On-device research index

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.

169,291 papers · 148 categories

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48 results for “many constraints”

Distributed Convex Optimization with Many Convex Constraintsmath.OC

Solves convex optimization with many constraints in a distributed system.

problem Solving convex optimization problems with many convex constraints in a distributed setting.

method Extension of ADMM to handle arbitrary inequality constraints.

result Inherits convergence guarantees of ADMM and Augmented Lagrangian method.

distributed optimization convex optimization ADMM Augmented Lagrangian many constraints

2016-10-07abs ↗pdf ↗

Graphical Representations for Algebraic Constraints of Linear Structural Equations Modelsmath.ST

Graphical notation simplifies complex polynomial constraints in linear models.

problem Complex polynomial constraints in linear structural equation models are impractical.

method Developed a graphical notation to represent these constraints.

result The graphical notation simplifies the representation of many polynomial constraints.

linear structural equation models polynomial constraints graphical notation covariance matrix

2022-08-01abs ↗pdf ↗

Private Optimization Without Constraint Violationscs.LG

Algorithm ensures privacy while strictly adhering to constraints.

problem Differential privacy with linear constraints that must be strictly followed.

method Developed an algorithm that releases a nearly-optimal solution satisfying constraints with probability 1.

result Achieved nearly optimal performance while preserving privacy and strictly adhering to constraints.

differential privacy resource allocation linear constraints privacy-preserving optimization probability 1 constraint satisfaction

2020-07-02abs ↗pdf ↗

Partial triangulations of surfaces with girth constraintsmath.GT

Research shows finiteness in triangulations with girth constraints.

problem Finiteness of cellular partial triangulations with girth constraints.

method Characterization of sparse graphs and contraction-minimal graphs.

result There are finitely many (3,6)-tight and (3,3)-tight graphs.

triangulations partial triangulations girth constraints finiteness sparse graphs

2023-09-19abs ↗pdf ↗

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraintscs.LG

Proposes a Thompson sampling algorithm for multi-objective contextual bandit problems with auxiliary constraints.

problem Real-world applications with multiple competing objectives and auxiliary constraints.

method Thompson sampling algorithm for multi-outcome contextual bandit problems with auxiliary constraints.

result Empirically evaluated and applied to a real-world video transcoding problem.

Thompson Sampling Contextual Bandit Reinforcement Learning Auxiliary Constraints Real-world Applications

2019-11-02abs ↗pdf ↗

Introducing user-prescribed constraints in Markov chains for nonlinear dimensionality reductioncs.LG

New method adds user constraints to Markov chains for better data reduction.

problem No systematic framework to impose user-defined constraints on Markov chains.

method Path entropy maximization to derive transition probabilities with user constraints.

result Improved nonlinear dimensionality reduction with user-prescribed constraints.

Markov chains dimensionality reduction user constraints kernel methods path entropy

2018-06-13abs ↗pdf ↗

Best Arm Identification with Safety Constraintscs.LG

Study best arm identification with safety constraints in bandit problems.

problem Real-world decision-making with safety constraints.

method Analyzed linear and monotonic reward and safety constraints, proposed algorithms.

result Guaranteed safe learning in both linear and general reward/safety constraint settings.

multi-armed bandit safety constraints best arm identification monotonic functions linear reward

2021-11-23abs ↗pdf ↗

Variational Inverse Problems for Second Order ODEs with and without Constraintsmath.DG

The paper explores solving inverse problems for ODEs with and without constraints.

problem Understanding when second order ODEs can represent Lagrangian models with or without constraints.

method Geometric techniques to address the inverse problem for both constrained and unconstrained systems of second order ODEs.

result The constrained case presents more ambiguities and complexities than the unconstrained one.

inverse problems second order ODEs Lagrangian models geometric techniques constraints

2026-06-04abs ↗pdf ↗

Infeasible Deterministic, Stochastic, and Variance-Reduction Algorithms for Optimization under Orthogonality Constraintsstat.ML

New algorithms reduce orthogonality constraint enforcement time in machine learning.

problem Efficiently solving orthogonality constraints in machine learning.

method Extending the landing algorithm to Stiefel manifold, incorporating stochastic and variance reduction techniques.

result All proposed methods achieve the same convergence rate as Riemannian counterparts enforcing constraints.

