arXiv research

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

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…

Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions …

Physarum-inspired solver speeds up LP solving in neural networks.

Graph neural networks improve solving linear optimization problems.

New method prunes large causal bounds LPs for scalable inference.

Much effort has been directed at algorithms for obtaining the highest probability configuration in a probabilistic random field model known as the maximum a posteriori (MAP) inference problem. In many situations, one could benefit from having not just a single solution, but the top M most probable solutions known as th…

Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time ener…

The linear Support Vector Machine (SVM) is a classic classification technique in machine learning. Motivated by applications in modern high dimensional statistics, we consider penalized SVM problems involving the minimization of a hinge-loss function with a convex sparsity-inducing regularizer such as: the L1-norm on t…

Transformers improve solving mixed-integer programs, especially CLSP.

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.

BanditLP optimizes personalized recommendations for large-scale systems.

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

Efficiently verifies neural networks by handling neuron splits, improving speed and accuracy.

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…

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.

Exact LAD line fitting via PALB with linear scaling and speed.

Study on liquidity providers' performance in decentralized exchanges.

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.

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

Quantum algorithms improve regret bounds for bandits with knapsacks.

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.

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…

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.

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.

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

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

Paper optimizes liquidity provision in decentralized finance markets.

Paper calculates greeks for DeFi LPs and introduces Impermanent Gain.

Maximum a posteriori (MAP) inference is a fundamental computational paradigm for statistical inference. In the setting of graphical models, MAP inference entails solving a combinatorial optimization problem to find the most likely configuration of the discrete-valued model. Linear programming (LP) relaxations in the Sh…

LP algorithm optimizes neural networks with architectural constraints.