51 citations · 113 across the 8 of their papers we have counts for
9 papers
A template for gradient norm minimization
Mihai I. Florea
The gradient mapping norm is a strong and easily verifiable stopping criterion for first-order methods on composite problems. When the objective exhibits the quadratic growth prope…
Adaptive first-order methods with enhanced worst-case rates
Mihai I. Florea
The Optimized Gradient Method (OGM), its strongly convex extension, the Information Theoretical Exact Method (ITEM), as well as the related Triple Momentum Method (TMM) have superi…
An optimal lower bound for smooth convex functions
Mihai I. Florea, Yurii Nesterov
First order methods endowed with global convergence guarantees operate using global lower bounds on the objective. The tightening of the bounds has been shown to increase both the…
Gradient Methods with Memory for Minimizing Composite Functions
Mihai I. Florea
The recently introduced Gradient Methods with Memory use a subset of the past oracle information to create an accurate model of the objective function that enables them to surpass…
Gradient Methods with Memory
Yurii Nesterov, Mihai I. Florea
In this paper, we consider gradient methods for minimizing smooth convex functions, which employ the information obtained at the previous iterations in order to accelerate the conv…
An axially-variant kernel imaging model applied to ultrasound image reconstruction
Mihai I. Florea, Adrian Basarab, Denis Kouamé +1
Existing ultrasound deconvolution approaches unrealistically assume, primarily for computational reasons, that the convolution model relies on a spatially invariant kernel and circ…
A Generalized Accelerated Composite Gradient Method: Uniting Nesterov's Fast Gradient Method and FISTA
Mihai I. Florea, Sergiy A. Vorobyov
The most popular first-order accelerated black-box methods for solving large-scale convex optimization problems are the Fast Gradient Method (FGM) and the Fast Iterative Shrinkage…
An Accelerated Composite Gradient Method for Large-scale Composite Objective Problems
Mihai I. Florea, Sergiy A. Vorobyov
We introduce a framework, which we denote as the augmented estimate sequence, for deriving fast algorithms with provable convergence guarantees. We use this framework to construct…
A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation
Vlad Olaru, Mihai Florea, Cristian Sminchisescu
This paper presents a framework that supports the implementation of parallel solutions for the widespread parametric maximum flow computational routines used in image segmentation…