orthogonality constraints machine learning Riemannian optimization stochastic algorithms variance reduction

2023-03-29abs ↗pdf ↗

Optimal Skorokhod embedding under finitely-many marginal constraintsmath.PR

The Skorokhod embedding problem aims to represent a given probability measure on the real line as the distribution of Brownian motion stopped at a chosen stopping time. In this paper, we consider an extension of the optimal Skorokhod embedding problem to the case of finitely-many marginal constraints. Using the classic…

2015-06-12abs ↗pdf ↗

On Constrained Spectral Clustering and Its Applicationscs.LG

Constrained clustering has been well-studied for algorithms such as $K$ -means and hierarchical clustering. However, how to satisfy many constraints in these algorithmic settings has been shown to be intractable. One alternative to encode many constraints is to use spectral clustering, which remains a developing area. I…

2012-01-25abs ↗pdf ↗

Discovering Multiple Constraints that are Frequently Approximately Satisfiedcs.LG

Some high-dimensional data.sets can be modelled by assuming that there are many different linear constraints, each of which is Frequently Approximately Satisfied (FAS) by the data. The probability of a data vector under the model is then proportional to the product of the probabilities of its constraint violations. We …

2013-01-10abs ↗pdf ↗

Half-Space and Box Constraints as NUV Priors: First Resultsstat.ML

Proposes NUV priors for half-space and box constraints.

problem Adding constraints to linear Gaussian models without computational cost.

method Introduces NUV representations for half-space and box constraints.

result Adds constraints to linear Gaussian models without affecting computational tractability.

NUV priors half-space constraints box constraints linear Gaussian models

2021-08-31abs ↗pdf ↗

On The Projection Operator to A Three-view Cardinality Constrained Setcs.LG

Paper solves NP-hard projection for three-view cardinality constraints.

problem NP-hard projection onto cardinality constraint sets.

method Formulates projection as linear programming with TVCS.

result Linear programming solution matches original projection.

cardinality constraint projection operator NP-hard linear programming three-view structure

2017-03-21abs ↗pdf ↗

A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challengescs.LG

Survey of Gaussian process constraints for modeling expensive data.

problem Modeling expensive data with physical constraints.

method Overview of various Gaussian process constraints and their implementation.

result Discussion of computational challenges introduced by constraints.

Gaussian process constraints Bayesian expensive data machine learning

2020-06-16abs ↗pdf ↗

Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraintsstat.ML

Consider convex optimization problems subject to a large number of constraints. We focus on stochastic problems in which the objective takes the form of expected values and the feasible set is the intersection of a large number of convex sets. We propose a class of algorithms that perform both stochastic gradient desce…

2015-11-12abs ↗pdf ↗

Balancing Constraints and Rewards with Meta-Gradient D4PGcs.LG

Meta-gradient D4PG optimizes performance and constraint adherence in RL.

problem Balancing performance and adherence to complex constraints in RL.

method Uses meta-gradients to find a balance between expected return and minimizing constraint violations.

result Meta-gradient D4PG consistently outperforms baselines across MuJoCo domains.

Reinforcement Learning Meta-Gradients Constraint Handling Soft Constraints MuJoCo

2020-10-13abs ↗pdf ↗

Applications of Fixed Point Theorems to the Vacuum Einstein Constraint Equations with Non-Constant Mean Curvaturemath-ph

In this paper, we introduce new methods for solving the vacuum Einstein constraints equations: the first one is based on Schaefer's fixed point theorem (known methods use Schauder's fixed point theorem) while the second one uses the concept of half-continuity coupled with the introduction of local supersolutions. These…

2014-05-29abs ↗pdf ↗

Bayesian Optimization with Unknown Constraintsstat.ML

Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general ca…

2014-03-22abs ↗pdf ↗

Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraintscs.LG

Stabilized neural differential equations enforce constraints on dynamical systems.

problem Ensuring dynamical systems preserve known constraints like conservation laws.

method SNDEs with a stabilization term to enforce manifold constraints.

result SNDEs outperform existing methods and broaden constraint types.

neural differential equations constraint enforcement dynamical systems stabilization conservation laws

