arXiv research

Study solves optimal portfolio selection using HJB equation.

New algorithms solve monotone inclusions and convex-concave minimax problems.

In this paper, we study monotonicity of eigenvalues of Laplacian-type operator $-Δ+cR$, where $c$ is a constant, along the Ricci-Bourguignon flow. For $c\neq0$, We derive monotonicity of the lowest eigenvalue of Laplacian-type operator $-Δ+cR$ which generalizes some results of Cao \cite{Cao2007}. For $c=0$, We derive m…

New methods solve MI problems with locally Lipschitz operators, improving solution efficiency.

The paper solves a complex financial optimization problem using a novel mathematical technique.

In this paper, we study fundamental problems of maximizing DR-submodular continuous functions that have real-world applications in the domain of machine learning, economics, operations research and communication systems. It captures a subclass of non-convex optimization that provides both theoretical and practical guar…

DO-EM framework for quantum models improves generative tasks.

New method improves submodular maximization for machine learning applications.

Geometrically studies Moore-Penrose inverse and polar decomposition continuity.

New algorithm maximizes non-monotone adaptive submodular functions in linear time.

Differentially private algorithms for submodular maximization under various constraints.

This is a revised version of our short note [arxiv.math.DG/0403065] where we discuss the monotonicity of the eigen-values of the Laplacian operator to the Ricci-Hamilton flow on a compact or a complete non-compact Riemannian manifold. We show that the eigenvalue of the Lapacian operator on a compact domain associated w…

We prove trace identities for commutators of operators, which are used to derive sum rules and sharp universal bounds for the eigenvalues of periodic Schroedinger operators and Schroedinger operators on immersed manifolds. In particular, we prove bounds on the eigenvalue lambda_{N+1} in terms of the lower spectrum, bou…

Diminishing-returns (DR) submodular optimization is an important field with many real-world applications in machine learning, economics and communication systems. It captures a subclass of non-convex optimization that provides both practical and theoretical guarantees. In this paper, we study the fundamental problem of…

The paper studies continuous submodular functions and their optimization.

New model outperforms Neural ODEs while being more efficient.

The theory of monotone Riemannian metrics on the state space of a quantum system was established by Denes Petz in 1996. In a recent paper he argued that the scalar curvature of a statistically relevant - monotone - metric can be interpreted as an average statistical uncertainty. The present paper contributes to this su…

Proves monotonicity of parabolic frequency on all manifolds without curvature assumptions.

In this expository article, we discuss various monotonicity formulas for parabolic and elliptic operators and explain how the analysis of the function spaces and the geometry of the underlining spaces are intertwined. After briefly discussing some of the well-known analytical applications of monotonicity for parabolic …

Submodular functions have many applications. Matchings have many applications. The bitext word alignment problem can be modeled as the problem of maximizing a nonnegative, monotone, submodular function constrained to matchings in a complete bipartite graph where each vertex corresponds to a word in the two input senten…

New algorithm solves maximal monotone inclusion problems.

Recently, the decentralized optimization problem is attracting growing attention. Most existing methods are deterministic with high per-iteration cost and have a convergence rate quadratically depending on the problem condition number. Besides, the dense communication is necessary to ensure the convergence even if the …

New surgery operation preserves monotonicity of Lagrangians.

For free boundary problems on Euclidean spaces, the monotonicity formulas of Alt-Caffarelli-Friedman and Caffarelli-Jerison-Kenig are cornerstones for the regularity theory as well as the existence theory. In this article we establish the analogs of these results for the Laplace-Beltrami operator on Riemannian manifold…

The paper characterizes optimal dynamic portfolios for a modified mean-variance utility.

The paper tackles robust submodular maximization under matroid constraints, providing approximation algorithms for summary extraction.

New method tackles online DR-submodular maximization with improved regret guarantees.

The study calculates Weyl entropy in spacetime regions and shows its monotonic behavior.

In this paper we study the fundamental problems of maximizing a continuous non-monotone submodular function over the hypercube, both with and without coordinate-wise concavity. This family of optimization problems has several applications in machine learning, economics, and communication systems. Our main result is the…

The paper examines lattice homology invariants of Seifert homology spheres.

Monotonic differentiable sorting networks improve upon previous methods.

New accelerators for EM improve convergence speed in complex mixture models.

A new stochastic primal--dual algorithm for solving a composite optimization problem is proposed. It is assumed that all the functions/operators that enter the optimization problem are given as statistical expectations. These expectations are unknown but revealed across time through i.i.d. realizations. The proposed al…

Paper tackles stochastic $k$-submodular bandits with full feedback, achieving sublinear regret.

Study finds surfaces in spherical caps that maximize modified energy.

In this work we generalise various recent results on the evolution and monotonicity of the eigenvalues of certain geometric operators under specified geometric flows. Given a closed, compact Riemannian manifold $\big(M^n,g(t)\big)$ and a smooth function $η\in C^{\infty}(M)$ we consider the family of operators $\mathbb{…

We consider the learning algorithms under general source condition with the polynomial decay of the eigenvalues of the integral operator in vector-valued function setting. We discuss the upper convergence rates of Tikhonov regularizer under general source condition corresponding to increasing monotone index function. T…

In this paper, we derive the evolution equation for the first eigenvalue of the Witten-Laplace operator acting on the space of functions along the mean curvature flow on a closed oriented manifold. We show some interesting monotonic quantities under the mean curvature flow.

Additive Gaussian process framework handles monotonicity constraints in high dimensions.

New algorithms solve DR-submodular maximization with faster convergence.

New algorithm for competing influence spread in unknown networks.

Study proves boundedness of operators in variable exponent Morrey spaces.

Non-affine aggregation rules cannot preserve monotonicity in convex learning.

This paper considers stochastic optimization problems for a large class of objective functions, including convex and continuous submodular. Stochastic proximal gradient methods have been widely used to solve such problems; however, their applicability remains limited when the problem dimension is large and the projecti…

In this paper, we mainly investigate continuity, monotonicity and differentiability for the first eigenvalue of the $p$-Laplace operator along the Ricci flow on closed manifolds. We show that the first $p$-eigenvalue is strictly increasing and differentiable almost everywhere along the Ricci flow under some curvature a…

New algorithms reduce regret for online submodular maximization under various conditions.

New algorithm for maximizing submodular functions in real-time data changes.

In this paper we study a robust expected utility maximization problem with random endowment in discrete time. We give conditions under which an optimal strategy exists and derive a dual representation for the optimal utility. Our approach is based on a general representation result for monotone convex functionals, a fu…