FDR achieves the optimal O(1/N^2) rate with improved constants for proximal minimization of convex plus strongly convex functions and proves a matching lower bound.
Princeton University Press (1970)
6 Pith papers cite this work. Polarity classification is still indexing.
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An analog of Cauchy's surface area formula is established for Funk geometry on a convex body K using Holmes-Thompson area and central projections, reducing to a weighted vertex sum for polytopes and yielding a generalized Crofton formula.
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
Non-affine approval functions create unavoidable miscalibration in proper scoring rules for strategic agents, but step-function thresholds enable first-best screening without it, uniquely for the Brier score.
Sublevel sets of invex functions are connected under mild assumptions, with the result extended to solution sets in invex-incave minimax problems and incave games.
Extends lossless convexification theory for linear time-varying systems with discrete inputs by proving normality preservation under epigraph reformulation and geometric conditions that guarantee the relaxed solution satisfies the original discrete constraints exactly.
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Optimal Acceleration for Proximal Minimization of the Sum of Convex and Strongly Convex Functions
FDR achieves the optimal O(1/N^2) rate with improved constants for proximal minimization of convex plus strongly convex functions and proves a matching lower bound.
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Cauchy's Surface Area Formula in the Funk Geometry
An analog of Cauchy's surface area formula is established for Funk geometry on a convex body K using Holmes-Thompson area and central projections, reducing to a weighted vertex sum for polytopes and yielding a generalized Crofton formula.
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Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
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The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting
Non-affine approval functions create unavoidable miscalibration in proper scoring rules for strategic agents, but step-function thresholds enable first-best screening without it, uniquely for the Brier score.
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On the Connectedness of Sublevel Sets in Invex Optimization
Sublevel sets of invex functions are connected under mild assumptions, with the result extended to solution sets in invex-incave minimax problems and incave games.
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Geometric Conditions for Lossless Convexification in Linear Optimal Control with Discrete-Valued Inputs: Real-Time Implementation for Spacecraft Rendezvous
Extends lossless convexification theory for linear time-varying systems with discrete inputs by proving normality preservation under epigraph reformulation and geometric conditions that guarantee the relaxed solution satisfies the original discrete constraints exactly.