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Found 17 papers, 5 papers with code
Monte Carlo Policy Gradient Method for Binary Optimization
Binary optimization has a wide range of applications in combinatorial optimization problems such as MaxCut, MIMO detection, and MaxSAT.
Convergence Analysis of Stochastic Gradient Descent with MCMC Estimators
Understanding stochastic gradient descent (SGD) and its variants is essential for machine learning.
Gauss-Newton Temporal Difference Learning with Nonlinear Function Approximation
Under mild conditions, non-asymptotic finite-sample convergence to the globally optimal Q function is derived for various nonlinear function approximations.
Riemannian Natural Gradient Methods
Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence.
A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP
As an important framework for safe Reinforcement Learning, the Constrained Markov Decision Process (CMDP) has been extensively studied in the recent literature.
NG+ : A Multi-Step Matrix-Product Natural Gradient Method for Deep Learning
In this paper, a novel second-order method called NG+ is proposed.
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning
A general theoretical analysis shows that the solutions generated from a sequence of the constrained optimizations converge to the optimal solution of the LP if the error is controlled properly.
Joint Bandwidth Allocation and Path Selection in WANs with Path Cardinality Constraints
Almost all existing works deal with such a problem using relaxation techniques to transform it to be a convex optimization problem.
Enhance Curvature Information by Structured Stochastic Quasi-Newton Methods
In this paper, we consider stochastic second-order methods for minimizing a finite summation of nonconvex functions.
Sketchy Empirical Natural Gradient Methods for Deep Learning
Experiments on the distributed large-batch training show that the scaling efficiency is quite reasonable.
A Stochastic Extra-Step Quasi-Newton Method for Nonsmooth Nonconvex Optimization
In this paper, a novel stochastic extra-step quasi-Newton method is developed to solve a class of nonsmooth nonconvex composite optimization problems.
Adaptive Low-Nonnegative-Rank Approximation for State Aggregation of Markov Chains
The efficiency and statistical properties of our approach are illustrated on synthetic data.
Structured Quasi-Newton Methods for Optimization with Orthogonality Constraints
In particular, we are interested in applications that the Euclidean Hessian itself consists of a computational cheap part and a significantly expensive part.
Optimization and Control
A Stochastic Semismooth Newton Method for Nonsmooth Nonconvex Optimization
In this work, we present a globalized stochastic semismooth Newton method for solving stochastic optimization problems involving smooth nonconvex and nonsmooth convex terms in the objective function.
Adaptive Regularized Newton Method for Riemannian Optimization
Optimization on Riemannian manifolds widely arises in eigenvalue computation, density functional theory, Bose-Einstein condensates, low rank nearest correlation, image registration, and signal processing, etc.
Optimization and Control
Orientation Determination from Cryo-EM images Using Least Unsquared Deviation
An approximation to the least squares global self consistency error was obtained using convex relaxation by semidefinite programming.
An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors
By taking the advantages of both nonnegativity and low-rankness, one can generally obtain superior results than those of just using one of the two properties.
Information Theory Numerical Analysis Information Theory Numerical Analysis
