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Creators/Authors contains: "Lewis, Adrian S"

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  1. ; ; ; (, Foundations of computational mathematics)
    Free, publicly-accessible full text available December 31, 2026
  2. ; ; ; (, Advances in Applied Mathematics)
    Free, publicly-accessible full text available April 1, 2026
  3. ; ; (, Journal of convex analysis)
    Free, publicly-accessible full text available February 1, 2026
  4. ; ; ; (, arXiv)
    Free, publicly-accessible full text available December 20, 2025
  5. ; ; (, arXiv)
    Accepted Manuscript Full Text Available
  6. ; ; (, SIAM Journal on Optimization)
    Differentiable structure ensures that many of the basics of classical convex analysis extend naturally from Euclidean space to Riemannian manifolds. Without such structure, however, extensions are more challenging. Nonetheless, in Alexandrov spaces with curvature bounded above (but possibly positive), we develop several basic building blocks. We define subgradients via pro- jection and the normal cone, prove their existence, and relate them to the classical affine minorant property. Then, in what amounts to a simple calculus or duality result, we develop a necessary optimality condition for minimizing the sum of two convex functions. 
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  7. ; (, SIAM Journal on Optimization)
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  8. ; ; (, SIAM Journal on Optimization)
    Full Text Available 
  9. ; (, Mathematics of Operations Research)
    Diverse optimization algorithms correctly identify, in finite time, intrinsic constraints that must be active at optimality. Analogous behavior extends beyond optimization to systems involving partly smooth operators, and in particular to variational inequalities over partly smooth sets. As in classical nonlinear programming, such active‐set structure underlies the design of accelerated local algorithms of Newton type. We formalize this idea in broad generality as a simple linearization scheme for two intersecting manifolds. 
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  10. ; (, SIAM Journal on Optimization)
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