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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.

168,695 papers · 148 categories

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125249374498 · Jun 202019922001200920172026
48 results for stochastic variational inequalities

Survey of methods for solving smooth stochastic variational inequalities.

problem Solving smooth (strongly) monotone stochastic variational inequalities.
method Deterministic foundation, general stochastic formulation, finite sum setup, recent advances.
result Review of various methods for solving smooth stochastic variational inequalities.

Unified approach for first-order methods with Markovian noise in stochastic optimization and variational inequalities.

problem Stochastic optimization problems with Markovian noise.
method Unified theoretical analysis of first-order gradient methods using randomized batching and multilevel Monte Carlo.
result Optimal (linear) dependence on the mixing time of the noise sequence, eliminating previous limiting assumptions.

New algorithms reduce variance in solving complex mathematical problems.

problem Solving convex-concave saddle point problems, variational inequalities, and inclusions.
method Stochastic variance reduction for extragradient, forward-backward-forward, and forward-reflected-backward methods.
result All proposed methods converge with complexities matching or improving deterministic counterparts.

New algorithms solve stochastic variational inequalities without bounded variance assumption.

problem Solving stochastic variational inequalities without bounded variance assumption.
method Developed algorithms for two classes of problems: monotone and structured nonmonotone VIs.
result Oracle complexity of O(ε^-4) for solving VIs with unbounded domains and possibly unbounded variance.

Market makers optimize trading with a new implicit scheme for complex inequalities.

problem Optimizing trading in a limit order book with stochastic and impulse control.
method Implicit numerical scheme coupled with policy iteration algorithm.
result Convergence to the unique viscosity solution of the HJBQVI.

New insights into stochastic methods for solving variational inequalities.

problem Understanding convergence behaviors of stochastic algorithms in variational inequalities.
method Re-casting SEG/SGDA as Markov Chains to analyze their probabilistic structures.
result The average iterate is asymptotically normal with a unique invariant distribution for various VIPs.

Two new algorithms solve privacy-constrained SVI and SSP problems.

problem Privacy-constrained stochastic variational inequality and saddle-point problems.
method Proposed Noisy Stochastic Extragradient (NSEG) and Noisy Inexact Stochastic Proximal Point (NISPP) algorithms.
result Optimal risk bounds for weak gap function with sampling with replacement.

MER algorithm speeds up VI solving with Markovian data.

problem Solving stochastic variational inequalities with Markovian data.
method MER algorithm using multi-scale sampling from a Markovian buffer.
result Achieves faster convergence without knowing Markov chain mixing time.

New control theory for self-path-dependent problems solves unique constraints.

problem Optimal control with self-path-dependent constraints in stochastic systems.
method Introduces new HJB equations for variational inequalities with historical maximum controls.
result Value functions are viscosity solutions to HJB equations under Lipschitz conditions.

New adaptive methods for constrained convex optimization and variational inequalities.

problem Optimization of constrained convex problems and variational inequalities.
method AdaACSA and AdaAGD+ are accelerated methods that achieve nearly-optimal convergence rates for smooth and non-smooth functions.
result Achieve nearly-optimal convergence rates for both smooth and non-smooth functions, even with stochastic gradients.

Unified analysis of efficient local training methods for distributed variational inequalities.

problem Efficient distributed/federated learning for variational inequality problems.
method Unified convergence analysis of communication-efficient local training methods.
result First local gradient descent-accent algorithms with improved communication complexity.

Alternative neural network training using monotone variational inequality.

problem Training neural networks efficiently and with guarantees.
method Using monotone variational inequality to solve non-convex problems efficiently.
result Our approach leads to fast convergence and competitive performance compared to traditional methods.

We improve bounds for stochastic processes, especially those with heavy tails.

problem Bounding the concentration of sub-ψψ processes with heavy tails.
method Variational approach to concentration, focusing on sub-Gaussian and other tail conditions.
result First dimension-free self-normalized empirical Bernstein inequality.

This paper shows how to learn variational inequalities fast with strong monotonicity.

problem Learning variational inequalities efficiently.
method Extending convex optimization techniques to variational inequalities with strong monotonicity.
result Fast generalization rates of Θ(1/ε)Θ(1/ε) for learning variational inequalities.

Investor aims to meet financial goals with deadlines and target amounts, considering stock trading costs.

problem Goal-based portfolio selection with fixed transaction costs.
method Stochastic Perron's method to show value function is unique viscosity solution to quasi-variational inequalities. Existence of optimal strategy established.
result Optimal trading strategy differs significantly from frictionless case, revealing complex regions and strategies.

Improved convergence for VIPs with SEG-RR, a variant of SEG with random reshuffling.

problem Solving variational inequality problems (VIPs) in machine learning.
method Stochastic Extragradient with Random Reshuffling (SEG-RR).
result SEG-RR achieves faster convergence rates than with-replacement variants for certain VIP classes.

Improved analysis of extragradient methods for structured VIPs.

problem Efficiently solving large-scale VIPs with weaker conditions.
method Single-call stochastic extragradient methods with expected residual condition.
result Convergence guarantees for quasi-strongly monotone and weak Minty VIPs.

New methods reduce communication in distributed training for variational inequalities.

problem Reducing communication in distributed training for high-dimensional models.
method Distributed methods with compressed communication for solving variational inequalities.
result Theoretical guarantees and practical algorithms for compressed communication.

