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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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0.3%0.5%0.8%0.1% · Aug 202219922001200920172026
5 results for “invexity”
Some properties of geodesic (α,E)(α,E)-preinvex functions on a Riemannian manifoldmath.DG

The paper defines new types of geodesic functions on Riemannian manifolds and explores their properties.

problem Exploring new types of functions on Riemannian manifolds.
method Introducing geodesic (α,E)(α,E)-invex set and developing geodesic (α,E)(α,E)-preinvex and invex functions.
result Established a relation between geodesic (α,E)(α,E)-preinvex and geodesic (α,E)(α,E)-invex functions.
geodesic functionsRiemannian manifoldsinvexitypreinvexitypreinvex set
2019-05-20abs ↗pdf ↗
Sparse Mixed Linear Regression with Guarantees: Taming an Intractable Problem with Invex Relaxationcs.LG

Paper solves NP-hard sparse mixed linear regression problem with provable guarantees.

problem Sparse mixed linear regression on unlabeled data.
method Invex relaxation for intractable problem with theoretical guarantees.
result Exact recovery of data labels and close approximation of regression parameters.
sparse regressioninvex relaxationNP-harddata labelingtheoretical guarantees
2022-06-02abs ↗pdf ↗
Strongly geodesic preinvexity and Strongly Invariant η-Monotonicity on Riemannian Manifolds and its Applicationmath.OC

The paper defines new geometric concepts on Riemannian manifolds and applies them to optimization problems.

problem Optimization problems on Riemannian manifolds.
method Strongly geodesic preinvexity, strongly η-invexity, and strongly invariant η-monotonicity definitions.
result Characterization of strict η-minimizers and solutions to variational like-inequality problems.
Riemannian manifoldsgeodesic preinvexityη-monotonicityoptimizationvariational inequalities
2023-02-06abs ↗pdf ↗
Convergence Rates for Stochastic Approximation: Biased Noise with Unbounded Variance, and Applicationsstat.ML

Paper analyzes convergence rates of SGD for non-convex functions under various assumptions.

problem Analyzing convergence rates of SGD for non-convex functions.
method Studied convergence properties of Stochastic Gradient Descent (SGD) for invex functions under weaker and stronger hypotheses.
result Derives estimates on the rate of convergence of $J(oldsymbolθ_t)$ to its limit for functions satisfying the Polyak-Lojasiewicz (PL) condition.
Stochastic Gradient DescentSGDConvergence RatesInvex FunctionsPolyak-Lojasiewicz Condition
2023-12-05abs ↗pdf ↗