2023-06-16abs ↗pdf ↗

Robust Metric Learning by Smooth Optimizationcs.LG

Most existing distance metric learning methods assume perfect side information that is usually given in pairwise or triplet constraints. Instead, in many real-world applications, the constraints are derived from side information, such as users' implicit feedbacks and citations among articles. As a result, these constra…

2012-03-15abs ↗pdf ↗

Online Convex Optimization with Stochastic Constraintsmath.OC

Paper tackles online convex optimization with stochastic constraints.

problem Online convex optimization with stochastic constraints.

method Proposes a new algorithm achieving

O(\sqrt{T})

expected regret and constraint violations and

O(\sqrt{T}\log(T))

high probability regret and constraint violations.

result Achieves optimal regret and constraint violation bounds.

online optimization convex optimization stochastic constraints regret minimization algorithm

2017-08-12abs ↗pdf ↗

DC3: A learning method for optimization with hard constraintscs.LG

DC3 uses deep learning to solve hard-constrained optimization problems efficiently.

problem Hard constraints in optimization problems make classical solvers slow and infeasible.

method DC3 employs a differentiable procedure to enforce feasibility and unrolls corrections for inequality constraints.

result DC3 achieves near-optimal solutions while maintaining feasibility in both synthetic and real-world tasks.

optimization hard constraints deep learning feasibility approximate solvers

2021-04-25abs ↗pdf ↗

Constrained Clustering and Multiple Kernel Learning without Pairwise Constraint Relaxationcs.LG

New algorithm clusters data and learns kernels without relaxing constraints.

problem Learning kernels or distance metrics from pairwise constraints without losing generalization.

method Joint clustering and kernel learning without relaxing constraints.

result Outperforms existing approaches on diverse datasets.

clustering kernel learning pairwise constraints generalization

2022-03-23abs ↗pdf ↗

Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimationcs.LG

Many machine learning approaches are characterized by information constraints on how they interact with the training data. These include memory and sequential access constraints (e.g. fast first-order methods to solve stochastic optimization problems); communication constraints (e.g. distributed learning); partial acce…

2013-11-14abs ↗pdf ↗

Categorical Distributions of Maximum Entropy under Marginal Constraintshep-th

A framework estimates categorical distributions under constraints, ensuring generality and uniqueness.

problem Estimating categorical distributions summarizing sample data under marginal constraints.

method Theoretical framework + Iterative Proportional Fitting (IPF) to estimate the distribution.

result A unique categorical distribution of Maximum Entropy under marginal constraints exists and is estimated.

Maximum Entropy Marginal Constraints Iterative Proportional Fitting Categorical Distributions Machine Learning

2022-04-07abs ↗pdf ↗

Predictive Entropy Search for Bayesian Optimization with Unknown Constraintsstat.ML

Unknown constraints arise in many types of expensive black-box optimization problems. Several methods have been proposed recently for performing Bayesian optimization with constraints, based on the expected improvement (EI) heuristic. However, EI can lead to pathologies when used with constraints. For example, in the c…

2015-02-18abs ↗pdf ↗

Handling the Positive-Definite Constraint in the Bayesian Learning Rulestat.ML

Improved Bayesian learning rule handles positive-definite constraints efficiently.

problem Bayesian learning rule struggles with positive-definite constraints.

method Proposes an improved rule using Riemannian gradient methods for block-coordinate natural parameterization.

result Outperforms existing methods without increased computation.

Bayesian learning positive-definite constraints Riemannian gradient variational inference natural-gradient

2020-02-24abs ↗pdf ↗

Once-for-All: Train One Network and Specialize it for Efficient Deploymentcs.LG

Train one network for efficient deployment across many devices.

problem Efficient inference across diverse devices with minimal resource constraints.

method Once-for-All (OFA) network training and progressive shrinking algorithm.

result OFA network achieves state-of-the-art accuracy with significantly reduced training time and resource usage.

neural architecture search neural network compression edge devices resource constraints image classification

2019-08-26abs ↗pdf ↗

Optimal Convergence for Stochastic Optimization with Multiple Expectation Constraintsmath.ST

Paper optimizes stochastic optimization with multiple constraints.

problem Optimizing objective functions with multiple expectation constraints.

method Extended cooperative stochastic approximation algorithm.

result Achieves optimal convergence rate for optimality gap and constraint violation.

