Optimization letters, ISSN 1862-4480, 2016, Volume 12, Issue 1, pp. 87 - 102

In this article, we first introduce a modified inertial Mann algorithm and an inertial CQ-algorithm by combining the accelerated Mann algorithm and the CQ-algorithm with the inertial extrapolation, respectively...

Mann algorithm | Computational Intelligence | The accelerated Mann algorithm | CQ-algorithm | Operations Research/Decision Theory | Mathematics | Numerical and Computational Physics, Simulation | Optimization | Nonexpansive mapping | Inertial extrapolation | The inertial Mann algorithm | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CONVERGENCE THEOREMS | MAXIMAL MONOTONE-OPERATORS | PROXIMAL METHOD | FIXED-POINTS | Signal processing | Yuan (China) | Medical colleges | Algorithms | Resveratrol

Mann algorithm | Computational Intelligence | The accelerated Mann algorithm | CQ-algorithm | Operations Research/Decision Theory | Mathematics | Numerical and Computational Physics, Simulation | Optimization | Nonexpansive mapping | Inertial extrapolation | The inertial Mann algorithm | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CONVERGENCE THEOREMS | MAXIMAL MONOTONE-OPERATORS | PROXIMAL METHOD | FIXED-POINTS | Signal processing | Yuan (China) | Medical colleges | Algorithms | Resveratrol

Journal Article

Journal of global optimization, ISSN 1573-2916, 2018, Volume 72, Issue 3, pp. 553 - 577

The proximal point algorithm is a widely used tool for solving a variety of convex optimization problems such as finding zeros of maximally monotone operators, fixed points of nonexpansive mappings...

Jointly firmly nonexpansive families | Uniformly firmly nonexpansive mappings | Mathematics | Proximal point algorithm | Optimization | CAT spaces | Convex optimization | Rates of convergence | 90C25 | Operations Research/Decision Theory | Proof mining | 47H09 | Computer Science, general | 47J25 | 46N10 | 03F10 | Real Functions | MATHEMATICS, APPLIED | HARMONIC MAPS | METRIC-SPACES | GEODESIC SPACES | ASYMPTOTIC-BEHAVIOR | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | VECTOR OPTIMIZATION | FUNCTIONAL-ANALYSIS | FIRMLY NONEXPANSIVE-MAPPINGS | LOGICAL METATHEOREMS | FIXED-POINTS | MONOTONE-OPERATORS | Computer science | Mineral industry | Algorithms | Numerical analysis | Mining industry | Analysis | Computational geometry | Fixed points (mathematics) | Hilbert space | Convexity | Data mining | Convex analysis | Convergence

Jointly firmly nonexpansive families | Uniformly firmly nonexpansive mappings | Mathematics | Proximal point algorithm | Optimization | CAT spaces | Convex optimization | Rates of convergence | 90C25 | Operations Research/Decision Theory | Proof mining | 47H09 | Computer Science, general | 47J25 | 46N10 | 03F10 | Real Functions | MATHEMATICS, APPLIED | HARMONIC MAPS | METRIC-SPACES | GEODESIC SPACES | ASYMPTOTIC-BEHAVIOR | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | VECTOR OPTIMIZATION | FUNCTIONAL-ANALYSIS | FIRMLY NONEXPANSIVE-MAPPINGS | LOGICAL METATHEOREMS | FIXED-POINTS | MONOTONE-OPERATORS | Computer science | Mineral industry | Algorithms | Numerical analysis | Mining industry | Analysis | Computational geometry | Fixed points (mathematics) | Hilbert space | Convexity | Data mining | Convex analysis | Convergence

Journal Article

3.
Full Text
A variable Krasnosel'skii–Mann algorithm and the multiple-set split feasibility problem

Inverse problems, ISSN 1361-6420, 2006, Volume 22, Issue 6, pp. 2021 - 2034

A variable Krasnosel'skii-Mann algorithm generates a sequence {x(n)} via the formula x(n+1) = (1 - alpha(n))x(n) + alpha(n)T(n)x(n), where {alpha(n)} is a sequence in [0, 1] and {T-n} is a sequence of nonexpansive mappings...