New convergence guarantees for SGDA and SCO under expected co-coercivity.

problem Solving smooth games with stochastic gradient descent-ascent and consensus optimization.
method Introducing expected co-coercivity and proving convergence guarantees for SGDA and SCO.
result Linear convergence of SGDA and SCO to a neighborhood of the solution with constant step-size, and convergence to the exact solution with stepsize-switching rules.

Study on private algorithms for saddle point and variational inequalities, improving efficiency and applicability.

problem Private algorithms for solving saddle point and variational inequalities under differential privacy constraints.
method Developed a recursive regularization algorithm for both Euclidean and non-Euclidean setups, providing bounds on strong SP-gap and VI-gap.
result Achieved nearly optimal rates for strong SP-gap and VI-gap under (ε,δ)(ε,δ)-differential privacy, applicable to various p,qp,q setups.

Stochastic approximation proves asymptotic normality for non-smooth problems.

problem Solving non-smooth stochastic approximation problems.
method Stochastic approximation algorithms for solving smooth equations, extended to non-smooth problems.
result Asymptotic normality and optimality in non-smooth stochastic approximation is proven.

We solve explicitly a two-dimensional singular control problem of finite fuel type for infinite time horizon. The problem stems from the optimal liquidation of an asset position in a financial market with multiplicative and transient price impact. Liquidity is stochastic in that the volume effect process, which determi…

2016-03-21abs ↗pdf ↗

New algorithm solves structured nonconvex-nonconcave min-max problems.

problem Min-max optimization challenges in deep learning.
method Generalized extragradient algorithm for structured nonconvex-nonconcave problems.
result Algorithm converges to stationary points in Euclidean and p\ell_p spaces.

Neural networks solve variational inequalities for optimal stopping problems.

problem Solving variational inequalities for optimal stopping problems in finance.
method Proposed neural network approach using loss functions directly incorporating variational inequality on whole domain.
result Existence and convergence of neural networks whose losses converge to zero.

This paper combines three techniques to reduce communications in distributed variational inequalities.

problem Efficiently communicating solutions in large-scale distributed variational inequalities.
method Combining similarity, compression, and local steps to reduce communication rounds and cost.
result Best theoretical guarantees of communication complexity and superior performance in adversarial learning experiments.

Deviation inequalities for stochastic approximation methods.

problem Establishing bounds on the deviation of stochastic approximation methods.
method Martingale approximation method for separately Lipschitz functions.
result Established various deviation inequalities for stochastic approximation by averaging and minimization.

In this paper we propose and analyze a class of NN-player stochastic games that include finite fuel stochastic games as a special case. We first derive sufficient conditions for the Nash equilibrium (NE) in the form of a verification theorem. The associated Quasi-Variational-Inequalities include an essential game comp…

2018-09-10abs ↗pdf ↗

Extends tracking guarantees for time-varying variational inequalities.

problem Tracking solutions of time-varying variational inequalities.
method Extends existing results to sublinear solution paths and periodic problems.
result Discrete dynamical systems of periodic time-varying VI can exhibit chaotic behavior or converge to the solution.

New proof of Gaffney's inequality for differential forms on manifolds with boundary.

problem Proving Gaffney's inequality for differential forms on manifolds with boundary.
method Variational approach combined with Bochner's technique.
result New proof of Gaffney's inequality for differential forms.

Study optimal stopping for diffusion processes with unknown primitives, applying RL and martingale methods.

problem Optimal stopping for diffusion processes with unknown model primitives.
method Continuous-time reinforcement learning framework, variational inequality formulation, stochastic optimal control, entropy regularizer, semi-analytical optimal Bernoulli distribution, policy improvement theorem, policy iterations.
result Demonstrated high accuracy in learning value functions and characterizing free boundaries for various optimal stopping problems.

A new method solves variational inequality problems with multiple constraints without needing optimal Lagrange multipliers.

problem Solving variational inequality problems with multiple functional constraints efficiently.
method Constrained Gradient Method (CGM) for Minty variational inequality problems.
result The Constrained Gradient Method achieves complexity similar to projection-based methods but with cheaper oracles.

Paper proposes an alternative to MLE for GLMs with non-canonical link functions.

problem Challenges in MLE for GLMs with non-canonical link functions.
method Variational Inequality (VI) estimation framework.
result Established finite-sample error bounds and asymptotic normality for VI estimator.

Improved algorithms for convex-concave min-max optimization and monotone variational inequalities.

problem Efficiently solving constrained convex-concave min-max problems and monotone variational inequalities.
method Higher-order methods achieving iteration complexities of O(1/T^{ rac{p+1}{2}}) for p-th order derivatives.
result Achieved improved convergence rates for min-max and monotone variational inequalities.

Paper tackles utility maximization with job-switching and retirement constraints.

problem Maximizing utility with job-switching and retirement constraints.
method Dual-martingale approach and double obstacle problem theory.
result Characterization of optimal job-switching strategy and wealth boundaries.

New schemes improve error estimates for sampling from non-log-concave distributions.

problem Improving sampling from non-log-concave distributions with super-linear drift growth.
method Developed tamed Euler and randomized Euler schemes with error estimates.
result Near-optimal error bounds for sampling and optimization problems.

New analysis of SGD with MCMC gradient estimator shows convergence rate and saddle point escape.

problem Analyzing SGD with MCMC gradient estimator under complex conditions.
method Introduced MCMC-SGD, analyzed convergence rate and saddle point escape using Bernstein inequality.
result Proven first order convergence rate O(logK/nK)O(\log K/\sqrt{n K}) and saddle point escape at least O(ε11/2log2(1/ε))O(ε^{-11/2}\log^{2}(1/ε) ) steps.