Mathematical Programming, ISSN 0025-5610, 6/2015, Volume 151, Issue 1, pp. 3 - 34

Coordinate descent algorithms solve optimization problems by successively performing approximate minimization along coordinate directions or coordinate...

Parallel numerical computing | 49M20 | Mathematical Methods in Physics | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | 90C25 | Numerical Analysis | Theoretical, Mathematical and Computational Physics | Randomized algorithms | Mathematics | Combinatorics | Coordinate descent | REGRESSION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | SHRINKAGE | CONVERGENCE | OPTIMIZATION | Analysis | Algorithms | Machine learning | Studies | Optimization algorithms

Parallel numerical computing | 49M20 | Mathematical Methods in Physics | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | 90C25 | Numerical Analysis | Theoretical, Mathematical and Computational Physics | Randomized algorithms | Mathematics | Combinatorics | Coordinate descent | REGRESSION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | SHRINKAGE | CONVERGENCE | OPTIMIZATION | Analysis | Algorithms | Machine learning | Studies | Optimization algorithms

Journal Article

Mathematical Programming, ISSN 0025-5610, 2/2013, Volume 137, Issue 1, pp. 91 - 129

In view of the minimization of a nonsmooth nonconvex function f, we prove an abstract convergence result for descent methods satisfying a sufficient-decrease...

Tame optimization | 65K15 | Theoretical, Mathematical and Computational Physics | Alternating minimization | Mathematics | Forward–backward splitting | Descent methods | 90C53 | Mathematical Methods in Physics | Iterative thresholding | Calculus of Variations and Optimal Control; Optimization | Proximal algorithms | Sufficient decrease | Combinatorics | 47J25 | Kurdyka–Łojasiewicz inequality | o-minimal structures | Nonconvex nonsmooth optimization | 34G25 | Semi-algebraic optimization | 47J30 | Mathematics of Computing | 90C25 | Numerical Analysis | Block-coordinate methods | Relative error | 49M15 | 49M37 | 47J35 | Kurdyka-Łojasiewicz inequality | Forward-backward splitting | MATHEMATICS, APPLIED | Kurdyka-Lojasiewicz inequality | GRADIENT-LIKE SYSTEMS | EVOLUTION-EQUATIONS | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | SETS | OPTIMIZATION | PROJECTIONS | POINT ALGORITHM | Methods | Algorithms | Studies | Data smoothing | Analysis | Optimization | Mathematical programming | Splitting | Gauss-Seidel method | Mathematical analysis | Minimization | Descent | Convergence

Tame optimization | 65K15 | Theoretical, Mathematical and Computational Physics | Alternating minimization | Mathematics | Forward–backward splitting | Descent methods | 90C53 | Mathematical Methods in Physics | Iterative thresholding | Calculus of Variations and Optimal Control; Optimization | Proximal algorithms | Sufficient decrease | Combinatorics | 47J25 | Kurdyka–Łojasiewicz inequality | o-minimal structures | Nonconvex nonsmooth optimization | 34G25 | Semi-algebraic optimization | 47J30 | Mathematics of Computing | 90C25 | Numerical Analysis | Block-coordinate methods | Relative error | 49M15 | 49M37 | 47J35 | Kurdyka-Łojasiewicz inequality | Forward-backward splitting | MATHEMATICS, APPLIED | Kurdyka-Lojasiewicz inequality | GRADIENT-LIKE SYSTEMS | EVOLUTION-EQUATIONS | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | SETS | OPTIMIZATION | PROJECTIONS | POINT ALGORITHM | Methods | Algorithms | Studies | Data smoothing | Analysis | Optimization | Mathematical programming | Splitting | Gauss-Seidel method | Mathematical analysis | Minimization | Descent | Convergence

Journal Article

Journal of the American Statistical Association, ISSN 0162-1459, 09/2011, Volume 106, Issue 495, pp. 1125 - 1138

We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with...