stochastic optimization expectation constraints cooperative approximation optimal convergence

2019-06-08abs ↗pdf ↗

Model-Free Unsupervised Learning for Optimization Problems with Constraintscs.LG

Proposes model-free learning for solving optimization problems with constraints.

problem Hard or impossible to derive expressions for objective function or constraints in optimization problems.

method Uses neural networks to parameterize the function and constraints, trains all networks simultaneously, and connects to reinforcement learning.

result Demonstrates the efficiency of model-free learning in power control problems.

model-free learning unsupervised learning optimization problems constraints wireless communications

2019-07-30abs ↗pdf ↗

Safe Model-Based Multi-Agent Mean-Field Reinforcement Learningcs.LG

Safe-M $^3$ -UCRL learns safe policies for multi-agent systems with global constraints.

problem Global constraints in mean-field reinforcement learning for multi-agent systems.

method Safe-M

^3

-UCRL uses epistemic uncertainty and log-barrier approach to ensure constraints satisfaction.

result Safe-M

^3

-UCRL learns safe policies for multi-agent systems with global constraints.

mean-field reinforcement learning multi-agent systems global constraints safe policies epistemic uncertainty

2023-06-29abs ↗pdf ↗

Constraint-Aware Neural Networks for Riemann Problemsphysics.comp-ph

Constraint-aware neural networks improve accuracy in fluid flow simulations.

problem Ensuring physical constraints in neural network simulations for fluid dynamics.

method Two strategies to create constraint-aware neural networks for Riemann problems.

result Decrease in constraint deviation correlates with low discretization errors.

neural networks constraint satisfaction fluid dynamics Riemann problems wave motion

2019-04-29abs ↗pdf ↗

Efficiently Searching for Frustrated Cycles in MAP Inferencecs.DS

Dual decomposition provides a tractable framework for designing algorithms for finding the most probable (MAP) configuration in graphical models. However, for many real-world inference problems, the typical decomposition has a large integrality gap, due to frustrated cycles. One way to tighten the relaxation is to intr…

2012-10-16abs ↗pdf ↗

Consistent Multiclass Algorithms for Complex Metrics and Constraintsstat.ML

Consistent algorithms for multiclass learning with complex metrics and constraints.

problem Learning with complex performance metrics and constraints.

method General framework for designing consistent algorithms by viewing the problem as an optimization over feasible confusion matrices.

result Rates of convergence to the optimal (feasible) classifier, showing asymptotic consistency.

multiclass learning consistent algorithms complex metrics constraints optimization

2022-10-18abs ↗pdf ↗

A Duality Result for Robust Optimization with Expectation Constraintsq-fin.MF

Paper solves robust optimization with expectation constraints for financial derivatives.

problem Computing robust maximization solutions with expectation constraints.

method Shows a single convex minimization problem for super-replication values.

result No-arbitrage bounds on various financial derivatives.

robust optimization expectation constraints super-replication financial derivatives convex minimization

2016-10-04abs ↗pdf ↗

Comonotonic improvement under feasibility constraintsecon.TH

Comonotonic allocations are restored under certain constraints, improving risk-sharing.

problem Feasibility constraints can distort optimal risk-sharing allocations.

method Identified componentwise convex-order solidity as a sufficient condition to restore comonotonic allocations.

result Componentwise convex-order solidity ensures comonotonic improvements under feasible constraints.

comonotonic improvement feasibility constraints risk-sharing convex-order solidity Value-at-Risk caps

2026-04-27abs ↗pdf ↗

Liquidity in Credit Networks with Constrained Agentscs.GT

Paper examines constraints on cryptocurrency networks to improve liquidity and capital costs.

problem Improving liquidity in cryptocurrency networks with limited capital deposits.

method Introduces constraints to bound loss in default scenarios and simplifies network structure.

result Achieves optimal tradeoff between liquidity and capital costs in payment networks.

cryptocurrency credit networks liquidity default constraints capital deposits

2019-10-05abs ↗pdf ↗

Neural Guided Constraint Logic Programming for Program Synthesiscs.LG

Neural model guides PBE problem solving in programming.

problem Synthesizing programs from example inputs/outputs.

method Uses a neural model to guide miniKanren's constraint logic programming system.

result Synthesizes programs faster and generalizes to larger problems.

