AI & ChatGPT searches , social queries for NEWTONS METHOD-IN-OPTIMIZATION
Search references for NEWTONS METHOD-IN-OPTIMIZATION. Phrases containing NEWTONS METHOD-IN-OPTIMIZATION
See searches and references containing NEWTONS METHOD-IN-OPTIMIZATION!
Search references for NEWTONS METHOD-IN-OPTIMIZATION. Phrases containing NEWTONS METHOD-IN-OPTIMIZATION
See searches and references containing NEWTONS METHOD-IN-OPTIMIZATION!NEWTONS METHOD-IN-OPTIMIZATION
Method for finding stationary points of a function
we seek to solve the optimization problem min x ∈ R f ( x ) . {\displaystyle \min _{x\in \mathbb {R} }f(x).} Newton's method attempts to solve this
Newton's method in optimization
Newton's_method_in_optimization
Optimization algorithm
quasi-Newton methods used in optimization exploit this symmetry. In optimization, quasi-Newton methods (a special case of variable-metric methods) are
Quasi-Newton_method
Algorithm for finding zeros of functions
convex optimization, second edition. Springer Optimization and its Applications, Volume 137. Süli & Mayers 2003. Kenneth L. Judd. Numerical methods in economics
Newton's_method
Mathematical optimization algorithms
truncated Newton method, originated in a paper by Ron Dembo and Trond Steihaug, also known as Hessian-free optimization, are a family of optimization algorithms
Truncated_Newton_method
Quasi-Newton root-finding method for the multivariable case
In numerical analysis, Broyden's method is a quasi-Newton method for finding roots in k variables. It was originally described by C. G. Broyden in 1965
Broyden's_method
Sequential model-based optimization of expensive black-box functions
Bayesian optimization is a sequential model-based strategy for global optimization of black-box objective functions whose evaluations are costly. It is
Bayesian_optimization
Optimization algorithm
programming (SQP) is an iterative method for constrained nonlinear optimization, also known as Lagrange-Newton method. SQP methods are used on mathematical problems
Sequential quadratic programming
Sequential_quadratic_programming
Optimization algorithm
Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate
Gradient_descent
Study of mathematical algorithms for optimization problems
generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer
Mathematical_optimization
Mathematical algorithm
overdetermined system. In what follows, the Gauss–Newton algorithm will be derived from Newton's method for function optimization via an approximation.
Gauss–Newton_algorithm
Algorithms for solving convex optimization problems
Interior-point methods (also referred to as barrier methods or IPMs) are algorithms for solving linear and non-linear convex optimization problems. IPMs
Interior-point_method
Iterative simulation method
In computational science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a population
Particle_swarm_optimization
Subfield of mathematical optimization
convex optimization problems admit polynomial-time algorithms, whereas mathematical optimization is in general NP-hard. A convex optimization problem
Convex_optimization
Field of engineering
Multi-disciplinary design optimization (MDO) is a field of engineering that uses optimization methods to solve design problems incorporating a number
Multidisciplinary design optimization
Multidisciplinary_design_optimization
Average solution cost is the same with any method
differentiable function) that can be exploited more efficiently (e.g., Newton's method in optimization) than random search or even has closed-form solutions (e.g
No free lunch in search and optimization
No_free_lunch_in_search_and_optimization
English polymath (1642–1727)
Leibniz. Newton contributed to and refined the scientific method, and his work is considered the most influential in bringing forth modern science. In the
Isaac_Newton
Class of algorithms for solving constrained optimization problems
Lagrangian methods are a certain class of algorithms for solving constrained optimization problems. They have similarities to penalty methods in that they
Augmented_Lagrangian_method
Concept in convex optimization mathematics
Subgradient methods are convex optimization methods which use subderivatives. Originally developed by Naum Z. Shor and others in the 1960s and 1970s, subgradient
Subgradient_method
Mathematical optimization method
The Barzilai–Borwein method is an iterative gradient descent method for unconstrained optimization using either of two step sizes derived from the linear
Barzilai–Borwein_method
in very-high-dimensional spaces Newton's method in optimization Nonlinear optimization BFGS method: a nonlinear optimization algorithm Gauss–Newton algorithm:
List_of_algorithms
Type of algorithm for constrained optimization