Degrees of freedom | Sparse regression | Nonconvex optimization | Regularization surface | LASSO | Variable selection | Error rates | Theory and Methods | Threshing | Linear regression | Variable coefficients | Coordinate systems | Calibration | Mathematical minima | Modeling | Estimators | REGRESSION | SPARSITY | STATISTICS & PROBABILITY | ORACLE PROPERTIES | NONCONCAVE PENALIZED LIKELIHOOD | MINIMIZATION | SHRINKAGE | MODEL SELECTION | Usage | Algorithms | Analysis | Linear models (Statistics) | Models | Mathematical optimization | Linear regression models | Methods

Degrees of freedom | Sparse regression | Nonconvex optimization | Regularization surface | LASSO | Variable selection | Error rates | Theory and Methods | Threshing | Linear regression | Variable coefficients | Coordinate systems | Calibration | Mathematical minima | Modeling | Estimators | REGRESSION | SPARSITY | STATISTICS & PROBABILITY | ORACLE PROPERTIES | NONCONCAVE PENALIZED LIKELIHOOD | MINIMIZATION | SHRINKAGE | MODEL SELECTION | Usage | Algorithms | Analysis | Linear models (Statistics) | Models | Mathematical optimization | Linear regression models | Methods

Journal Article

Journal of Statistical Software, ISSN 1548-7660, 2010, Volume 33, Issue 1, pp. 1 - 22

We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic...

Logistic regression | Elastic net | Coordinate-descent | penalty | Lasso | Regularization path | lasso | l penalty | CLASSIFICATION | STATISTICS & PROBABILITY | ALGORITHMS | VARIABLE SELECTION | CANCER | coordinate-descent | regularization path | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PENALIZED LOGISTIC-REGRESSION | elastic net | logistic regression | ℓ1 penalty

Logistic regression | Elastic net | Coordinate-descent | penalty | Lasso | Regularization path | lasso | l penalty | CLASSIFICATION | STATISTICS & PROBABILITY | ALGORITHMS | VARIABLE SELECTION | CANCER | coordinate-descent | regularization path | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PENALIZED LOGISTIC-REGRESSION | elastic net | logistic regression | ℓ1 penalty

Journal Article

SIAM Journal on Optimization, ISSN 1052-6234, 2015, Volume 25, Issue 4, pp. 1997 - 2023

We propose a new randomized coordinate descent method for minimizing the sum of convex functions each of which depends on a small number of coordinates only....

Partial separability | Randomized coordinate descent | Convex optimization | Proximal methods | Parallel methods | Big data | Acceleration | Complexity | acceleration | complexity | MATHEMATICS, APPLIED | OPTIMIZATION PROBLEMS | parallel methods | MINIMIZATION | ALGORITHM | randomized coordinate descent | proximal methods | convex optimization | big data | partial separability

Partial separability | Randomized coordinate descent | Convex optimization | Proximal methods | Parallel methods | Big data | Acceleration | Complexity | acceleration | complexity | MATHEMATICS, APPLIED | OPTIMIZATION PROBLEMS | parallel methods | MINIMIZATION | ALGORITHM | randomized coordinate descent | proximal methods | convex optimization | big data | partial separability

Journal Article

IEEE Transactions on Signal Processing, ISSN 1053-587X, 03/2014, Volume 62, Issue 6, pp. 1464 - 1475

Recently, there has been a lot of focus on penalized least squares problems for noisy sparse signal estimation. The penalty induces sparsity and a very common...

inverse problem | l_q optimization | Inverse problems | non-convex | Sparsity | Convex optimization | IMAGE-RESTORATION | RECONSTRUCTION | SIGNAL RECOVERY | ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | MINIMIZATION | l(q) optimization | COORDINATE DESCENT | INVERSE PROBLEMS | CONVERGENCE | OPTIMIZATION | REGULARIZATION | Signal processing | Usage | Regression analysis | Mathematical optimization | Least squares | Innovations

inverse problem | l_q optimization | Inverse problems | non-convex | Sparsity | Convex optimization | IMAGE-RESTORATION | RECONSTRUCTION | SIGNAL RECOVERY | ALGORITHM | ENGINEERING, ELECTRICAL & ELECTRONIC | MINIMIZATION | l(q) optimization | COORDINATE DESCENT | INVERSE PROBLEMS | CONVERGENCE | OPTIMIZATION | REGULARIZATION | Signal processing | Usage | Regression analysis | Mathematical optimization | Least squares | Innovations

Journal Article

Journal of Machine Learning Research, ISSN 1532-4435, 02/2015, Volume 16, pp. 285 - 322

We describe an asynchronous parallel stochastic coordinate descent algorithm for minimizing smooth unconstrained or separably constrained functions. The method...

Stochastic coordinate descent | Asynchronous parallel optimization | CONVERGENCE | stochastic coordinate descent | SHRINKAGE | asynchronous parallel optimization | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Stochastic coordinate descent | Asynchronous parallel optimization | CONVERGENCE | stochastic coordinate descent | SHRINKAGE | asynchronous parallel optimization | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Journal Article

SIAM Journal on Optimization, ISSN 1052-6234, 2012, Volume 22, Issue 2, pp. 341 - 362

In this paper we propose new methods for solving huge-scale optimization problems. For problems of this size, even the simplest full-dimensional vector...