MATHEMATICS, APPLIED | NONEXPANSIVE-MAPPINGS | BANACH-SPACES | THEOREMS | ITERATIVE ALGORITHMS | CQ ALGORITHM | PHYSICS, MATHEMATICAL | OPERATORS | FIXED-POINTS

MATHEMATICS, APPLIED | NONEXPANSIVE-MAPPINGS | BANACH-SPACES | THEOREMS | ITERATIVE ALGORITHMS | CQ ALGORITHM | PHYSICS, MATHEMATICAL | OPERATORS | FIXED-POINTS

Journal Article

Journal of optimization theory and applications, ISSN 1573-2878, 2018, Volume 176, Issue 3, pp. 605 - 624

.... In this paper, we explore the behaviour of the algorithm when the inclusion problem has no solution...

Normal problem | 65K05 | Primary 47H09 | Mathematics | Theory of Computation | Optimization | Calculus of Variations and Optimal Control; Optimization | Operations Research/Decision Theory | Firmly nonexpansive mapping | Forward–backward splitting operator | 49M29 | 65K10 | 49M27 | 49N15 | Applications of Mathematics | Engineering, general | Secondary 47H05 | 47H14 | Attouch–Théra duality | Fixed point | MATHEMATICS, APPLIED | MAXIMALLY MONOTONE-OPERATORS | INCLUSIONS | Attouch-Thera duality | SIGNAL RECOVERY | DECOMPOSITION | SUM | PARAMONOTONICITY | SPACE | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CONVERGENCE | MAPPINGS | DUALITY | Forward-backward splitting operator | Electrical engineering | Algorithms

Normal problem | 65K05 | Primary 47H09 | Mathematics | Theory of Computation | Optimization | Calculus of Variations and Optimal Control; Optimization | Operations Research/Decision Theory | Firmly nonexpansive mapping | Forward–backward splitting operator | 49M29 | 65K10 | 49M27 | 49N15 | Applications of Mathematics | Engineering, general | Secondary 47H05 | 47H14 | Attouch–Théra duality | Fixed point | MATHEMATICS, APPLIED | MAXIMALLY MONOTONE-OPERATORS | INCLUSIONS | Attouch-Thera duality | SIGNAL RECOVERY | DECOMPOSITION | SUM | PARAMONOTONICITY | SPACE | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | CONVERGENCE | MAPPINGS | DUALITY | Forward-backward splitting operator | Electrical engineering | Algorithms

Journal Article

Computational Optimization and Applications, ISSN 0926-6003, 12/2018, Volume 71, Issue 3, pp. 767 - 794

.... The resulting algorithm with memory inherits strong convergence properties of the original best approximation proximal primal–dual algorithm...

Proximal algorithm with memory | Inclusions with maximally monotone operators | Best approximation of the Kuhn–Tucker set | Operations Research/Decision Theory | Convex and Discrete Geometry | Mathematics | Operations Research, Management Science | Statistics, general | Attraction property | Optimization | Primal–dual algorithm | Image reconstruction | MATHEMATICS, APPLIED | Primal-dual algorithm | MAXIMAL MONOTONE-OPERATORS | INCLUSIONS | SUM | CONVEX-OPTIMIZATION | COMPOSITE | ALTERNATING DIRECTION METHOD | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | Best approximation of the Kuhn-Tucker set | PROJECTIVE SPLITTING METHODS | POINT ALGORITHM | FIXED-POINTS | STRONG-CONVERGENCE | Projectors | Information science | Algorithms | Memory | Computer memory | Approximation | Mathematical analysis | Intersections

Proximal algorithm with memory | Inclusions with maximally monotone operators | Best approximation of the Kuhn–Tucker set | Operations Research/Decision Theory | Convex and Discrete Geometry | Mathematics | Operations Research, Management Science | Statistics, general | Attraction property | Optimization | Primal–dual algorithm | Image reconstruction | MATHEMATICS, APPLIED | Primal-dual algorithm | MAXIMAL MONOTONE-OPERATORS | INCLUSIONS | SUM | CONVEX-OPTIMIZATION | COMPOSITE | ALTERNATING DIRECTION METHOD | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | Best approximation of the Kuhn-Tucker set | PROJECTIVE SPLITTING METHODS | POINT ALGORITHM | FIXED-POINTS | STRONG-CONVERGENCE | Projectors | Information science | Algorithms | Memory | Computer memory | Approximation | Mathematical analysis | Intersections

Journal Article

Computational optimization and applications, ISSN 1573-2894, 2018, Volume 70, Issue 3, pp. 841 - 863

This paper proposes an algorithm for solving structured optimization problems, which covers both the backward...

