ArticlePloS one2018
Optimization by Adaptive Stochastic Descent.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- Cost-benefit analysis of a potential hepatitis C vaccine: a modelling study.BMC infectious diseases · 2026Article
- Estimating the optimal linear combination of predictors using spherically constrained optimization.BMC bioinformatics · 2022Article
- Cost optimisation analysis of the expanded programme for immunisation: balancing equity and coverage in Pakistan.BMJ global health · 2022Article
- Optima TB: A tool to help optimally allocate tuberculosis spending.PLoS computational biology · 2021Article
- Covasim: An agent-based model of COVID-19 dynamics and interventions.PLoS computational biology · 2021Article
- Opportunities for improved HIV prevention and treatment through budget optimization in Eswatini.PloS one · 2020Article
- Optimal allocation of HIV resources among geographical regions.BMC public health · 2019Article
- The Cascade Analysis Tool: software to analyze and optimize care cascades.Gates open research · 2019Article
- Optima Nutrition: an allocative efficiency tool to reduce childhood stunting by better targeting of nutrition-related interventions.BMC public health · 2018Article
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- In the interests of time: improving HIV allocative efficiency modelling via optimal time-varying allocations.Journal of the International AIDS Society · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
When standard optimization methods fail to find a satisfactory solution for a parameter fitting problem, a tempting recourse is to adjust parameters manually. While tedious, this approach can be surprisingly powerful in terms of achieving optimal or near-optimal solutions. This paper outlines an optimization algorithm, Adaptive Stochastic Descent (ASD), that has been designed to replicate the essential aspects of manual parameter fitting in an automated way. Specifically, ASD uses simple principles to form probabilistic assumptions about (a) which parameters have the greatest effect on the objective function, and (b) optimal step sizes for each parameter. We show that for a certain class of optimization problems (namely, those with a moderate to large number of scalar parameter dimensions, especially if some dimensions are more important than others), ASD is capable of minimizing the objective function with far fewer function evaluations than classic optimization methods, such as the Nelder-Mead nonlinear simplex, Levenberg-Marquardt gradient descent, simulated annealing, and genetic algorithms. As a case study, we show that ASD outperforms standard algorithms when used to determine how resources should be allocated in order to minimize new HIV infections in Swaziland.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.