In mathematical optimization, penalty methods are a certain class of algorithms for solving constrained optimization problems. A penalty method replaces
Penalty_method
Numerical optimization algorithm
function in a multidimensional space. It is a direct search method (based on function comparison) and is often applied to nonlinear optimization problems
Nelder–Mead_method
In optimization, a gradient method is an algorithm to solve problems of the form min x ∈ R n f ( x ) {\displaystyle \min _{x\in \mathbb {R} ^{n}}\;f(x)}
Gradient_method
Optimization algorithm
Learning rate Pattern search (optimization) Secant method Nemirovsky and Ben-Tal (2023). "Optimization III: Convex Optimization" (PDF). Dennis, J. E. Jr.;
Line_search
a given number Meta-optimization — optimization of the parameters in an optimization method Multidisciplinary design optimization Optimal computing budget
List of numerical analysis topics
List_of_numerical_analysis_topics
Optimizing objective functions that have constrained variables
In mathematical optimization, constrained optimization (in some contexts called constraint optimization) is the process of optimizing an objective function
Constrained_optimization
Optimization algorithm
Singer, Y. (2016). "A Stochastic Quasi-Newton method for Large-Scale Optimization". SIAM Journal on Optimization. 26 (2): 1008–1031. arXiv:1401.7020. doi:10
Stochastic_gradient_descent
Methods in numerical computation
Kaps–Rentrop methods. Rosenbrock search is a numerical optimization algorithm applicable to optimization problems in which the objective function is inexpensive
Rosenbrock_methods
Numerical approximation algorithm
method like gradient descent, hill climbing, Newton's method, or quasi-Newton methods like BFGS, is an algorithm of an iterative method or a method of
Iterative_method
Concept in mathematics
In numerical optimization, the nonlinear conjugate gradient method generalizes the conjugate gradient method to nonlinear optimization. For a quadratic
Nonlinear conjugate gradient method
Nonlinear_conjugate_gradient_method
Numerical software
"Benchmarks for optimization software". Decision tree for optimization software. March 2022. Retrieved 31 March 2022. "Optimization and Operational Research:
HiGHS_optimization_solver
Meta-optimization from numerical optimization is the use of one optimization method to tune another optimization method. Meta-optimization is reported
Meta-optimization
Optimization algorithm
routing and internet routing. As an example, ant colony optimization is a class of optimization algorithms modeled on the actions of an ant colony. Artificial
Ant colony optimization algorithms
Ant_colony_optimization_algorithms
Optimization algorithm
Limited-memory BFGS (L-BFGS or LM-BFGS) is an optimization algorithm in the collection of quasi-Newton methods that approximates the Broyden–Fletcher–Goldfarb–Shanno
Limited-memory_BFGS
Method of mathematical optimization
being optimized, which means DE does not require the optimization problem to be differentiable, as is required by classic optimization methods such as
Differential_evolution
Approximation method in statistics
The Taylor series expansion of the model function. This is Newton's method in optimization. f ( x i , β ) = f k ( x i , β ) + ∑ j J i j Δ β j + 1 2 ∑
Non-linear_least_squares
Mathematical optimization algorithm
gradient method provides a generalization to non-symmetric matrices. Various nonlinear conjugate gradient methods seek minima of nonlinear optimization problems
Conjugate_gradient_method
Historical mathematical concept; form of derivative
and detailed them in his mathematical treatise, Method of Fluxions. Fluxions and fluents made up Newton's early calculus. If the fluent y {\displaystyle
Fluxion
Iterative optimisation algorithm
region algorithms for optimization". Iciam. Vol. 99. Powell, M.J.D. (1970). "A new algorithm for unconstrained optimization". In Rosen, J.B.; Mangasarian
Powell's_dog_leg_method
Iterative method for minimizing convex functions
In mathematical optimization, the ellipsoid method is an iterative method for minimizing convex functions over convex sets. The ellipsoid method generates
Ellipsoid_method
as Girard-Newton Newton's inequalities Newton's method also known as Newton–Raphson Newton's method in optimization Newton's notation Newton number, another
List of things named after Isaac Newton
List_of_things_named_after_Isaac_Newton
Optimization technique for solving (mixed) integer linear programs
In mathematical optimization, the cutting-plane method is any of a variety of optimization methods that iteratively refine a feasible set or objective
Cutting-plane_method
Algorithm for finding the extrema of a unimodal function
bounds; the maximum is between them return (left + right) / 2 Newton's method in optimization (can be used to search for where the derivative is zero) Golden-section
