arXiv research

New algorithms solve L1-regularized SVMs and related LPs, outperforming existing methods.

New algorithm reduces online decision-making regret with efficient LP re-solving and parallel first-order method.

Nonnegative matrix factorization (NMF) has been shown to be identifiable under the separability assumption, under which all the columns(or rows) of the input data matrix belong to the convex cone generated by only a few of these columns(or rows) [1]. In real applications, however, such separability assumption is hard t…

We provide a simple method and relevant theoretical analysis for efficiently estimating higher-order lp distances. While the analysis mainly focuses on l4, our methodology extends naturally to p = 6,8,10..., (i.e., when p is even). Distance-based methods are popular in machine learning. In large-scale applications, sto…

Optimal fees protect passive LPs in AMMs under varying market conditions.

BanditLP optimizes personalized recommendations for large-scale systems.

Sharp Lp affine isoperimetric inequalities are established for the entire class of Lp projection bodies and the entire class of Lp centroid bodies. These new inequalities strengthen the Lp Petty projection and the Lp Busemann--Petty centroid inequality.

This study compares 16 embedding-based link prediction methods for knowledge graphs.

We consider the problem of computing a Wasserstein barycenter for a set of discrete probability distributions with finite supports, which finds many applications in areas such as statistics, machine learning and image processing. When the support points of the barycenter are pre-specified, this problem can be modeled a…

FLAIR measures LP competitiveness in AMMs, improving LP performance evaluations.

JIT liquidity providers can sometimes reduce overall market liquidity by crowding out passive LPs.

Study on LP-Sasakian manifolds with generalized η-Ricci solitons.

In this paper, we give a new sharp generalization bound of lp-MKL which is a generalized framework of multiple kernel learning (MKL) and imposes lp-mixed-norm regularization instead of l1-mixed-norm regularization. We utilize localization techniques to obtain the sharp learning rate. The bound is characterized by the d…

A robust algorithm for non-negative matrix factorization (NMF) is presented in this paper with the purpose of dealing with large-scale data, where the separability assumption is satisfied. In particular, we modify the Linear Programming (LP) algorithm of [9] by introducing a reduced set of constraints for exact NMF. In…

The object of the present paper is to study locally $φ$-symmetric LP-Sasakian manifolds admitting semi-symmetric metric connection and obtain a necessary and sufficient condition for a locally $φ$-symmetric LP-Sasakian manifold with respect to semi-symmetric metric connection to be locally $φ$-symmetric LP-Sasakian man…

The abstract discusses p-harmonic forms and their geometric properties, proving new theorems about Lp-cohomology.

Study on liquidity providers' performance in decentralized exchanges.

Efficiently computes quasiconcave envelope with limited data.

Classifies SL(n) covariant matrix-valued valuations on Lp-spaces.

In this work we introduce a category of discrete Lagrange--Poincare systems LP_d and study some of its properties. In particular, we show that the discrete mechanical systems and the discrete mechanical systems obtained by the Lagrangian reduction of symmetric discrete mechanical systems are objects in LP_d. We introdu…

Study on convex capillary hypersurfaces with Lp curvature in half-space.

Study analyzes factors affecting profits in crypto liquidity provision.

New LP method recovers MAP solution from noisy stable instances.

Study growth of LP wealth in G3Ms affected by trading fees and arbitrage.

Optimizes liquidity withdrawal timing for AMM LPs to balance fees and impermanent loss.

This is a short survey of Riemannian geometric applications of Lp-cohomology of thick spaces, p not equal to 2.

High-fee pools attract more liquidity but execute less volume; low-fee pools have more stable LPs.

New method prunes large causal bounds LPs for scalable inference.

We use a new combinatorial technique to prove the optimal interior partial regularity result for Lp-vectorfields with integer fluxes minimizing the Lp-energy. More precisely, we prove that the minimal vectorfields are Hölder outside a set which is locally finite inside the domain. The results continue the program start…

Existence and uniqueness of the solution to the discrete Lp Minkowski problem for $\mathfrak{p}$-capacity are proved when $p \geq 1$ and $1<\mathfrak{p}<n$. For general Lp Minkowski problem for $\mathfrak{p}$-capacity, existence and uniqueness of the solution are given when $p \geq 1$ and $1<\mathfrak{p}\le 2$. These r…

LP-SparseMAP relaxes SparseMAP for more complex structures.

We prove that the first reduced cohomology with values in a mixing Lp-representation, p larger than 1, vanishes for a class of amenable groups including connected amenable Lie groups. In particular this solves for this class of amenable groups a conjecture of Gromov saying that every finitely generated amenable group h…

AMM finds optimal contract for LPs to maximize order flow.

MAP inference for general energy functions remains a challenging problem. While most efforts are channeled towards improving the linear programming (LP) based relaxation, this work is motivated by the quadratic programming (QP) relaxation. We propose a novel MAP relaxation that penalizes the Kullback-Leibler divergence…

Proposes a method to repair arbitrage in option prices data.

AI enhances refinery optimization by detecting data errors and improving decision-making.

Optimal fees for G3Ms align LP value with market accuracy.

New formula identifies and quantifies costs for automated market makers.

Proves existence and uniqueness of solutions to the Lp Gaussian Minkowski problem.

New offline RL algorithm with optimal sample complexity using LP and error bounds.

Modeling DEX liquidity with heterogeneous LPs and MEV bots.

This paper improves conformal prediction for robust interval estimation under distribution shifts.

Paper optimizes liquidity provision in decentralized finance markets.

LP-FT improves personalized model training in FL by balancing generalization and personalization.

Paper calculates greeks for DeFi LPs and introduces Impermanent Gain.

LP algorithm optimizes neural networks with architectural constraints.

We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the lp-norm of the parameters. We discuss several implications of Lipschitz parametrization. We present an upper bound of the Kullback-Leibler divergence that allows understanding methods that penalize the …

Graph neural networks improve solving linear optimization problems.