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Real function with secant line between points above the graph itself
function is called convex if the line segment between any two distinct points on the graph of the function lies above or on the graph of the function
Convex_function
Mathematical function
K-convex functions, first introduced by Scarf, are a special weakening of the concept of convex function which is crucial in the proof of the optimality
K-convex_function
In geometry, set whose intersection with every line is a single line segment
the function) is a convex set. Convex minimization is a subfield of optimization that studies the problem of minimizing convex functions over convex sets
Convex_set
Function in mathematical analysis
In mathematics, a Schur-convex function, also known as S-convex, isotonic function and order-preserving function is a function f : R d → R {\displaystyle
Schur-convex_function
Type of function
In convex analysis and the calculus of variations, both branches of mathematics, a pseudoconvex function is a function that behaves like a convex function
Pseudoconvex_function
Mathematics of convex functions and sets
Convex analysis is the branch of mathematics that studies convex sets, convex functions, and their applications to optimization, functional analysis,
Convex_analysis
Subfield of mathematical optimization
Convex optimization is a subfield of mathematical optimization that studies the problem of minimizing convex functions over convex sets (or, equivalently
Convex_optimization
Concept in convex analysis
particular the subfields of convex analysis and optimization, a proper convex function is an extended real-valued convex function with a non-empty domain
Proper_convex_function
Concept in mathematics
In mathematics, the K-function, typically denoted K(z), is a generalization of the hyperfactorial to complex numbers, similar to the generalization of
K-function
Type of mathematical functions
set of holomorphic functions on G. For a compact set K ⊂ G {\displaystyle K\subset G} , the holomorphically convex hull of K is K ^ G = { z ∈ G ; | f
Function of several complex variables
Function_of_several_complex_variables
Smallest convex set containing a given set
In geometry, the convex hull, convex envelope or convex closure of a shape is the smallest convex set that contains it. The convex hull may be defined
Convex_hull
self-concordant barrier is a particular self-concordant function, that is also a barrier function for a particular convex set. Self-concordant barriers are important
Self-concordant_function
Property of functions which is weaker than continuity
in convex analysis. Given a convex (extended real) function, the epigraph might not be closed. But the lower semicontinuous hull of a convex function is
Semi-continuity
Type of mathematical function
In convex analysis, a non-negative function f : Rn → R+ is logarithmically concave (or log-concave for short) if its domain is a convex set, and if it
Logarithmically concave function
Logarithmically_concave_function
Invex functions were introduced by Hanson as a generalization of convex functions. Ben-Israel and Mond provided a simple proof that a function is invex
Invex_function
Minimal superset that intersects each axis-parallel line in an interval
a set K ⊂ Rd is defined to be orthogonally convex if, for every line L that is parallel to one of standard basis vectors, the intersection of K with L
Orthogonal_convex_hull
Algorithms for solving convex optimization problems
a convex function and G is a convex set. Without loss of generality, we can assume that the objective f is a linear function. Usually, the convex set
Interior-point_method
Strong form of uniform continuity
all real-valued Lipschitz functions on a compact metric space X having Lipschitz constant ≤ K is a locally compact convex subset of the Banach space
Lipschitz_continuity
Optimization algorithm
compact convex set in a vector space and f : D → R {\displaystyle f\colon {\mathcal {D}}\to \mathbb {R} } is a convex, differentiable real-valued function. The
Frank–Wolfe_algorithm
Closely related to the problems on convex sets is the following problem on a compact convex set K and a convex function f: Rn → R given by an approximate
Algorithmic problems on convex sets
Algorithmic_problems_on_convex_sets
Theorem of convex functions
mathematician Johan Jensen, relates the value of a convex function of an integral to the integral of the convex function. It was proved by Jensen in 1906, building
Jensen's_inequality
Mathematical set closed under positive linear combinations
combinations with positive coefficients. It follows that convex cones are convex sets. The definition of a convex cone makes sense in a vector space over any ordered
