{ "_id": "6a23cdf6530b9bc726be22cd", "Package": "GALAHAD", "Type": "Package", "Title": "Geometry-Adaptive Lyapunov-Assured Hybrid Optimizer with\nSoftplus Reparameterization and Trust-Region Control", "Version": "2.0.0", "Authors@R": "person(\"Richard A.\", \"Feiss\",\nemail = \"feiss026@umn.edu\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0008-0409-6042\"))", "Author": "Richard A. Feiss [aut, cre] (ORCID:\n)", "Maintainer": "Richard A. Feiss ", "Description": "Implements the GALAHAD algorithm (Geometry-Adaptive\nLyapunov-Assured Hybrid Optimizer), updated in version 2 to\nreplace the hard-clamp positivity constraint of v1 with a\nnumerically smooth softplus reparameterization, add rho-based\ntrust-region adaptation (actual vs. predicted objective\nreduction), extend convergence detection to include both\nabsolute and relative function-stall criteria, and enrich the\nper-iteration history with Armijo backtrack counts and\ntrust-region quality ratios. Parameters constrained to be\npositive (rates, concentrations, scale parameters) are handled\nin a transformed z-space via the softplus map so that gradients\nremain well-defined at the constraint boundary. A two-partition\nAPI (positive / euclidean) replaces the three-way T/P/E\npartition of v1; the legacy form is still accepted for\nbackwards compatibility. Designed for biological modeling\nproblems (germination, dose-response, prion RT-QuIC, survival)\nwhere rates, concentrations, and unconstrained coefficients\ncoexist. Developed at the Minnesota Center for Prion Research\nand Outreach (MNPRO), University of Minnesota. Based on Conn et\nal. (2000) , Barzilai and Borwein\n(1988) , Xu and An (2024)\n, Polyak (1969)\n, Nocedal and Wright (2006,\nISBN:978-0-387-30303-1), and Dugas et al. (2009)\n.", "URL": "https://github.com/RFeissIV/GALAHAD", "BugReports": "https://github.com/RFeissIV/GALAHAD/issues", "Note": "Development followed an iterative human-machine collaboration\nwhere all algorithmic design, statistical methodologies, and\nbiological validation logic were conceptualized, tested, and\niteratively refined by Richard A. Feiss through repeated cycles\nof running experimental data, evaluating analytical outputs,\nand selecting among candidate algorithms and approaches. AI\nsystems ('Anthropic Claude' and 'OpenAI GPT') served as coding\nassistants and analytical sounding boards under continuous\nhuman direction. The selection of statistical methods,\nevaluation of biological plausibility, and all final\nmethodology decisions were made by the human author. 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