Program synthesis Constraint logic programming Neural networks Programming by example Recurrent neural networks

2018-09-08abs ↗pdf ↗

Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platformscs.LG

Distill-Net creates efficient CNNs for IoT by distilling complex models.

problem Efficient inference of deep CNNs on resource-constrained IoT platforms.

method Application-specific distillation of deep CNNs.

result Efficient inference on resource-constrained platforms with high accuracy.

IoT CNN Distillation Efficiency Resource-constrained

2018-12-16abs ↗pdf ↗

Classifying Causal Structures: Ascertaining when Classical Correlations are Constrained by Inequalitiesquant-ph

Detects causal scenarios with inequality constraints among classical correlations.

problem Classifying causal structures and identifying those with inequality constraints.

method Using d-separation, e-separation, incompatible supports, and HLP condition.

result Resolved all but three causal scenarios with up to 4 observed variables.

causal structure inequality constraints classical correlations d-separation e-separation

2023-08-04abs ↗pdf ↗

Algebraic Equivalence of Linear Structural Equation Modelsmath.ST

New method uses algebraic trek criterion for model selection in linear structural equation models.

problem Deciding algebraic equivalence of linear structural equation models.

method Using the half-trek criterion to enumerate and compare constraints.

result Algebraic constraints improve model selection accuracy.

structural equation models linear models model selection algebraic constraints trek criterion

2018-07-10abs ↗pdf ↗

POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networkscs.LG

POLICE enforces linear constraints on deep neural networks efficiently.

problem Enforcing constraints on deep neural networks without affecting optimization.

method Provably optimal affine constraint enforcement method that minimally modifies DNNs.

result POLICE ensures DNNs fulfill affine constraints during training and testing.

deep learning constraint enforcement provably optimal affine constraints neural networks

2022-11-02abs ↗pdf ↗

Nonnegativity-Enforced Gaussian Process Regressioncs.LG

Enforces physical constraints in GP regression models.

problem Unbounded GP models can produce infeasible values.

method Enforces nonnegativity constraints probabilistically.

result Reduces model variance and enforces physical bounds.

Gaussian Process Regression Nonnegativity Physical Constraints Probabilistic Approach

2020-04-07abs ↗pdf ↗

Reinforcement Learning with Convex Constraintscs.LG

Proposes an RL algorithm for handling convex constraints.

problem Handling constraints in reinforcement learning.

method Algorithm for convex constraints in RL.

result Algorithm can enforce new properties like diversity.

reinforcement learning convex constraints theoretical guarantees model-free model-based

2019-06-21abs ↗pdf ↗

The Theory and Practice of MAP Inference over Non-Convex Constraintscs.LG

The paper tackles MAP inference over non-convex constraints in safety-critical settings.

problem Efficiently computing MAP predictions subject to non-convex constraints is challenging.

method The paper investigates conditions for exact and efficient MAP inference over continuous variables and devises scalable algorithms for both tractable and general cases.

result The proposed methods outperform constraint-agnostic baselines and scale to complex densities.

MAP inference non-convex constraints scalable algorithms safety-critical settings

2026-02-09abs ↗pdf ↗

DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variablescs.LG

DiffSlack learns neural networks with nonlinear constraints via learnable slack variables.

problem Enforcing nonlinear inequality constraints in neural networks.

method DiffSlack reformulates inequalities as equalities with learnable slack variables, predicting them as part of the network output.

result DiffSlack achieves higher planning success rates and stronger geometric constraint satisfaction compared to existing methods.

neural networks inequality constraints differentiable projection CARLA experiments vehicle path planning

2026-06-03abs ↗pdf ↗

Stochastic Dominance Constrained Optimization with S-shaped Utilities: Poor-Performance-Region Algorithm and Neural Networkq-fin.MF

We solve S-shaped utility portfolio selection with SD constraints using algorithms and neural networks.

problem Optimizing portfolios with S-shaped utility functions under SD constraints.

method First-order SD constraint solution, numerical algorithm for SSD, neural network approach.

result Effective numerical and neural network solutions for SSD constrained problems.

portfolio optimization S-shaped utility stochastic dominance neural networks numerical algorithms

2025-11-29abs ↗pdf ↗