^{1/4})$ iterations for an order-of-magnitude reduction in iterations, despite a worst-case bound of $O(\sqrt{\kappa})$ iterations. The method is shown to be competitive against standard preconditioned Krylov subspace methods for saddle-point problems. The method is embedded within SeDuMi, a popular open-source solver for conic optimization written in MATLAB, and used to solve many large-scale semidefinite programs with error that decreases like $O(1/k^{2})$, instead of $O(1/k)$, where $k$ is the iteration index.Comment: 31 pages, 7 figures. Accepted for publication in SIAM Journal on Optimization (SIOPT

Subadditivity of Matrix phi-Entropy and Concentration of Random Matrices

Matrix concentration inequalities provide a direct way to bound the typical spectral norm of a random matrix. The methods for establishing these results often parallel classical arguments, such as the Laplace transform method. This work develops a matrix extension of the entropy method, and it applies these ideas to obtain some matrix concentration inequalities.Comment: 23 page

Heterogeneous biomedical database integration using a hybrid strategy: a p53 cancer research database.

Complex problems in life science research give rise to multidisciplinary collaboration, and hence, to the need for heterogeneous database integration. The tumor suppressor p53 is mutated in close to 50% of human cancers, and a small drug-like molecule with the ability to restore native function to cancerous p53 mutants is a long-held medical goal of cancer treatment. The Cancer Research DataBase (CRDB) was designed in support of a project to find such small molecules. As a cancer informatics project, the CRDB involved small molecule data, computational docking results, functional assays, and protein structure data. As an example of the hybrid strategy for data integration, it combined the mediation and data warehousing approaches. This paper uses the CRDB to illustrate the hybrid strategy as a viable approach to heterogeneous data integration in biomedicine, and provides a design method for those considering similar systems. More efficient data sharing implies increased productivity, and, hopefully, improved chances of success in cancer research. (Code and database schemas are freely downloadable, http://www.igb.uci.edu/research/research.html.)