Ternary_search
Term in mathematical optimization
constrained optimization", SIAM J. Numer. Anal., 24 (1987), pp. 1152–1170. Yuan, Y. "A review of trust region algorithms for optimization" in ICIAM 99:
Trust_region
Matrix decomposition
compact representation for quasi-Newton methods is a matrix decomposition, which is typically used in gradient based optimization algorithms or for solving nonlinear
Compact quasi-Newton representation
Compact_quasi-Newton_representation
Inequalities for inexact line search
search, especially in quasi-Newton methods, first published by Philip Wolfe in 1969 (also named after Larry Armijo). In these methods the idea is to find
Wolfe_conditions
Matrix decomposition method
favorable for other reasons; for example, when performing Newton's method in optimization, adding a diagonal matrix can improve stability when far from
Cholesky_decomposition
Optimization method
In numerical optimization, the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm is an iterative method for solving unconstrained nonlinear optimization
Broyden–Fletcher–Goldfarb–Shanno algorithm
Broyden–Fletcher–Goldfarb–Shanno_algorithm
Set of methods for supervised statistical learning
approximation to the matrix is often used in the kernel trick. Another common method is Platt's sequential minimal optimization (SMO) algorithm, which breaks the
Support_vector_machine
Mathematical optimization problem restricted to integers
optimization, problem is a mathematical optimization or feasibility program in which some or all of the variables are restricted to be integers. In many
Integer_programming
Subfield of mathematical optimization
Combinatorial optimization is a subfield of mathematical optimization that consists of finding an optimal object from a finite set of objects, where the
Combinatorial_optimization
search Ant colony optimization algorithms Differential evolution Genetic algorithm Genetic programming Particle swarm optimization Backward chaining DPLL
List of artificial intelligence algorithms
List_of_artificial_intelligence_algorithms
Method to solve constrained optimization problems
In mathematical optimization, the method of Lagrange multipliers is a strategy for finding the local maxima and minima of a function subject to equation
Lagrange_multiplier
Concept in mathematics
In mathematics, mirror descent is an iterative optimization algorithm for finding a local minimum of a differentiable function. It generalizes algorithms
Mirror_descent
differential inequality, which makes it particularly easy for optimization using Newton's method A self-concordant barrier is a particular self-concordant
Self-concordant_function
Optimization algorithm
In numerical analysis, hill climbing is a mathematical optimization technique which belongs to the family of local search. It is an iterative algorithm
Hill_climbing
Optimization algorithm
algorithmic method for optimizing systems with multiple unknown parameters. It is a type of stochastic approximation algorithm. As an optimization method, it
Simultaneous perturbation stochastic approximation
Simultaneous_perturbation_stochastic_approximation
Optimization by removing non-optimal solutions to subproblems
Branch-and-bound (BB, B&B, or BnB) is a method for solving optimization problems by breaking them down into smaller subproblems and using a bounding function
Branch_and_bound
Solution process for some optimization problems
In mathematics, nonlinear programming (NLP), also known as nonlinear optimization, is the process of solving an optimization problem where some of the
Nonlinear_programming
Mathematical optimization method
In (unconstrained) mathematical optimization, a backtracking line search is a line search method to determine the amount to move along a given search direction
Backtracking_line_search
Algorithm used to solve non-linear least squares problems
By using the Gauss–Newton algorithm it often converges faster than first-order methods. However, like other iterative optimization algorithms, the LMA
Levenberg–Marquardt_algorithm
Problem optimization method
mathematical optimization method and an algorithmic paradigm. The method was developed by Richard Bellman in the 1950s and has found applications in numerous
Dynamic_programming
Optimization method
problems. Newton's method Newton's method in optimization Quasi-Newton method Broyden–Fletcher–Goldfarb–Shanno (BFGS) method Limited-memory BFGS method Symmetric
Davidon–Fletcher–Powell formula
Davidon–Fletcher–Powell_formula
Method to solve optimization problems
programming (LP), also called linear optimization, is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose
Linear_programming