Google problem | Fast gradient schemes | Convex optimization | Coordinate relaxation | Worst-case efficiency estimates | MATHEMATICS, APPLIED | worst-case efficiency estimates | MINIMIZATION | fast gradient schemes | convex optimization | CONVERGENCE | coordinate relaxation

Google problem | Fast gradient schemes | Convex optimization | Coordinate relaxation | Worst-case efficiency estimates | MATHEMATICS, APPLIED | worst-case efficiency estimates | MINIMIZATION | fast gradient schemes | convex optimization | CONVERGENCE | coordinate relaxation

Journal Article

Optimization Methods and Software, ISSN 1055-6788, 09/2017, Volume 32, Issue 5, pp. 993 - 1005

We propose a novel stochastic gradient method-semi-stochastic coordinate descent-for the problem of minimizing a strongly convex function represented as the...

90C06 | coordinate descent | 90C15 | empirical risk minimization | Stochastic gradient | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | Randomness | Computation | Probability theory | Descent

90C06 | coordinate descent | 90C15 | empirical risk minimization | Stochastic gradient | COMPUTER SCIENCE, SOFTWARE ENGINEERING | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | Randomness | Computation | Probability theory | Descent

Journal Article

Mathematical Programming, ISSN 0025-5610, 3/2016, Volume 156, Issue 1, pp. 433 - 484

In this work we show that randomized (block) coordinate descent methods can be accelerated by parallelization when applied to the problem of minimizing the sum...

68W20 | 68W40 | 65K05 | Theoretical, Mathematical and Computational Physics | 68W10 | 90C06 | Mathematics | Big data optimization | Parallel coordinate descent | Iteration complexity | 49M20 | Mathematical Methods in Physics | Partial separability | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | Convex optimization | 90C25 | Numerical Analysis | Expected separable over-approximation | Huge-scale optimization | LASSO | 49M27 | Combinatorics | Composite objective | MATHEMATICS, APPLIED | ALGORITHM | CONVEX-OPTIMIZATION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | CONVERGENCE | CONSTRAINTS | Big data | Algorithms | Multiprocessing | Analysis | Methods | Studies | Optimization techniques | Computer programming | Mathematical analysis | Blocking | Serials | Mathematical models | Iterative methods | Processors | Descent | Optimization

68W20 | 68W40 | 65K05 | Theoretical, Mathematical and Computational Physics | 68W10 | 90C06 | Mathematics | Big data optimization | Parallel coordinate descent | Iteration complexity | 49M20 | Mathematical Methods in Physics | Partial separability | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | Convex optimization | 90C25 | Numerical Analysis | Expected separable over-approximation | Huge-scale optimization | LASSO | 49M27 | Combinatorics | Composite objective | MATHEMATICS, APPLIED | ALGORITHM | CONVEX-OPTIMIZATION | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | CONVERGENCE | CONSTRAINTS | Big data | Algorithms | Multiprocessing | Analysis | Methods | Studies | Optimization techniques | Computer programming | Mathematical analysis | Blocking | Serials | Mathematical models | Iterative methods | Processors | Descent | Optimization

Journal Article

Mathematical Programming, ISSN 0025-5610, 3/2009, Volume 117, Issue 1, pp. 387 - 423

We consider the problem of minimizing the sum of a smooth function and a separable convex function. This problem includes as special cases bound-constrained...

Global convergence | 65K05 | Mathematical and Computational Physics | 90C06 | Error bound | Mathematics | 90C26 | Coordinate descent | Linear convergence rate | Mathematical Methods in Physics | 90C30 | Mathematics of Computing | Calculus of Variations and Optimal Control; Optimization | 90C25 | Numerical Analysis | 90C55 | Nonsmooth optimization | 49M27 | 49M37 | Combinatorics | REGRESSION | MATHEMATICS, APPLIED | error bound | CONTINUOUSLY DIFFERENTIABLE FUNCTION | linear convergence rate | ASCENT METHODS | global convergence | ALGORITHM | coordinate descent | nonsmooth optimization | LINEAR CONVERGENCE | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | NONDIFFERENTIABLE OPTIMIZATION PROBLEMS | CONSTRAINTS | UNCONSTRAINED OPTIMIZATION | WAVELET SHRINKAGE | CONVEX MINIMIZATION | Studies | Optimization | Mathematical programming

