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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.

169,341 papers · 148 categories

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48 results for linear matrix inequality

Develops inequalities for high-dimensional linear processes with dependent innovations.

problem Estimating high-dimensional VAR(p) systems and HAC covariance estimation.
method Concentration inequalities for ll_\infty norm of vector linear processes with sub-Weibull, mixingale innovations.
result Obtained concentration bounds for the maximum entrywise norm of lag-hh autocovariance matrices.

Paper shows linear convergence of ISTA and FISTA for ill-conditioned images.

problem Solving linear inverse problems with sparse representation in signal and image processing.
method Revisits iterative shrinkage-thresholding algorithms (ISTA) and improves their convergence properties.
result Linear convergence of ISTA and FISTA for strongly convex smooth parts, even in ill-conditioned cases.

This paper describes a simple framework for structured sparse recovery based on convex optimization. We show that many structured sparsity models can be naturally represented by linear matrix inequalities on the support of the unknown parameters, where the constraint matrix has a totally unimodular (TU) structure. For …

2014-11-07abs ↗pdf ↗

Paper develops new inequalities for high-dimensional statistics under sub-Weibull tail assumptions.

problem High-dimensional statistical methods under sub-Weibull tail assumptions.
method Develops new concentration inequalities for sums of independent random variables under sub-Weibull tail assumptions.
result Concentration inequalities match asymptotics of central limit theorem and match sub-Gaussian tail behavior.

New technique stabilizes singular values in concatenated matrices.

problem How singular values of concatenated matrices relate to individual components.
method Developed perturbation technique extending classical results to concatenated matrices.
result Dominant singular values remain stable under small perturbations in submatrices.

New conditions ensure Dantzig-Wolfe relaxation matches rank-constrained optimization problems.

problem Rank-constrained optimization problems with linear matrix inequalities.
method Investigates Dantzig-Wolfe relaxation and develops conditions for exactness.
result Conditions for extreme point, convex hull, and objective exactness.

New principle in online learning: Regret can be expressed using sufficient statistics and a Burkholder function.

problem Achieving optimal online learning performance with limited memory.
method Introducing a Burkholder function that depends only on sufficient statistics, not the entire data sequence.
result Developed novel online strategies for matrix prediction and parameter-free supervised learning.

New tail inequalities for sums of random matrices without matrix-dimension terms.

problem Tail behavior of matrix functions in high-dimensional settings.
method Developed new tail inequalities for matrix sums, independent of matrix dimension.
result Tail inequalities for various matrix functions without matrix-dimension terms.

Paper presents neural network controllers for offset-free setpoint tracking.

problem Offset-free setpoint tracking using neural network controllers.
method Exploiting slope-restricted activation functions, linear matrix inequalities are used to verify stability.
result Global and local stability conditions for neural network controllers are derived.

We derive exponential tail inequalities for sums of random matrices with no dependence on the explicit matrix dimensions. These are similar to the matrix versions of the Chernoff bound and Bernstein inequality except with the explicit matrix dimensions replaced by a trace quantity that can be small even when the dimens…

2011-04-09abs ↗pdf ↗

The paper analyzes a method for non-negative matrix factorization using quasi-Bayesian aggregation.

problem Understanding the convergence rate of non-negative matrix factorization with quasi-Bayesian methods.
method Derives an oracle inequality for an aggregated estimator under a broad class of prior distributions.
result The prior distribution significantly influences the rate of convergence in non-negative matrix factorization.

New inequalities for matrix supermartingales converge under various conditions.

problem Convergence and maximal inequalities of supermartingales in positive semidefinite matrices.
method Developed new concentration inequalities for matrix supermartingales.
result New inequalities for matrix supermartingales under different tail conditions.

Derives matrix Harnack inequalities for semilinear heat equations on manifolds.

problem Bounding solutions of semilinear heat equations on manifolds with geometric constraints.
method Applies Li-Yau estimates to derive Harnack inequalities for positive solutions.
result Derives matrix Harnack inequalities for positive solutions of semilinear heat equations.

Data science reveals co-evolution of income inequality and savings across countries.

problem Understanding the co-evolution of income inequality and savings across countries.
method Time series data for Gini indices and Gross Domestic Savings (% of GDP) were used to construct correlation and similarity matrices, and a multi-dimensional scaling technique was applied. Linear regression was used to test the empirical linkage between income inequality and savings.
result The empirical model proposed by Chakraborti-Chakrabarti (2000) holds reasonably true for many economies of the world, showing a moderate relationship between income inequality and savings.

We prove a generalization of the Li-Yau estimate for a board class of second order linear parabolic equations. As a consequence, we obtain a new Cheeger-Yau inequality and a new Harnack inequality for these equations. We also prove a Hamilton-Li-Yau estimate, which is a matrix version of the Li-Yau estimate, for these …

2012-11-23abs ↗pdf ↗

Unified framework for solving linear systems with improved convergence rates.

problem Efficiently solving linear systems with randomized batch-sampling methods.
method Developed a unified randomized batch-sampling Kaczmarz framework with concentration inequalities for analysis.
result Derived new expected linear convergence rate bounds that are tighter and more reflective of empirical behavior.

The paper proves new Harnack inequalities for various nonlinear heat equations on manifolds.

problem Analyzing and proving new Harnack inequalities for nonlinear heat equations.
method Proving constrained trace, matrix, and interpolated Harnack inequalities for specific nonlinear heat equations.
result Derives new differential Harnack inequalities with time-exponential correction terms.