coefficients through optimization. A number of optimization algorithms have the following general structure. Suppose that the function to be optimized is Q(β). Then
Berndt–Hall–Hall–Hausman algorithm
Berndt–Hall–Hall–Hausman_algorithm
Mathematical algorithm
Optimization algorithm Mathematical optimization – Study of mathematical algorithms for optimization problems Newton's method – Method for finding stationary points
Coordinate_descent
Sequential linear-quadratic programming (SLQP) is an iterative method for nonlinear optimization problems where objective function and constraints are twice
Sequential linear-quadratic programming
Sequential_linear-quadratic_programming
Method of solving linear programming problems
in the objective function, the Big M method sometimes refers to formulations of linear optimization problems in which violations of a constraint or set
Big_M_method
Method for mathematical optimization
In mathematical optimization, the criss-cross algorithm is any of a family of algorithms for linear programming. Variants of the criss-cross algorithm
Criss-cross_algorithm
Tuning parameter (hyperparameter) in optimization
the step length determined by inexact line search in quasi-Newton methods and related optimization algorithms. Initial rate can be left as system default
Learning_rate
Observatory
The Isaac Newton Group of Telescopes or ING consists of three optical telescopes: the William Herschel Telescope, the Isaac Newton Telescope, and the Jacobus
Isaac Newton Group of Telescopes
Isaac_Newton_Group_of_Telescopes
Approximation for nonlinear optimization
is an optimization technique for approximately solving nonlinear optimization problems. It is related to, but distinct from, quasi-Newton methods. Starting
Successive_linear_programming
Iterative method in numerical analysis
choices, the optimization problem is in the form of an unconstrained linear least-squares problem, which can be solved by standard methods including QR
Anderson_acceleration
Subfield of convex optimization
solutions of polynomial optimization problems. Semidefinite programming has been used in the optimization of complex systems. In recent years, some quantum
Semidefinite_programming
Algorithm for linear programming
In mathematical optimization, Dantzig's simplex algorithm (or simplex method) is an algorithm for linear programming. The name of the algorithm is derived
Simplex_algorithm
Search for an atomic arrangement with the lowest inter-atomic force
In the field of computational chemistry, energy minimization (also called energy optimization, geometry minimization, or geometry optimization) is the
Energy_minimization
Optimization algorithm
iterative first-order optimization algorithm for constrained convex optimization. Also known as the conditional gradient method, reduced gradient algorithm
Frank–Wolfe_algorithm
Collective behavior of decentralized, self-organized systems
Evolutionary algorithms (EA), particle swarm optimization (PSO), differential evolution (DE), ant colony optimization (ACO) and their variants dominate the field
Swarm_intelligence
Primal-Dual algorithm optimization for convex problems
In mathematics, the Chambolle–Pock algorithm is an algorithm used to solve convex optimization problems. It was introduced by Antonin Chambolle and Thomas
Chambolle–Pock_algorithm
Numerical optimization method
search (RS) is a family of numerical optimization methods that do not require the gradient of the optimization problem, and RS can hence be used on functions
Random_search
Approximation method in statistics
In regression analysis, least squares is a method to determine the best-fit model by minimizing the sum of the squared residuals—the differences between
Least_squares
Optimization technique
stochastic optimization, so that the solution found is dependent on the set of random variables generated. In combinatorial optimization, there are many
Metaheuristic
Combinatorial optimization method
Branch and cut is a method of combinatorial optimization for solving integer linear programs (ILPs), that is, linear programming (LP) problems where some
Branch_and_cut
Optimization software library
"Interior Point OPTimizer, pronounced I-P-Opt", is a software library for large scale nonlinear optimization of continuous systems. It is written in C++ (after
IPOPT
Linear programming algorithm
In mathematical optimization, the revised simplex method is a variant of George Dantzig's simplex method for linear programming. The revised simplex method
Revised_simplex_method
Branch of mathematical optimization
Discrete optimization is a branch of optimization in applied mathematics and computer science. As opposed to continuous optimization, some or all of the
Discrete_optimization
Evolutionary algorithm
