Search Results for author:
Found 33 papers, 18 papers with code
Forward-backward envelope for the sum of two nonconvex functions: Further properties and nonmonotone line-search algorithms
Extending previous results we show that, despite being nonsmooth for fully nonconvex problems, the FBE still enjoys favorable first- and second-order properties which are key for the convergence results of ZeroFPR.
Optimization and Control 90C06, 90C25, 90C26, 90C53, 49J52, 49J53
Douglas-Rachford splitting and ADMM for nonconvex optimization: Accelerated and Newton-type linesearch algorithms
Although the performance of popular optimization algorithms such as Douglas-Rachford splitting (DRS) and the ADMM is satisfactory in small and well-scaled problems, ill conditioning and problem size pose a severe obstacle to their reliable employment.
Optimization and Control 90C06, 90C25, 90C26, 49J52, 49J53
Forward-backward quasi-Newton methods for nonsmooth optimization problems
We propose an algorithmic scheme that enjoys the same global convergence properties of FBS when the problem is convex, or when the objective function possesses the Kurdyka-{\L}ojasiewicz property at its critical points.
Optimization and Control
QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs
The resulting implementation is shown to be extremely robust in numerical simulations, solving all of the Maros-Meszaros problems and finding a stationary point for most of the nonconvex QPs in the Cutest test set.
Optimization and Control 90C05, 90C20, 90C26, 49J53, 49M15
Block-coordinate and incremental aggregated proximal gradient methods for nonsmooth nonconvex problems
This paper analyzes block-coordinate proximal gradient methods for minimizing the sum of a separable smooth function and a (nonseparable) nonsmooth function, both of which are allowed to be nonconvex.
Optimization and Control 90C06, 90C25, 90C26, 49J52, 49J53
Adaptive proximal algorithms for convex optimization under local Lipschitz continuity of the gradient
Backtracking linesearch is the de facto approach for minimizing continuously differentiable functions with locally Lipschitz gradient.
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine
Detecting out-of-distribution (OOD) samples is an essential requirement for the deployment of machine learning systems in the real world.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Tensor-based Multi-view Spectral Clustering via Shared Latent Space
In our method, the dual variables, playing the role of hidden features, are shared by all views to construct a common latent space, coupling the views by learning projections from view-specific spaces.
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems.
Deep Kernel Principal Component Analysis for Multi-level Feature Learning
Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction.
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
The goal of this paper is to revisit Kernel Principal Component Analysis (KPCA) through dualization of a difference of convex functions.
On the convergence of adaptive first order methods: proximal gradient and alternating minimization algorithms
Building upon recent works on linesearch-free adaptive proximal gradient methods, this paper proposes AdaPG$^{\pi, r}$, a framework that unifies and extends existing results by providing larger stepsize policies and improved lower bounds.
Multi-block Bregman proximal alternating linearized minimization and its application to orthogonal nonnegative matrix factorization
We introduce and analyze BPALM and A-BPALM, two multi-block proximal alternating linearized minimization algorithms using Bregman distances for solving structured nonconvex problems.
Optimization and Control Numerical Analysis Numerical Analysis
Inertial Block Proximal Methods for Non-Convex Non-Smooth Optimization
We propose inertial versions of block coordinate descent methods for solving non-convex non-smooth composite optimization problems.
Bregman forward-backward splitting for nonconvex composite optimization: superlinear convergence to nonisolated critical points
We introduce Bella, a locally superlinearly convergent Bregman forward-backward splitting method for minimizing the sum of two nonconvex functions, one of which satisfying a relative smoothness condition and the other one possibly nonsmooth.
Optimization and Control 90C06, 90C25, 90C26, 49J52, 49J53
SuperMann: a superlinearly convergent algorithm for finding fixed points of nonexpansive operators
As a result, SuperMann enhances and robustifies all operator splitting schemes for structured convex optimization, overcoming their well known sensitivity to ill conditioning.
Optimization and Control 47H09, 90C25, 90C53, 65K15
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations.
Data-driven distributionally robust MPC for constrained stochastic systems
In this paper we introduce a novel approach to distributionally robust optimal control that supports online learning of the ambiguity set, while guaranteeing recursive feasibility.
Lasry-Lions Envelopes and Nonconvex Optimization: A Homotopy Approach
Lasry-Lions envelopes can also be seen as an "intermediate" between a given function and its convex envelope, and we make use of this property to develop a method that builds a sequence of approximate subproblems that are easier to solve than the original problem.
Block Alternating Bregman Majorization Minimization with Extrapolation
In this paper, we consider a class of nonsmooth nonconvex optimization problems whose objective is the sum of a block relative smooth function and a proper and lower semicontinuous block separable function.
Safe, Learning-Based MPC for Highway Driving under Lane-Change Uncertainty: A Distributionally Robust Approach
We present a case study applying learning-based distributionally robust model predictive control to highway motion planning under stochastic uncertainty of the lane change behavior of surrounding road users.
SPIRAL: A superlinearly convergent incremental proximal algorithm for nonconvex finite sum minimization
We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption.
Sim2real for Autonomous Vehicle Control using Executable Digital Twin
In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT).
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs.
Solving stochastic weak Minty variational inequalities without increasing batch size
This paper introduces a family of stochastic extragradient-type algorithms for a class of nonconvex-nonconcave problems characterized by the weak Minty variational inequality (MVI).
Distributionally Robust Optimization using Cost-Aware Ambiguity Sets
We present a novel framework for distributionally robust optimization (DRO), called cost-aware DRO (CADRO).
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method
We describe a nonlinear extension of the matrix Singular Value Decomposition through asymmetric kernels, namely KSVD.
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers
In the context of deep learning with kernel machines, the deep Restricted Kernel Machine (DRKM) framework allows multiple levels of kernel PCA (KPCA) and Least-Squares Support Vector Machines (LSSVM) to be combined into a deep architecture using visible and hidden units.
Zeroth-order Asynchronous Learning with Bounded Delays with a Use-case in Resource Allocation in Communication Networks
Distributed optimization has experienced a significant surge in interest due to its wide-ranging applications in distributed learning and adaptation.
A Deep Learning Based Resource Allocator for Communication Systems with Dynamic User Utility Demands
The optimization algorithm aims to optimize the on-off status of users in a time-sharing problem to satisfy their utility demands in expectation.
Fast data-driven iterative learning control for linear system with output disturbance
This paper studies data-driven iterative learning control (ILC) for linear time-invariant (LTI) systems with unknown dynamics, output disturbances and input box-constraints.
Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation
The autonomous driving industry is continuously dealing with more safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations.
Adaptive proximal gradient methods are universal without approximation
We show that adaptive proximal gradient methods for convex problems are not restricted to traditional Lipschitzian assumptions.