Almost averagedness | Collection of sets | Metric subregularity | Picard iteration | RAAR algorithm | Alternating projection method | Transversality | Krasnoselski–Mann relaxation | Douglas–Rachford method | 65K05 | Mathematics | 90C26 | Statistics, general | Optimization | Secondary 49K40 | Operations Research/Decision Theory | Convex and Discrete Geometry | 65K10 | Operations Research, Management Science | 49M05 | 49M27 | Primary 49J53 | FEASIBILITY PROBLEMS | MATHEMATICS, APPLIED | Douglas-Rachford method | Krasnoselski-Mann relaxation | COLLECTIONS | LOCAL LINEAR CONVERGENCE | METRIC REGULARITY | NONLINEAR REGULARITY MODELS | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | CONVEX | SETS | NONCONVEX | ALTERNATING PROJECTIONS | Control systems | Analysis | Algorithms | Economic models | Fixed points (mathematics) | Direct reduction | Feasibility | Criteria | Convergence

Almost averagedness | Collection of sets | Metric subregularity | Picard iteration | RAAR algorithm | Alternating projection method | Transversality | Krasnoselski–Mann relaxation | Douglas–Rachford method | 65K05 | Mathematics | 90C26 | Statistics, general | Optimization | Secondary 49K40 | Operations Research/Decision Theory | Convex and Discrete Geometry | 65K10 | Operations Research, Management Science | 49M05 | 49M27 | Primary 49J53 | FEASIBILITY PROBLEMS | MATHEMATICS, APPLIED | Douglas-Rachford method | Krasnoselski-Mann relaxation | COLLECTIONS | LOCAL LINEAR CONVERGENCE | METRIC REGULARITY | NONLINEAR REGULARITY MODELS | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MINIMIZATION | CONVEX | SETS | NONCONVEX | ALTERNATING PROJECTIONS | Control systems | Analysis | Algorithms | Economic models | Fixed points (mathematics) | Direct reduction | Feasibility | Criteria | Convergence

Journal Article

Fixed Point Theory and Applications, ISSN 1687-1820, 12/2014, Volume 2014, Issue 1, pp. 1 - 15

In this paper, we investigate a splitting algorithm for treating monotone operators...

maximal monotone operator | Mathematical and Computational Biology | fixed point | Analysis | Mathematics, general | nonexpansive mapping | Mathematics | zero point | Applications of Mathematics | Topology | Differential Geometry | proximal point algorithm | Maximal monotone operator | Proximal point algorithm | Zero point | Nonexpansive mapping | Fixed point | APPROXIMATION | ITERATIVE ALGORITHM | MATHEMATICS | SEMIGROUPS | THEOREMS | MAPPINGS | ZERO POINTS | EQUILIBRIUM PROBLEMS | FIXED-POINTS | Fixed point theory | Usage | Hilbert space | Contraction operators | Operators | Theorems | Splitting | Algorithms | Convergence

maximal monotone operator | Mathematical and Computational Biology | fixed point | Analysis | Mathematics, general | nonexpansive mapping | Mathematics | zero point | Applications of Mathematics | Topology | Differential Geometry | proximal point algorithm | Maximal monotone operator | Proximal point algorithm | Zero point | Nonexpansive mapping | Fixed point | APPROXIMATION | ITERATIVE ALGORITHM | MATHEMATICS | SEMIGROUPS | THEOREMS | MAPPINGS | ZERO POINTS | EQUILIBRIUM PROBLEMS | FIXED-POINTS | Fixed point theory | Usage | Hilbert space | Contraction operators | Operators | Theorems | Splitting | Algorithms | Convergence

Journal Article

Journal of scientific computing, ISSN 1573-7691, 2018, Volume 76, Issue 3, pp. 1698 - 1717

In this paper, we propose a new primal–dual algorithm for minimizing $$f({\mathbf {x}})+g({\mathbf {x}})+h({\mathbf {A}}{\mathbf {x}})$$ f(x)+g(x)+h(Ax...