strategy for numerical optimization. Evolution strategies (ES) are stochastic, derivative-free methods for numerical optimization of non-linear or non-convex
CMA-ES
Quasi-Newton method Broyden's method Newton's method in optimization Broyden-Fletcher-Goldfarb-Shanno (BFGS) method L-BFGS method Compact quasi-Newton representation
Symmetric_rank-one
Algorithm for solving the quadratic programming problem from training SVMs
is closely related to a family of optimization algorithms called Bregman methods or row-action methods. These methods solve convex programming problems
Sequential minimal optimization
Sequential_minimal_optimization
In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple (at least locally optimal)
Evolutionary multimodal optimization
Evolutionary_multimodal_optimization
Solving multiple machine learning tasks at the same time
learning in predictive analytics. The key motivation behind multi-task optimization is that if optimization tasks are related to each other in terms of
Multi-task_learning
Mathematical combinatorial optimization method
In applied mathematics, branch and price is a method of combinatorial optimization for solving integer linear programming (ILP) and mixed integer linear
Branch_and_price
Algorithm for solving linear programming problems
In mathematical optimization, affine scaling is an algorithm for solving linear programming problems. Specifically, it is an interior point method, discovered
Affine_scaling
Israeli-American computer scientist
Convex Optimization (2016) ISBN 9781521003442 Hazan, E., Agarwal, A., & Kale, S. (2007). Logarithmic regret algorithms for online convex optimization. Machine
Elad_Hazan
Solving an optimization problem with a quadratic objective function
of solving certain mathematical optimization problems involving quadratic functions. Specifically, one seeks to optimize (minimize or maximize) a multivariate
Quadratic_programming
Continuous function whose value increases to infinity
In constrained optimization, a field of mathematics, a barrier function is a continuous function whose value increases to infinity as its argument approaches
Barrier_function
Time-varying quantity or variable
treatise, Method of Fluxions. Newton described any variable that changed its value as a fluent – for example, the velocity of a ball thrown in the air.
Fluent_(mathematics)
Applied mathematician (1936–2015)
called Powell's method), and radial basis function.[citation needed] He had been working on derivative-free optimization algorithms in recent years, the
Michael_J._D._Powell
Overview of and topical guide to algorithms
Local search (optimization) Hill climbing Tabu search Genetic algorithm Ant colony optimization algorithms Particle swarm optimization Evolutionary algorithm
Outline_of_algorithms
Advanced method of process control
Another promising candidate for the nonlinear optimization problem is to use a randomized optimization method. Optimum solutions are found by generating
Model_predictive_control
NEWTONS METHOD-IN-OPTIMIZATION
NEWTONS METHOD-IN-OPTIMIZATION
Male
Greek
(Μεθόδιος) Greek name derived from methodos, METHODIOS means "method."
Female
Irish
Irish form of French Madeline, MADAILÉIN means "of Magdala."
Female
Irish
Variant spelling of Irish Gaelic LÃadan, LÃADÃIN means "grey lady."
Male
Polish
Polish form of Greek Methodios, METODY means "method."
Girl/Female
Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Sindhi, Tamil, Telugu
Method; Organisation; System
Girl/Female
Hindu, Indian
Method
Boy/Male
French, German, Polish
Long
Surname or Lastname
English
English : variant of Newton.Probably a translation of equivalents in other European languages, such as French Neuville or German Neustadt.
Girl/Female
Indian, Telugu
Method; Manner
Boy/Male
American, Anglo, Australian, British, Christian, English, French, Jamaican
From the New Estate; New Town; New Settlement
Male
Slovene
Slovene form of Greek Methodios, METOD means "method."
Boy/Male
Arabic, Muslim
Organization; Arrangement; Method
Boy/Male
Anglo Saxon American English
From the new estate.
Boy/Male
Indian, Sanskrit
Method; Law
Surname or Lastname
English
English : habitational name from any of the many places so named, from Old English nēowe ‘new’ + tūn ‘enclosure’, ‘settlement’. According to Ekwall, this is the commonest English place name. For this reason, the surname has a highly fragmented origin.
Girl/Female
Indian
Method
Male
Greek
(Σήθος) Greek form of Egyptian Sutekh, possibly SETHOS means "one who dazzles." In mythology, this is the name of an ancient evil god of Chaos, storms, and the desert, who slew Osiris.Â
Surname or Lastname
English
English : possibly a habitational name from Neaton in Norfolk. However, the modern surname occurs chiefly in the English Midlands suggesting a different source may be involved.