Convex_cone
Linear combination of points where all coefficients are non-negative and sum to 1
In convex geometry and vector algebra, a convex combination is a linear combination of points (which can be vectors, scalars, or more generally points
Convex_combination
Space with topology generated by convex sets
analysis and related areas of mathematics, locally convex topological vector spaces (LCTVS) or locally convex spaces are examples of topological vector spaces
Locally convex topological vector space
Locally_convex_topological_vector_space
Concept in convex optimization mathematics
be a convex function with domain R n . {\displaystyle \mathbb {R} ^{n}.} A classical subgradient method iterates x ( k + 1 ) = x ( k ) − α k g ( k )
Subgradient_method
Optimization algorithm
minimum under certain assumptions on the function f {\displaystyle f} (for example, f {\displaystyle f} convex and ∇ f {\displaystyle \nabla f} Lipschitz)
Gradient_descent
Point where the curvature of a curve changes sign
the case of the graph of a function, it is a point where the function changes from being concave (concave downward) to convex (concave upward), or vice
Inflection_point
Theorem in topology
general form than the latter is for continuous functions from a nonempty convex compact subset K {\displaystyle K} of Euclidean space to itself. Among hundreds
Brouwer_fixed-point_theorem
Smoothed ramp function
multivariable generalization of the logistic function. Both LogSumExp and softmax are used in machine learning. The convex conjugate (specifically, the Legendre
Softplus
Extension of the factorial function
is the unique interpolating function for the factorial, defined over the positive reals, which is logarithmically convex, meaning that y = log f ( x
Gamma_function
Iterative method for minimizing convex functions
the ellipsoid method is an iterative method for minimizing convex functions over convex sets. The ellipsoid method generates a sequence of ellipsoids
Ellipsoid_method
Study of mathematical algorithms for optimization problems
Generally, unless the objective function is convex in a minimization problem, there may be several local minima. In a convex problem, if there is a local
Mathematical_optimization
\alpha \prod _{k=1}^{n-1}{\Bigl (}1+{\frac {\alpha }{k}}{\Bigr )}={\frac {\pi }{\mathrm {B} (\alpha ,n)}}.} Logarithmically convex function Khabibullin B
Khabibullin's conjecture on integral inequalities
Khabibullin's_conjecture_on_integral_inequalities
Mathematical transformation
transformation on real-valued functions that are convex on a real variable. Specifically, if a real-valued multivariable function is convex on one of its independent
Legendre_transformation
Form of projection
^{d}\rightarrow \mathbb {R} ,\ i=1,\dots ,n} are possibly non-differentiable convex functions. The lack of differentiability rules out conventional smooth optimization
Proximal_gradient_method
Type of function in linear algebra
and positive homogeneity implies the third. Every sublinear function is a convex function: For 0 ≤ t ≤ 1 , {\displaystyle 0\leq t\leq 1,} p ( t x + (
Sublinear_function
{\displaystyle M_{k+1}/M_{k}} is increasing. When M k {\displaystyle M_{k}} is logarithmically convex, then ( M k ) 1 / k {\displaystyle (M_{k})^{1/k}} is increasing
Quasi-analytic_function
Solving an optimization problem with a quadratic objective function
1137/S1064827598345667. Kozlov, M. K.; S. P. Tarasov; Leonid G. Khachiyan (1979). "[Polynomial solvability of convex quadratic programming]". Doklady Akademii
Quadratic_programming
Sums of sets of vectors are nearly convex
are sums of many functions. In probability, it can be used to prove a law of large numbers for random sets. A set is said to be convex if every line segment
Shapley–Folkman_lemma
Theorem on extension of bounded linear functionals
1.} Every sublinear function is a convex function. On the other hand, if p : X → R {\displaystyle p:X\to \mathbb {R} } is convex with p ( 0 ) ≥ 0 , {\displaystyle
Hahn–Banach_theorem
Construct in functional analysis
set or disk in a vector space (over a field K {\displaystyle \mathbb {K} } with an absolute value function | ⋅ | {\displaystyle |\cdot |} ) is a set S
Balanced_set
Sums vector sets A and B by adding each vector in A to each vector in B
For K and L compact convex subsets in R n {\textstyle \mathbb {R} ^{n}} , the Minkowski sum can be described by the support function of the convex sets:
Minkowski_addition
Function reducing distance between all points
metric space (M, d) is a function f from M to itself, with the property that there is some real number 0 ≤ k < 1 {\displaystyle 0\leq k<1} such that for all