Computational Mathematics and Numerical Analysis | Nonexpansive operator | Algorithms | Theoretical, Mathematical and Computational Physics | Mathematical and Computational Engineering | Three-operator splitting | Chambolle–Pock | Mathematics | Fixed-point iteration | Primal–dual | MATHEMATICS, APPLIED | Chambolle-Pock | Primal-dual | CONVERGENCE RATE ANALYSIS | OPTIMIZATION | SPLITTING SCHEMES | SOLVING MONOTONE INCLUSIONS

Computational Mathematics and Numerical Analysis | Nonexpansive operator | Algorithms | Theoretical, Mathematical and Computational Physics | Mathematical and Computational Engineering | Three-operator splitting | Chambolle–Pock | Mathematics | Fixed-point iteration | Primal–dual | MATHEMATICS, APPLIED | Chambolle-Pock | Primal-dual | CONVERGENCE RATE ANALYSIS | OPTIMIZATION | SPLITTING SCHEMES | SOLVING MONOTONE INCLUSIONS

Journal Article

9.
Full Text
Further Results on the Convergence of the Pavon–Ferrante Algorithm for Spectral Estimation

IEEE transactions on automatic control, ISSN 1558-2523, 2018, Volume 63, Issue 10, pp. 3510 - 3515

In this paper, we provide a detailed analysis of the global convergence properties of an extensively studied and extremely effective fixed-point algorithm for the Kullback-Leibler approximation...

Algorithm design and analysis | Kullback–Leibler divergence | Density measurement | spectral estimation | Estimation | Approximation of spectral densities | Steady-state | Convergence | convergence analysis | Signal processing algorithms | Approximation algorithms | fixed-point iteration | generalized moment problems | Kullback-Leibler divergence | NEVANLINNA-PICK INTERPOLATION | POWER SPECTRA | KULLBACK-LEIBLER APPROXIMATION | H-INFINITY | ENGINEERING, ELECTRICAL & ELECTRONIC | DENSITIES | CONVEX-OPTIMIZATION APPROACH | COMPLEXITY CONSTRAINT | AUTOMATION & CONTROL SYSTEMS | ENTROPY | Spectra | Algorithms | Approximation | Mathematical analysis

Algorithm design and analysis | Kullback–Leibler divergence | Density measurement | spectral estimation | Estimation | Approximation of spectral densities | Steady-state | Convergence | convergence analysis | Signal processing algorithms | Approximation algorithms | fixed-point iteration | generalized moment problems | Kullback-Leibler divergence | NEVANLINNA-PICK INTERPOLATION | POWER SPECTRA | KULLBACK-LEIBLER APPROXIMATION | H-INFINITY | ENGINEERING, ELECTRICAL & ELECTRONIC | DENSITIES | CONVEX-OPTIMIZATION APPROACH | COMPLEXITY CONSTRAINT | AUTOMATION & CONTROL SYSTEMS | ENTROPY | Spectra | Algorithms | Approximation | Mathematical analysis

Journal Article

Pattern recognition, ISSN 0031-3203, 2002, Volume 35, Issue 10, pp. 2267 - 2278

...) clustering algorithms. These alternative types of c-means clustering have more robustness than c-means clustering...

Alternative c-means | Hard c-means | Robustness | Noise | Fixed-point iterations | Fuzzy c-means | alternative c-means | hard c-means | fixed-point iterations | robustness | noise | FUZZY | fuzzy c-means | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC

Alternative c-means | Hard c-means | Robustness | Noise | Fixed-point iterations | Fuzzy c-means | alternative c-means | hard c-means | fixed-point iterations | robustness | noise | FUZZY | fuzzy c-means | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC

Journal Article

Journal of global optimization, ISSN 1573-2916, 2017, Volume 70, Issue 3, pp. 687 - 704

In this article, we introduce an inertial projection and contraction algorithm by combining inertial type algorithms with the projection and contraction algorithm for solving a variational inequality...