Study non-asymptotic bounds on correlation in high-dimensional linear systems, revealing invariant subspaces and bottlenecks.

problem Understanding correlation and mixing in high-dimensional linear systems with Gaussian noise.
method Sampling from sub-trajectories, using Talagrand's inequality, and analyzing invariant subspaces.
result Large discrepancy between algebraic and geometric multiplicity leads to bottlenecks between invariant subspaces.

Nonnegative sectional curvature linked to matrix displacement convexity.

problem Nonnegative sectional curvature in Riemannian manifolds.
method Matrix displacement convexity as a criterion for nonnegative sectional curvature.
result Entropy functional matrix displacement convexity implies nonnegative sectional curvature.

A new matrix concentration inequality for random products of matrices.

problem Understanding the behavior of random matrix products under bounded independent positive semidefinite matrices.
method Developed a non-asymptotic concentration inequality for the product of matrices.
result The inequality provides a bound on the deviation of the matrix product from its expected value.

The paper sets bounds on how much regret is unavoidable in adaptive LQR with unknown B-matrix.

problem Understanding the limits of adaptive LQR with unknown B-matrix.
method Local asymptotic minimax regret lower bounds using van Trees' inequality and Bellman error representation.
result Logarithmic regret is impossible if the parametrization induces an uninformative optimal policy.

We use topological methods to prove a semicontinuity property of the Hodge spectra for analytic germs defined on an isolated surface singularity. For this we introduce an analogue of the Seifert matrix (the fractured Seifert matrix), and of the Levine--Tristram signatures associated with it, defined for null-homologous…

2013-08-23abs ↗pdf ↗

New algorithms detect anomalies in high-dimensional data using less space than traditional methods.

problem Finding anomalies in high-dimensional data using PCA-based scores efficiently.
method Developed streaming algorithms using linear or sublinear space, and proved matrix perturbation inequalities.
result Proved that certain matrix sketching techniques can approximate PCA-based anomaly scores efficiently.

This work establishes always-valid risk bounds for online matrix completion.

problem Challenges in establishing always-valid concentration inequalities for online matrix completion.
method Combines non-asymptotic martingale concentration and regularized low-rank matrix regression.
result Establishes always-valid risk bound process for online matrix completion.

Study recasts learning non-linear functions from noisy data as robust regression, proving reconstruction guarantees.

problem Learning non-linear functions from corrupted and dependent data.
method Sparse robust linear regression with 1\ell_1-optimization, incorporating unknown coefficients and corruptions.
result Reconstruction guarantees for 1\ell_1-optimization problem with dependent data, proving null and stable null space properties.

The paper develops concentration inequalities for structured random data, extending beyond independent terms.

problem Developing concentration inequalities for structured weighted sums of random data, including tensors and matrix-valued data.
method The paper develops Hoeffding and Bernstein bounds for structured weighted sums under exchangeability, extending beyond the classical framework of independent terms.
result The paper develops a sharper concentration bound for combinatorial sums of matrix arrays.

Paper presents a novel approach to train deep neural networks using geometric and topological methods.

problem Training deep neural networks efficiently and effectively.
method Uses topological coverings and linear matrix inequalities to define neural network architecture.
result Constructive algorithm trains deep neural networks in one shot with equal or superior accuracy.

The paper provides a finite-sample deviation bound for stable autoregressive processes.

problem Deviation bounds for least squares estimators in Gaussian AR(n) processes.
method Utilizes martingale concentration inequalities and tail-bound for χ² distributed variables.
result Problem-dependent finite-time bound on the deviation probability of AR(n) process parameters.

Study global solutions for Boussinesq systems on curved manifolds.

problem Global existence and uniqueness of solutions to Boussinesq systems on non-compact Riemannian manifolds with gravitational fields.
method Used dispersive and smoothing estimates of a vectorial matrix semigroup to establish global existence and uniqueness of mild solutions for linear systems. Then, applied fixed point arguments to semilinear systems. Proved exponential stability using Gronwall's inequality.
result Established global existence, uniqueness, and exponential stability of mild solutions to the Boussinesq systems on non-compact Riemannian manifolds with gravitational fields.

Study variational Bayes for high-dimensional linear regression with sparse priors.

problem Sparse high-dimensional linear regression model selection.
method Mean-field spike and slab variational Bayes approximation, oracle inequalities, coordinate-ascent variational inference (CAVI), prioritized updating scheme.
result Mean-field VB approximation converges to the sparse truth at optimal rate and gives optimal prediction.

Study on stochastic approximation with Polyak-Ruppert averaging for linear systems.

problem Understanding the asymptotic and non-asymptotic properties of stochastic approximation procedures.
method Detailed analysis of linear stochastic approximation with Polyak-Ruppert averaging, focusing on asymptotic and non-asymptotic properties.
result Proves CLT and non-asymptotic concentration inequality for averaged iterates, providing refined understanding of linear stochastic approximation.

Improved MMWU algorithm achieves instance-optimal regret bound for matrix LEA.

problem Matrix Learning from Expert Advice problem.
method Developed a general potential-based framework for matrix LEA, using a new Jensen's trace inequality.
result Achieved instance-optimal regret bound of O(TS(Xd1Id))O(\sqrt{T\cdot S(X||d^{-1}I_d)}).

Efficient model selection framework for online learning without parameter tuning.

problem Model selection in online learning without predefined parameters.
method Generic meta-algorithm framework for model selection in arbitrary Banach spaces under mild smoothness assumptions.
result First computationally efficient parameter-free algorithms in arbitrary Banach spaces.

In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (…

2015-01-07abs ↗pdf ↗