Contraction_mapping
Loss function used in robust regression
heavy-tailed distributions. As defined above, the Huber loss function is strongly convex in a uniform neighborhood of its minimum a = 0 {\displaystyle
Huber_loss
On when a function on convex body K does not decrease if K is translated inwards
integrable, symmetric, unimodal, non-negative function f over an n-dimensional convex body K does not decrease if K is translated inwards towards the origin
Anderson's_theorem
Topological vector spaces
complex-valued functions on U. For any compact subset K ⊆ U , {\displaystyle K\subseteq U,} let C k ( K ) {\displaystyle C^{k}(K)} and C k ( K ; U ) {\displaystyle
Spaces of test functions and distributions
Spaces_of_test_functions_and_distributions
Class of algorithms in computational geometry
additional work. As stated above, the complexity of finding a convex hull as a function of the input size n {\displaystyle n} is lower bounded by Ω (
Convex_hull_algorithms
Function made from a set
balanced function. Absorbing: If K {\textstyle K} is convex or balanced and if ( 0 , ∞ ) K = X {\textstyle (0,\infty )K=X} then K {\textstyle K} is absorbing
Minkowski_functional
Fundamental trigonometric functions
four functions. The ( 4 n + k ) {\displaystyle (4n+k)} -th derivative, evaluated at the point 0: sin ( 4 n + k ) ( 0 ) = { 0 when k = 0 1 when k = 1
Sine_and_cosine
Principle in mathematical optimization
with replacing a non-convex function with its convex closure, that is the function that has the epigraph that is the closed convex hull of the original
Duality_(optimization)
Mathematical function characterizing set membership
characteristic function in convex analysis, which is defined as if using the reciprocal of the standard definition of the indicator function. A related concept
Indicator_function
Concept in mathematical optimization
variable chosen from a convex subset of R n {\displaystyle \mathbb {R} ^{n}} , f {\displaystyle f} is the objective or utility function, g i ( i = 1 , …
Karush–Kuhn–Tucker_conditions
Function with a multiplicative scaling behaviour
homogeneous function. For example, a homogeneous polynomial of degree k defines a homogeneous function of degree k. The above definition extends to functions whose
Homogeneous_function
Mathematical function having a characteristic S-shaped curve or sigmoid curve
asymptotes as x → ± ∞ {\displaystyle x\rightarrow \pm \infty } . A sigmoid function is convex for values less than a particular point, and it is concave for values
Sigmoid_function
Concept in probability theory and statistics
generating functions are positive and log-convex,[citation needed] with M(0) = 1. An important property of the moment generating function is that it uniquely
Moment_generating_function
Concept in mathematics
particular geometries. We are given convex function f {\displaystyle f} to optimize over a convex set K ⊂ R n {\displaystyle K\subset \mathbb {R} ^{n}} , and
Mirror_descent
Length in a vector space
seminorm is a sublinear function and thus satisfies all properties of the latter. In particular, every norm is a convex function. The concept of unit circle
Norm_(mathematics)
In convex geometry, the projection body Π K {\displaystyle \Pi K} of a convex body K {\displaystyle K} in n-dimensional Euclidean space is the convex body
Projection_body
Fourier transform of the probability density function
even, continuous function which satisfies the conditions φ ( 0 ) = 1 {\displaystyle \varphi (0)=1} , φ {\displaystyle \varphi } is convex for t > 0 {\displaystyle
Characteristic function (probability theory)
Characteristic_function_(probability_theory)
Study of optimal transportation and allocation of resources
are both optimal. If, on the other hand, we choose the strictly convex cost function proportional to the square of Euclidean distance ( c ( x , y ) =
Transportation theory (mathematics)
Transportation_theory_(mathematics)
Mathematical concept
Pareto front, convex or concave. Definition For a minimization problem with objective functions f 1 , … , f k {\displaystyle f_{1},\dots ,f_{k}} and the ideal
Multi-objective_optimization
Set of probability measures
If additionally K ( X ) {\displaystyle K(X)} is also closed and convex, then the lower prevision of a function f {\displaystyle f} of X {\displaystyle
Credal_set
Special function in the physical sciences
mathematics, the Airy function (or Airy function of the first kind) A i ( x ) {\displaystyle \mathbf {Ai({\boldsymbol {x}})} } is a special function named after
Airy_function
Meromorphic function
) {\displaystyle \ln \Gamma (x)} is strictly convex. For m = 0 {\displaystyle m=0} , the digamma function, ψ ( x ) = ψ ( 0 ) ( x ) {\displaystyle \psi