49J35 | Extragradient algorithm | Inertial type algorithm | Variational inequality | 90C47 | Operations Research/Decision Theory | Projection and contraction algorithm | Mathematics | Computer Science, general | Optimization | Real Functions | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MAXIMAL MONOTONE-OPERATORS | CONVERGENCE | PROXIMAL POINT ALGORITHM | ITERATIVE METHODS | HILBERT-SPACE | Signal processing | Medical colleges | Algorithms | Resveratrol | Projection | Fixed points (mathematics) | Hilbert space | Portfolio management

49J35 | Extragradient algorithm | Inertial type algorithm | Variational inequality | 90C47 | Operations Research/Decision Theory | Projection and contraction algorithm | Mathematics | Computer Science, general | Optimization | Real Functions | MATHEMATICS, APPLIED | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MAXIMAL MONOTONE-OPERATORS | CONVERGENCE | PROXIMAL POINT ALGORITHM | ITERATIVE METHODS | HILBERT-SPACE | Signal processing | Medical colleges | Algorithms | Resveratrol | Projection | Fixed points (mathematics) | Hilbert space | Portfolio management

Journal Article

Medical Physics, ISSN 0094-2405, 07/2015, Volume 42, Issue 7, pp. 4007 - 4014

...). This algorithm, denoted polyenergetic SART (pSART), replaces the monoenergetic forward projection operation used by SART with a postlog, polyenergetic forward projection, while leaving the rest of the algorithm unchanged...

iterative methods | Nonlinear dynamics | ART | Matrix theory | Jacobian matrices | Digital computing or data processing equipment or methods, specially adapted for specific applications | nonlinear fixed point iteration | Computed tomography | polyenergetic CT | Medical X‐ray imaging | Eigenvalues | X‐ray spectra | Linear equations | Numerical approximation and analysis | Computerised tomographs | medical image processing | beam hardening | Reconstruction | algebraic reconstruction technique | convergence of numerical methods | image reconstruction | Biological material, e.g. blood, urine; Haemocytometers | computerised tomography | Image data processing or generation, in general | SART | Medical image reconstruction | Jacobians | computed tomography | RAY COMPUTED-TOMOGRAPHY | LINEAR-EQUATIONS | STATISTICAL IMAGE-RECONSTRUCTION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Algorithms | Models, Biological | Computer Simulation | Humans | Tomography, X-Ray Computed - methods | Nonlinear Dynamics | Phantoms, Imaging | Tomography, X-Ray Computed - instrumentation

iterative methods | Nonlinear dynamics | ART | Matrix theory | Jacobian matrices | Digital computing or data processing equipment or methods, specially adapted for specific applications | nonlinear fixed point iteration | Computed tomography | polyenergetic CT | Medical X‐ray imaging | Eigenvalues | X‐ray spectra | Linear equations | Numerical approximation and analysis | Computerised tomographs | medical image processing | beam hardening | Reconstruction | algebraic reconstruction technique | convergence of numerical methods | image reconstruction | Biological material, e.g. blood, urine; Haemocytometers | computerised tomography | Image data processing or generation, in general | SART | Medical image reconstruction | Jacobians | computed tomography | RAY COMPUTED-TOMOGRAPHY | LINEAR-EQUATIONS | STATISTICAL IMAGE-RECONSTRUCTION | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Algorithms | Models, Biological | Computer Simulation | Humans | Tomography, X-Ray Computed - methods | Nonlinear Dynamics | Phantoms, Imaging | Tomography, X-Ray Computed - instrumentation

Journal Article

Journal of inequalities and applications, ISSN 1029-242X, 2013, Volume 2013, Issue 1, pp. 199 - 14

In this paper, an iterative algorithm is proposed to study some nonlinear operators which are inverse-strongly monotone, maximal monotone, and strictly pseudocontractive...

maximal monotone operator | strictly pseudocontractive mapping | inverse-strongly monotone mapping | fixed point | Analysis | Mathematics, general | Mathematics | Applications of Mathematics | resolvent | Inverse-strongly monotone mapping | Maximal monotone operator | Strictly pseudocontractive mapping | Resolvent | Fixed point | COMMON SOLUTIONS | MATHEMATICS, APPLIED | NONEXPANSIVE-MAPPINGS | APPROXIMATION | MATHEMATICS | HYBRID PROJECTION METHODS | THEOREMS | WEAK-CONVERGENCE | SEQUENCE | EQUILIBRIUM PROBLEMS | FIXED-POINT PROBLEMS

maximal monotone operator | strictly pseudocontractive mapping | inverse-strongly monotone mapping | fixed point | Analysis | Mathematics, general | Mathematics | Applications of Mathematics | resolvent | Inverse-strongly monotone mapping | Maximal monotone operator | Strictly pseudocontractive mapping | Resolvent | Fixed point | COMMON SOLUTIONS | MATHEMATICS, APPLIED | NONEXPANSIVE-MAPPINGS | APPROXIMATION | MATHEMATICS | HYBRID PROJECTION METHODS | THEOREMS | WEAK-CONVERGENCE | SEQUENCE | EQUILIBRIUM PROBLEMS | FIXED-POINT PROBLEMS