Boy/Male
Arabic, Muslim
Management; Method; Order; Regulation
Male
Croatian
, goodness.
NEWTONS METHOD-IN-OPTIMIZATION
NEWTONS METHOD-IN-OPTIMIZATION
Male
Native American
Native American Shawnee name HOKOLESQUA means "cornstalk."
Girl/Female
Hindu, Indian, Russian, Swahili, Tamil
Hope; Generous; Successful
Girl/Female
Australian, Hawaiian, Hebrew
Lamp of God; Angel
Boy/Male
Tamil
Robbie | ரோபà¯à®ªà¯€Â Â
Abbreviation of robert famed: bright: shining
Boy/Male
Australian, Gujarati, Indian, Kannada
Pride; Lord Shiva
Boy/Male
German
Mighty or intelligent.
Girl/Female
Hindu, Indian
Sleep
Boy/Male
Hindu, Indian, Traditional
A Grateful Person
Girl/Female
Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Tamil
Lord Krishna's Mother; Belongs to God
Girl/Female
Assamese, Hindu, Indian, Kannada
My Beloved
NEWTONS METHOD-IN-OPTIMIZATION
NEWTONS METHOD-IN-OPTIMIZATION
NEWTONS METHOD-IN-OPTIMIZATION
NEWTONS METHOD-IN-OPTIMIZATION
NEWTONS METHOD-IN-OPTIMIZATION
a.
Of or pertaining to the Teutons, esp. the ancient Teutons; Germanic.
n.
Classification; a mode or system of classifying natural objects according to certain common characteristics; as, the method of Theophrastus; the method of Ray; the Linnaean method.
prep.
With reference to a limit of time; as, in an hour; it happened in the last century; in all my life.
prep.
With reference to movement or tendency toward a certain limit or environment; -- sometimes equivalent to into; as, to put seed in the ground; to fall in love; to end in death; to put our trust in God.
adv.
In a cubical method.
n.
A follower of Newton.
prep.
With reference to physical surrounding, personal states, etc., abstractly denoted; as, I am in doubt; the room is in darkness; to live in fear.
n.
The technical name of methyl alcohol or wood spirit; also, by extension, the class name of any of the series of alcohols of the methane series of which methol proper is the type. See Methyl alcohol, under Methyl.
n.
A hydrocarbon radical, CH3, not existing alone but regarded as an essential residue of methane, and appearing as a component part of many derivatives; as, methyl alcohol, methyl ether, methyl amine, etc.
adv.
In the Socratic method.
prep.
With reference to circumstances or conditions; as, he is in difficulties; she stood in a blaze of light.
n.
A part of the sacerdotal habit among Jews, being a covering for the back and breast, held together on the shoulders by two clasps or brooches of onyx stones set in gold, and fastened by a girdle of the same stuff as the ephod. The ephod for the priests was of plain linen; that for the high priest was richly embroidered in colors. The breastplate of the high priest was worn upon the ephod in front.
n.
An orderly procedure or process; regular manner of doing anything; hence, manner; way; mode; as, a method of teaching languages; a method of improving the mind.
adv.
With privilege or possession; -- used to denote a holding, possession, or seisin; as, in by descent; in by purchase; in of the seisin of her husband.
prep.
With reference to space or place; as, he lives in Boston; he traveled in Italy; castles in the air.
v. t.
To inclose; to take in; to harvest.
adv.
Not out; within; inside. In, the preposition, becomes an adverb by omission of its object, leaving it as the representative of an adverbial phrase, the context indicating what the omitted object is; as, he takes in the situation (i. e., he comprehends it in his mind); the Republicans were in (i. e., in office); in at one ear and out at the other (i. e., in or into the head); his side was in (i. e., in the turn at the bat); he came in (i. e., into the house).
n.
A binary compound of methyl with some element; as, aluminium methide, Al2(CH3)6.
n.
Orderly arrangement, elucidation, development, or classification; clear and lucid exhibition; systematic arrangement peculiar to an individual.
prep.
A prefix from Eng. prep. in, also from Lat. prep. in, meaning in, into, on, among; as, inbred, inborn, inroad; incline, inject, intrude. In words from the Latin, in- regularly becomes il- before l, ir- before r, and im- before a labial; as, illusion, irruption, imblue, immigrate, impart. In- is sometimes used with an simple intensive force.