Polygamma_function
Primal-Dual algorithm optimization for convex problems
designed to efficiently solve convex optimization problems that involve the minimization of a non-smooth cost function composed of a data fidelity term
Chambolle–Pock_algorithm
Type of mathematical plane curve
The support function describing the supporting lines for a convex set K {\displaystyle K} is defined by h ( q ) = max { p ⋅ q ∣ p ∈ K } {\displaystyle
Hedgehog_(geometry)
Theorem in geometry about convex sets
K is any (d + 1)-dimensional compact convex set, and ƒ is any continuous function from K to d-dimensional space, then there exists a linear function g
Radon's_theorem
Fixed-point theorem for set-valued functions
fixed-point theorem for set-valued functions. It provides sufficient conditions for a set-valued function defined on a convex, compact subset of a Euclidean
Kakutani_fixed-point_theorem
Exponential function of an exponential function
A double exponential function is a constant raised to the power of an exponential function. The general formula is f ( x ) = a b x = a ( b x ) {\displaystyle
Double_exponential_function
Concept in financial economics
distribution function g {\displaystyle g} if and only if g {\displaystyle g} is concave. If instead of the sublinear property,R is convex, then R is a
Coherent_risk_measure
Point where function's value is zero
sometimes called a root) of a real-, complex-, or generally vector-valued function f {\displaystyle f} , is a member x {\displaystyle x} of the domain of
Zero_of_a_function
Type of metric space in mathematics
curvature ≤ k {\displaystyle \leq k} if every point of X {\displaystyle X} has a geodesically convex CAT ( k ) {\displaystyle \operatorname {CAT} (k)} neighbourhood
CAT(k)_space
Auxiliary functions used to probe equations, distributions, and weak formulations
subset K of U such that φ {\displaystyle \varphi } (x) = 0 for all x in U \ K. The elements of D(U) are the infinitely differentiable functions φ {\displaystyle
Test_function
Branch of geometry
valuations on convex bodies inequalities and extremum problems convex functions and convex programs spherical and hyperbolic convexity Convex geometry is
Convex_geometry
Generalized function whose value is zero everywhere except at zero
Moreover, the convex hull of the image of X under this embedding is dense in the space of probability measures on X. The delta function satisfies the
Dirac_delta_function
function f ∘ γ ( t ) − λ t 2 {\displaystyle f\circ \gamma (t)-\lambda t^{2}} is convex. Convex A subset K of a Riemannian manifold M is called convex
Glossary of Riemannian and metric geometry
Glossary_of_Riemannian_and_metric_geometry
Set-to-real map with diminishing returns
\sum _{S}\alpha _{S}=1,\alpha _{S}\geq 0\right)} . The convex closure of any set function is convex over [ 0 , 1 ] n {\displaystyle [0,1]^{n}} . Consider
Submodular_set_function
Concept in Hlibert spaces mathematics
function defined on an interval I and let m and n be natural numbers. If f is convex, we then have the inequality Tr ( f ( ∑ k = 1 n A k ∗ X k A k )
Trace_inequality
Game where groups of players may enforce cooperative behaviour
are reversed, so that we say the cost game is convex if the characteristic function is submodular. Convex cooperative games have many nice properties:
Cooperative_game_theory
Four-dimensional analogues of the regular polyhedra in three dimensions
polygons in two dimensions. There are six convex and ten star regular 4-polytopes, giving a total of sixteen. The convex regular 4-polytopes were first described
Regular_4-polytope
_{\delta K}{\frac {H}{2\pi }}dS=b(K)} where δ K {\displaystyle \delta K} is the boundary of the convex body K {\displaystyle K} and d S {\displaystyle dS} a
Mean_width
Locally convex topological vector space that is also a complete metric space
sequences K N {\displaystyle \mathbb {K} ^{\mathbb {N} }} (with the product topology) is a Fréchet space. There does not exist any Hausdorff locally convex topology
Fréchet_space
Method for finding stationary points of a function
setting x k + 1 = x k + t {\displaystyle x_{k+1}=x_{k}+t} . If the second derivative is positive, the quadratic approximation is a convex function of t {\displaystyle
Newton's method in optimization
Newton's_method_in_optimization
Function whose values are sets (mathematics)
2008 Mitroi, F.-C.; Nikodem, K.; Wąsowicz, S. (2013). "Hermite-Hadamard inequalities for convex set-valued functions". Demonstratio Mathematica. 46
Set-valued_function
Mathematical optimization function
regularization) M f {\displaystyle M_{f}} of a proper lower semi-continuous convex function f {\displaystyle f} is a smoothed version of f {\displaystyle f} .