Journal Article

Pattern recognition, ISSN 0031-3203, 2016, Volume 60, pp. 971 - 982

We propose a fast algorithm for approximate matching of large graphs. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability...

Projected fixed-point | Feature correspondence | Large graph algorithm | Point matching | Graph matching | RECOGNITION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms

Projected fixed-point | Feature correspondence | Large graph algorithm | Point matching | Graph matching | RECOGNITION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Algorithms

Journal Article

Archiv der Mathematik, ISSN 0003-889X, 6/2014, Volume 102, Issue 6, pp. 589 - 600

We discuss the Douglas–Rachford algorithm to solve the feasibility problem for two closed sets A,B in $${\mathbb{R}^d}$$ R d...

Stability | Mathematics, general | Nonconvex feasibility problem | Mathematics | Discrete dynamical system | Fixed-point | Convergence | MATHEMATICS | Algorithms | Optimization and Control

Stability | Mathematics, general | Nonconvex feasibility problem | Mathematics | Discrete dynamical system | Fixed-point | Convergence | MATHEMATICS | Algorithms | Optimization and Control

Journal Article

Bulletin of the Malaysian Mathematical Sciences Society, ISSN 0126-6705, 1/2019, Volume 42, Issue 1, pp. 201 - 221

The paper proposes an explicit parallel iterative algorithm for solving variational inequalities over the intersection of fixed point sets of finitely many demicontractive mappings...

47J20 | Variational inequality | Mathematics, general | Mathematics | Demicontractive mapping | Parallel computation | Applications of Mathematics | 47H05 | 47J25 | 65J15 | Nonexpansive mapping | ITERATIVE ALGORITHM | REGULARIZING SYSTEMS | MATHEMATICS | KACZMARZ METHODS | STEEPEST-DESCENT METHODS | MAPPINGS | EQUILIBRIUM PROBLEMS | FIXED-POINT PROBLEMS | STRONG-CONVERGENCE

47J20 | Variational inequality | Mathematics, general | Mathematics | Demicontractive mapping | Parallel computation | Applications of Mathematics | 47H05 | 47J25 | 65J15 | Nonexpansive mapping | ITERATIVE ALGORITHM | REGULARIZING SYSTEMS | MATHEMATICS | KACZMARZ METHODS | STEEPEST-DESCENT METHODS | MAPPINGS | EQUILIBRIUM PROBLEMS | FIXED-POINT PROBLEMS | STRONG-CONVERGENCE

Journal Article

Energy (Oxford), ISSN 0360-5442, 09/2018, Volume 159, pp. 61 - 73

This paper proposes a capacity estimation algorithm for Li-ion batteries (LIBs) using the successive approximation method...

Lithium | State of health (SOH) | Cycle aging | Fixed point iteration | Capacity estimation | CELL STATE | MODEL PARAMETERS | ENERGY & FUELS | SOC | DEGRADATION MECHANISMS | ONLINE ESTIMATION | THERMODYNAMICS | STATE-OF-CHARGE | OPEN-CIRCUIT-VOLTAGE | FRAMEWORK | HEALTH ESTIMATION | Electrical engineering | Control systems | Algorithms | Batteries | Analysis

Lithium | State of health (SOH) | Cycle aging | Fixed point iteration | Capacity estimation | CELL STATE | MODEL PARAMETERS | ENERGY & FUELS | SOC | DEGRADATION MECHANISMS | ONLINE ESTIMATION | THERMODYNAMICS | STATE-OF-CHARGE | OPEN-CIRCUIT-VOLTAGE | FRAMEWORK | HEALTH ESTIMATION | Electrical engineering | Control systems | Algorithms | Batteries | Analysis

Journal Article