Global convergence | 65K05 | Mathematical and Computational Physics | 90C06 | Error bound | Mathematics | 90C26 | Coordinate descent | Linear convergence rate | Mathematical Methods in Physics | 90C30 | Mathematics of Computing | Calculus of Variations and Optimal Control; Optimization | 90C25 | Numerical Analysis | 90C55 | Nonsmooth optimization | 49M27 | 49M37 | Combinatorics | REGRESSION | MATHEMATICS, APPLIED | error bound | CONTINUOUSLY DIFFERENTIABLE FUNCTION | linear convergence rate | ASCENT METHODS | global convergence | ALGORITHM | coordinate descent | nonsmooth optimization | LINEAR CONVERGENCE | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | NONDIFFERENTIABLE OPTIMIZATION PROBLEMS | CONSTRAINTS | UNCONSTRAINED OPTIMIZATION | WAVELET SHRINKAGE | CONVEX MINIMIZATION | Studies | Optimization | Mathematical programming

Journal Article

The Annals of Applied Statistics, ISSN 1932-6157, 3/2008, Volume 2, Issue 1, pp. 224 - 244

Imposition of a lasso penalty shrinks parameter estimates toward zero and performs continuous model selection. Lasso penalized regression is capable of...

Datasets | Error rates | Algorithms | Objective functions | Linear regression | Directional derivatives | Coordinate systems | Mathematical independent variables | Mathematical minima | Modeling | Edgeworth's algorithm | Cyclic | Model selection | Greedy | Consistency | Convergence | APPROXIMATION | convergence | greedy | STATISTICS & PROBABILITY | consistency | EXPRESSION | cyclic | Statistics - Applications | Edgeworth’s algorithm

Datasets | Error rates | Algorithms | Objective functions | Linear regression | Directional derivatives | Coordinate systems | Mathematical independent variables | Mathematical minima | Modeling | Edgeworth's algorithm | Cyclic | Model selection | Greedy | Consistency | Convergence | APPROXIMATION | convergence | greedy | STATISTICS & PROBABILITY | consistency | EXPRESSION | cyclic | Statistics - Applications | Edgeworth’s algorithm

Journal Article

Mathematical Programming, ISSN 0025-5610, 4/2014, Volume 144, Issue 1, pp. 1 - 38

In this paper we develop a randomized block-coordinate descent method for minimizing the sum of a smooth and a simple nonsmooth block-separable convex function...

65K05 | Theoretical, Mathematical and Computational Physics | Block coordinate descent | 90C06 | Mathematics | Sparse regression | 90C05 | Iteration complexity | Gradient descent | Mathematical Methods in Physics | Gauss–Seidel method | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | Composite minimization | Convex optimization | Coordinate relaxation | 90C25 | Numerical Analysis | Huge-scale optimization | LASSO | Combinatorics | Gauss-Seidel method | REGRESSION | MATHEMATICS, APPLIED | ALGORITHM | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | CONVERGENCE | OPTIMIZATION | SELECTION | Methods | Algorithms | Studies | Regression analysis | Optimization | Mathematical programming | Least squares method | Mathematical analysis | Blocking | Texts | Mathematical models | Iterative methods | Descent | Complexity

65K05 | Theoretical, Mathematical and Computational Physics | Block coordinate descent | 90C06 | Mathematics | Sparse regression | 90C05 | Iteration complexity | Gradient descent | Mathematical Methods in Physics | Gauss–Seidel method | Calculus of Variations and Optimal Control; Optimization | Mathematics of Computing | Composite minimization | Convex optimization | Coordinate relaxation | 90C25 | Numerical Analysis | Huge-scale optimization | LASSO | Combinatorics | Gauss-Seidel method | REGRESSION | MATHEMATICS, APPLIED | ALGORITHM | COMPUTER SCIENCE, SOFTWARE ENGINEERING | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | CONVERGENCE | OPTIMIZATION | SELECTION | Methods | Algorithms | Studies | Regression analysis | Optimization | Mathematical programming | Least squares method | Mathematical analysis | Blocking | Texts | Mathematical models | Iterative methods | Descent | Complexity

Journal Article

BMC Bioinformatics, ISSN 1471-2105, 04/2016, Volume 17, Issue 1, p. 158

Background: Existing feature selection methods typically do not consider prior knowledge in the form of structural relationships among features. In this study,...