Moreau_envelope
Theorems generalizing the Brouwer fixed-point theorem
fixed-point theorem: Let K be a nonempty closed bounded convex set in a uniformly convex Banach space. Then any non-expansive function f : K → K has a fixed point
Fixed-point theorems in infinite-dimensional spaces
Fixed-point_theorems_in_infinite-dimensional_spaces
over the real or complex numbers that at the same time is also a (locally convex) Fréchet space. The multiplication operation ( a , b ) ↦ a ∗ b {\displaystyle
Fréchet_algebra
The Schwartz–Bruhat space is not only a vector space of functions but also a locally convex topological vector space. In the real case this is the usual
Schwartz–Bruhat_function
Provides conditions for a parametric optimization problem to have continuous solutions
and C {\displaystyle C} is convex-valued, then C ∗ {\displaystyle C^{*}} is single-valued, and thus is a continuous function rather than a correspondence
Maximum_theorem
Measure of difference between two points
measure of difference between two points, defined in terms of a strictly convex function; they form an important class of divergences. When the points are interpreted
Bregman_divergence
Relation among continuous functions
In mathematical analysis, a family of functions is equicontinuous if all the functions are continuous and they have equal variation over a given neighbourhood
Equicontinuity
Flat-sided three-dimensional shape
reflecting. Convex polyhedra are a well-defined class of polyhedra with several equivalent standard definitions. Every convex polyhedron is the convex hull of
Polyhedron
Objects that generalize functions
non-metrizable, locally convex topological vector space. The duality pairing between a distribution T in D′(U) and a test function φ {\displaystyle \varphi
Distribution (mathematical analysis)
Distribution_(mathematical_analysis)
h(δ(t)) - h(y) is a convex Lipschitz function on [0,r] with Lipschitz constant 1 satisfying k(t) ≤ – t and k(0) = 0 and k(r) = –r. So k vanishes everywhere
Busemann_function
Type of fair division
be represented by a continuous utility function. Theorem 1 (Varian): If the preferences of all agents are convex and strongly monotone, then PEEF allocations
Efficient_envy-free_division
Optimization algorithm
function with a simpler, convex approximation. This approximation is represented by linear constraints and a convex objective function. Starting from an initial
Method_of_moving_asymptotes
Method of machine learning
example, with other convex loss functions. Consider the setting of supervised learning with f {\displaystyle f} being a linear function to be learned: f
Online_machine_learning
n\geq 0} is log-convex. It also means that for every n {\displaystyle n} the function f ( n ) {\displaystyle f^{(n)}} is log-convex because ( log f
Absolutely and completely monotonic functions and sequences
Absolutely_and_completely_monotonic_functions_and_sequences
Fundamental theorem in probability theory and statistics
density is a function constant inside a given convex body and vanishing outside; it corresponds to the uniform distribution on the convex body, which explains
Central_limit_theorem
Preorder on vectors of real numbers
x j ) {\displaystyle \varepsilon \in (0,x_{i}-x_{j})} . For every convex function h : R → R {\displaystyle h:\mathbb {R} \to \mathbb {R} } , ∑ i = 1
Majorization
K CONVEX-FUNCTION
K CONVEX-FUNCTION
Male
English
Anglicized form of Irish Gaelic Conláed, CONLEY means "purifying fire."
Surname or Lastname
English
English : from Middle English cony ‘rabbit’ (a back-formation from conies, from Old French conis, plural of conil), a nickname for someone thought to resemble a rabbit in some way or a metonymic occupational name for a dealer in rabbits or rabbit skins.
Male
Polish
Polish form of Russian Svyatopolk, ÅšWIĘTOPEÅK means "blessed people."
Surname or Lastname
English (Leicestershire)
English (Leicestershire) : variant of Culver.
Male
Greek
(Ἰσαάκ) Greek form of Hebrew Yitzchak, ISAÃK means "he will laugh."Â
Boy/Male
Irish American
Strong willed or wise. Also a : Hero.
Surname or Lastname
Italian
Italian : from the title of rank conte ‘count’ (from Latin comes, genitive comitis ‘companion’). Probably in this sense (and the Late Latin sense of ‘traveling companion’), it was a medieval personal name; as a title it was no doubt applied ironically as a nickname for someone with airs and graces or simply for someone who worked in the service of a count.English : variant of Count, cognate with 1.French : nickname for someone in the service of a count or for someone who behaved pretentiously, from Old French conte, cunte ‘count’ (of the same derivation as 1).French (Conté) : variant of Comté (see Comte).
