A Subspace, Interior, and Conjugate Gradient Method for Large-scale Bound-constrained Minimization Problems

A subspace adaption of the Coleman-Li trust region and interior method is proposed for solving large-scale bound-constrained minimization problems. This method can be implemented with either sparse Cholesky factorization or conjugate gradient computation. Under reasonable conditions the convergence properties of this subspace trust region method are as strong as those of its full-space version. Computational performance on various large-scale test problems are reported; advantages of our approach are demonstrated. Our experience indicates our proposed method represents an efficient way to solve large-scalebound-constrained minimization problems

Beyond Resilience and Burnout: The Need for Organizational Change to Promote Humanistic Practice and Teaching in Healthcare

Rapid changes in healthcare organization and practice environments, increasingly driven by business models and commercial interests, are associated with widespread burnout and dissatisfaction among healthcare professionals and pose barriers to humanistic relationship-centered quality care. Studies show burnout and significant stress currently affect over half of US physicians and nurses. Clinicians’ ability to provide compassionate care is significantly challenged. Most solutions to date have included individual interventions designed to enhance well-being and promote resilience. We examined organizational factors that inhibit or promote humanistic practice by faculty physicians in today’s healthcare environment. In this qualitative study, physician faculty who completed a one-year faculty development program in humanism at eight US academic medical centers provided written answers to two open-ended questions: a) What institutional or specific organizational unit-related factors promote humanism for you and others? b) What institutional or specific organizational unit-related factors inhibit or pose barriers, to humanism for you and others? 74% (68/92) of the physicians participated. The constant comparative method was used to analyze responses. We found that organizational culture was the central theme. Motivators of humanism included leadership supportive of humanistic practice, responsibility to role model humanism, organized activities promoting humanism, and practice structures that facilitate humanism. Factors that inhibited humanism included “top down” organizational culture, non-supportive leadership, time and bureaucratic pressures, and non-facilitative practice structures. Our findings suggest that organizational culture is, at a minimum, equally important as individual interventions. We describe features of organizational culture that reinforce humanistic practice and care in healthcare institutions and offer recommendations for organizational change that support the primacy of humanistic, compassionate, high quality patient care. 

Convalescent plasma in patients admitted to hospital with COVID-19 (RECOVERY): a randomised controlled, open-label, platform trial

SummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication

INEXACT REFLECTIVE NEWTON METHODS FOR LARGE-SCALE OPTIMIZATION SUBJECT TOBOUND CONSTRAINTS

This thesis addresses the problem of minimizing a large-scale nonlinear function subject to simple bound constraints. The most popular methods to handle bound constrained problems, active-set methods, introduce a combinatorial aspect to the problem. For these methods, the number of steps to converge may be related to the number of constraints. For large problems, this behavior is particularly detrimental. Reflective Newton methods avoid this problem by staying strictly within the constrained region. As a result, these methods have strong theoretical properties. Moreover, they behave experimentally like an unconstrained method: the number of steps to a solution is not strongly correlated with problem size. In this thesis, we discuss the reflective Newton approach and how it can be combined with inexact Newton techniques, within a subspace trust-region method, to efficiently solve large problems. Two algorithms are presented. The first uses a line search as its globalizing strategy. The second uses a strictly trust-region approach to globally converge to a local minimizer. Global convergence and rate of convergence results are established for both methods. We present computational evidence that using inexact Newton steps preserves the properties of the reflective Newton methods: the iteration counts are as low as when "exact" Newton steps are used. Also, both the inexact and exact methods are robust when the starting point is varied. Furthermore, the inexact reflective Newton methods have fast convergence when negative curvature is encountered, a trait not always shared by similar active-set type methods. The role of negative curvature is further explored by comparing the subspace trust-region approach to other common approximations to the full-space trust-region problem. On problems where only positive curvature is found, these trust-region methods differ little in the number of iterations to converge. However, for problems with negative curvature, the subspace method is more effective in capturing the negative curvature information, resulting in faster convergence. Finally a parallel implementation on the IBM SP2 is described and evaluated; the scalability and efficiency of this implementation are shown to be as good as the matrix-vector multiply routine it depends on

Getting CUTE with Matlab

CUTE is a testing environment for nonlinear programming algorithms developed by Bongartz, Conn, Gould and Toint ([CUTE: Constrained and unconstrained testing environment, Research Report RC 18860, IBM T.J. Watson Research Center, Yorktown Heights, USA, 1993]). The test problems in this environment are encoded in standard input format (SIF) and accessed by a set of FORTRAN subroutines. An extension to this environment was developed to allow fast access to the CUTE test problems from Matlab. This report describes this new Matlab interface to CUTE and how to use it

A subspace, interior, and conjugate gradient method for large-scale bound-constrained minimization problems

Abstract. A subspace adaptation of the Coleman–Li trust region and interior method is proposed for solving large-scale bound-constrained minimization problems. This method can be implemented with either sparse Cholesky factorization or conjugate gradient computation. Under reasonable conditions the convergence properties of this subspace trust region method are as strong as those of its full-space version. Computational performance on various large test problems is reported; advantages of our approach are demonstrated. Our experience indicates that our proposed method represents an efficient way to solve large bound-constrained minimization problems

Training and Career Development in Cardio-Oncology Translational and Implementation Science.

Cardiovascular disease is a leading cause of death in cancer survivors, after recurrence of the primary tumor or occurrence of a secondary malignancy. Consequently, the interdisciplinary field of cardio-oncology has grown rapidly in recent years to address the cardiovascular care needs of this unique population through clinical care and research initiatives. Here, the authors discuss the ideal infrastructure for training and career development in cardio-oncology translational and implementation science and emphasize the importance of the multidisciplinary cardiovascular team for both research and patient care. Cardio-oncology training opportunities in general cardiology, hematology/oncology, and specialized cardio-oncology clinical and research fellowships are also considered