Prior knowledge | Block coordinate gradient descent | Structured feature selection | Gene expression | Microarray analysis | ALGORITHM | BIOCHEMICAL RESEARCH METHODS | PATHS | DISCOVERY | GENE-EXPRESSION DATA | CANCER CLASSIFICATION | BIOTECHNOLOGY & APPLIED MICROBIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | Melanoma - diagnosis | Hemoglobinuria - genetics | Humans | Databases, Genetic | Corneal Neovascularization - genetics | Neuroendocrine Tumors - diagnosis | Genetic Variation | Nevus - genetics | Melanoma - genetics | Microarray Analysis | HIV Infections - diagnosis | Virus Diseases - genetics | Gene Ontology | Models, Theoretical | Nevus - diagnosis | Multiple Myeloma - diagnosis | Stress, Physiological - genetics | HIV Infections - genetics | Gene Expression Regulation | Neuroendocrine Tumors - genetics | Algorithms | Corneal Neovascularization - diagnosis | Virus Diseases - diagnosis | Hemoglobinuria - diagnosis | Multiple Myeloma - genetics | Usage | DNA microarrays | Computer-generated environments | Computer simulation | Analysis

Prior knowledge | Block coordinate gradient descent | Structured feature selection | Gene expression | Microarray analysis | ALGORITHM | BIOCHEMICAL RESEARCH METHODS | PATHS | DISCOVERY | GENE-EXPRESSION DATA | CANCER CLASSIFICATION | BIOTECHNOLOGY & APPLIED MICROBIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | Melanoma - diagnosis | Hemoglobinuria - genetics | Humans | Databases, Genetic | Corneal Neovascularization - genetics | Neuroendocrine Tumors - diagnosis | Genetic Variation | Nevus - genetics | Melanoma - genetics | Microarray Analysis | HIV Infections - diagnosis | Virus Diseases - genetics | Gene Ontology | Models, Theoretical | Nevus - diagnosis | Multiple Myeloma - diagnosis | Stress, Physiological - genetics | HIV Infections - genetics | Gene Expression Regulation | Neuroendocrine Tumors - genetics | Algorithms | Corneal Neovascularization - diagnosis | Virus Diseases - diagnosis | Hemoglobinuria - diagnosis | Multiple Myeloma - genetics | Usage | DNA microarrays | Computer-generated environments | Computer simulation | Analysis

Journal Article

SIAM Journal on Optimization, ISSN 1052-6234, 2015, Volume 25, Issue 1, pp. 351 - 376

We describe an asynchronous parallel stochastic proximal coordinate descent algorithm for minimizing a composite objective function, which consists of a smooth...

Stochastic coordinate descent | Asynchronous parallelism | Composite objective | Inconsistent read | MATHEMATICS, APPLIED | composite objective | inconsistent read | stochastic coordinate descent | asynchronous parallelism | Microprocessors | Mathematical analysis | Mathematical models | Stochasticity | Vectors (mathematics) | Processors | Descent | Optimization | Convergence

Stochastic coordinate descent | Asynchronous parallelism | Composite objective | Inconsistent read | MATHEMATICS, APPLIED | composite objective | inconsistent read | stochastic coordinate descent | asynchronous parallelism | Microprocessors | Mathematical analysis | Mathematical models | Stochasticity | Vectors (mathematics) | Processors | Descent | Optimization | Convergence

Journal Article

Computational Optimization and Applications, ISSN 0926-6003, 06/2018, Volume 70, Issue 2, pp. 351 - 394

We present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate)...

Iteration complexity | Large scale optimization | Curvature information | Randomized | Nonsmooth problems | Block coordinate descent | Second-order methods | MATHEMATICS, APPLIED | OPTIMIZATION PROBLEMS | ALGORITHMS | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | COMPLEXITY | LASSO | QUASI-NEWTON METHOD | L-REGULARIZED LEAST-SQUARES | PARALLEL | Computer science | Algorithms | Analysis | Methods | Mathematical models | Conditioning | Robustness (mathematics) | Iterative methods | Curvature | Descent

Iteration complexity | Large scale optimization | Curvature information | Randomized | Nonsmooth problems | Block coordinate descent | Second-order methods | MATHEMATICS, APPLIED | OPTIMIZATION PROBLEMS | ALGORITHMS | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | COMPLEXITY | LASSO | QUASI-NEWTON METHOD | L-REGULARIZED LEAST-SQUARES | PARALLEL | Computer science | Algorithms | Analysis | Methods | Mathematical models | Conditioning | Robustness (mathematics) | Iterative methods | Curvature | Descent

Journal Article