Male
Hungarian
Hungarian form of Old High German Berhtram, BERTÓK means "bright raven."
Male
Hungarian
Hungarian form of Greek Isaák, IZSÃK means "he will laugh."Â
Boy/Male
Irish American
Hound lover. Full of desire; much desire.
Surname or Lastname
English
English : metathesized form of the occupational name Coyner.English : possibly an occupational name for a dealer in rabbits or rabbit skins, from an agent derivative of Middle English cony ‘rabbit’ (see Coney).
Male
Czechoslovakian
, famous war.
Boy/Male
Irish
Hero.
Male
English
Variant spelling of English Connor, CONNER means "hound-lover."
Surname or Lastname
English
English : habitational name from a place named Cove, examples of which are found in Devon, Hampshire, and Suffolk, from Old English cofa ‘cove’, ‘bay’, ‘inlet’, also ‘shelter’, ‘hut’, or a topographic name with the same meaning.
Male
Icelandic
Icelandic form of German Ludwig, LÚÃVÃK means "famous warrior."
Surname or Lastname
English
English : from Old French covine ‘fraud’, ‘deceit’, hence a derogatory nickname for a trickster.English : habitational name from a place in Staffordshire named Coven ‘(place) at the huts or shelters (Old English cofa, dative plural cofum)’.
Surname or Lastname
Irish
Irish : variant spelling of Connor, now common in Scotland.English : occupational name for an inspector of weights and measures, Middle English connere, cunnere ‘inspector’, an agent derivative of cun(nen) ‘to examine’.
Boy/Male
British, Christian, English
Wagoner; To Convey
Surname or Lastname
Spanish and Portuguese
Spanish and Portuguese : nickname from the title of rank conde ‘count’, a derivative of Latin comes, comitis ‘companion’.English : unexplained.
K CONVEX-FUNCTION
K CONVEX-FUNCTION
Girl/Female
Celtic
Mythical wife of Loegaire.
Girl/Female
Indian, Tamil
God's Presence
Girl/Female
Hindu, Indian, Traditional
Pleased
Boy/Male
Tamil
Ashwarth | à®…à®·à¯à®µà®¾à®°à¯à®¤
Generation / banyan tree
Boy/Male
Muslim
Strong, Powerful, Firm, Mighty. one of the names of Allah
Surname or Lastname
English
English : from a variant of the medieval female personal name Mab(be), a short form of Middle English, Old French Amabel (from Latin amabilis ‘loveable’). This has survived into the 20th century in the short form Mabel.English : possibly from an unattested Old English male personal name, Mappa.English : from Old Welsh map, mab ‘son’, which was used as a distinguishing epithet.
Girl/Female
Latin American Greek
Dark.
Girl/Female
Sikh
Embodiment of Love
Girl/Female
Muslim
Glorious
Girl/Female
Assamese, Bengali, Gujarati, Hebrew, Hindu, Indian, Kannada, Marathi, Sindhi, Telugu
River; Princess
K CONVEX-FUNCTION
K CONVEX-FUNCTION
K CONVEX-FUNCTION
K CONVEX-FUNCTION
K CONVEX-FUNCTION
a.
Convex on both sides; as, a biconvex lens.
a.
Made convex; protuberant in a spherical form.
imp. & p. p.
of Cove
v. t.
To call before a judge or judicature; to summon; to convene.
a.
Plane or flat on one side, and convex on the other; as, a plano-convex lens. See Convex, and Lens.
v. t.
To impart or communicate; as, to convey an impression; to convey information.
v. t.
To exchange for some specified equivalent; as, to convert goods into money.
a.
Specifically, having such a combination of concave and convex sides as makes the focal axis the shortest line between them. See Illust. under Lens.
a.
Convex on one side, and flat on the other; plano-convex.
n.
The conger eel; -- called also congeree.
a.
Convex on one side, and concave on the other. The curves of the convex and concave sides may be alike or may be different. See Meniscus.
a.
Convex on both sides; double convex. See under Convex, a.
adv.
In a convex form; as, a body convexly shaped.
v. t.
To cause to pass from one place or person to another; to serve as a medium in carrying (anything) from one place or person to another; to transmit; as, air conveys sound; words convey ideas.
v. t.
To context.
v. t.
To accompany; to convoy.
n. & v.
See Conge, Conge.
dv.
In a convex form; convexly.
a.
Concave on one side and convex on the other, as an eggshell or a crescent.
n.
A convex body or surface.