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Bangladeshi content platform and production company
Alpha-i Studios Ltd. is a Bangladeshi film production and distribution company, founded by Shahriar Shakil. Apart from producing and distributing Bengali
Alpha-i
2026 Indian film by Shiv Rawail
Alpha is a 2026 Indian Hindi-language action thriller film directed by Shiv Rawail, in his feature film debut, and produced by Aditya Chopra under Yash
Alpha_(2026_film)
Probability distribution
x_{K};\alpha _{1},\ldots ,\alpha _{K}\right)={\frac {1}{\mathrm {B} ({\boldsymbol {\alpha }})}}\prod _{i=1}^{K}x_{i}^{\alpha _{i}-1}} where x i ∈ [ 0
Dirichlet_distribution
Generative topic model
\left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}+\alpha _{i}-1}\,d\theta
Latent_Dirichlet_allocation
Error correction code
i = 0 d − 3 t c + i x i = α k 1 ∑ i = 0 d − 3 s c + i x i − ∑ i = 1 d − 2 s c + i x i − 1 . {\displaystyle T(x)=\sum _{i=0}^{d-3}t_{c+i}x^{i}=\alpha ^{k_{1}}\sum
BCH_code
Cohort born from 2010s to 2020s
Generation Alpha, often shortened to Gen Alpha, is the demographic cohort succeeding Generation Z and preceding the proposed Generation Beta. While researchers
Generation_Alpha
Economic formula of productivity
p_{i}x_{i}{\frac {(\alpha _{i}+\sum _{j\neq i}^{n}\alpha _{j})}{\alpha _{i}}}=w\Rightarrow p_{i}x_{i}{\frac {1}{\alpha _{i}}}=w} ⇒ x i ∗ = α i w p i ∀ i
Cobb–Douglas production function
Cobb–Douglas_production_function
Standard division algorithm for multi-digit numbers
d i = b r i − 1 + α i + l − 1 {\displaystyle d_{i}=br_{i-1}+\alpha _{i+l-1}} r i = d i − m β i = b r i − 1 + α i + l − 1 − m β i {\displaystyle r_{i}=d_{i}-m\beta
Long_division
Discrete probability distribution
{\boldsymbol {\alpha }})&=\,{\frac {c_{i}^{(-n)}+\alpha _{i}}{N-1+\sum _{i}\alpha _{i}}}&\propto \,c_{i}^{(-n)}+\alpha _{i}\end{aligned}}} where c i ( − n )
Categorical_distribution
Statistical model
_{t=1}^{T}u_{it}} . Since α i {\displaystyle \alpha _{i}} is constant, α i ¯ = α i {\displaystyle {\overline {\alpha _{i}}}=\alpha _{i}} and hence the effect
Fixed_effects_model
Descriptive statistic
_{ik})\\&={\text{Cov}}(\alpha _{i}+\epsilon _{ij},\alpha _{i}+\epsilon _{ik})\\&={\text{Cov}}(\alpha _{i},\alpha _{i})+2{\text{Cov}}(\alpha _{i},\epsilon
Intraclass_correlation
Risk-adjusted measure of the so-called active return on an investment
regression. S C L : R i , t − R f = α i + β i ( R M , t − R f ) + ε i , t {\displaystyle \mathrm {SCL} :R_{i,t}-R_{f}=\alpha _{i}+\beta _{i}\,(R_{M,t}-R_{f})+\varepsilon
Alpha_(finance)
Matrix normal form
elements α i {\displaystyle \alpha _{i}} satisfy α i ∣ α i + 1 {\displaystyle \alpha _{i}\mid \alpha _{i+1}} for all 1 ≤ i < r {\displaystyle 1\leq i<r} . This
Smith_normal_form
Linear combination of points
{\displaystyle \alpha _{1}x_{1}+\alpha _{2}x_{2}+\cdots +\alpha _{n}x_{n}} where the real numbers α i {\displaystyle \alpha _{i}} satisfy α i ≥ 0 {\displaystyle
Convex_combination
Error-correcting codes
/ α i {\displaystyle 1/\alpha _{i}} , o i = i {\displaystyle o_{i}=i} The error value for o i {\displaystyle o_{i}} is e i = ( − α i ω ( 1 / α i ) )
Reed–Solomon_error_correction
Construct for Hermitian matrices
eigenvectors v i {\displaystyle v_{i}} : x = ∑ i = 1 n α i v i , {\displaystyle x=\sum _{i=1}^{n}\alpha _{i}v_{i},} where α i = x ′ v i v i ′ v i = ⟨ x , v i ⟩ ‖
Rayleigh_quotient
Measure of polynomial height
| ∏ | α i | ≥ 1 | α i | = | a | ∏ i = 1 n max { 1 , | α i | } , {\displaystyle M(p)=|a|\prod _{|\alpha _{i}|\geq 1}|\alpha _{i}|=|a|\prod _{i=1}^{n}\max\{1
Mahler_measure
Theorem in transcendental number theory
has: | I i ( α k ) | ≤ | α k | e | α k | F i ( | α k | ) , {\displaystyle |I_{i}(\alpha _{k})|\leq {|\alpha _{k}|}e^{|\alpha _{k}|}F_{i}({|\alpha _{k}|})
Lindemann–Weierstrass_theorem
[H_{i},E_{\alpha }]=\alpha _{i}E_{\alpha }} Defining the dual root or coroot of α {\displaystyle \alpha } as α ∨ = 2 α ( α , α ) {\displaystyle \alpha ^{\vee
Chevalley_basis
Theorem
_{i=1}^{n}x_{i}g_{i}(x)&&\\&=\sum _{i=1}^{n}\left[x_{i}\left(g_{i}(x)-\alpha _{i}\right)\right]+\sum _{i=1}^{n-1}\left[x_{i}\alpha _{i}\right]&&\quad
Hadamard's_lemma
Statistical learning theory
= ∑ i = 1 n α i k ( ⋅ , x i ) , {\displaystyle f^{*}(\cdot )=\sum _{i=1}^{n}\alpha _{i}k(\cdot ,x_{i}),} where α i ∈ R {\displaystyle \alpha _{i}\in \mathbb
Representer_theorem
Computing a robot's end-effector position from joint values and kinematic equations
_{i}\cos \alpha _{i,i+1}&-\cos \theta _{i}\sin \alpha _{i,i+1}&a_{i,i+1}\sin \theta _{i}\\0&\sin \alpha _{i,i+1}&\cos \alpha _{i,i+1}&d_{i}\\0&0&0&1\end{bmatrix}}
Forward_kinematics
Statistical technique
i ℓ = 1 | X i ℓ ) = exp ( α i + β ⊤ X i ℓ ) 1 + exp ( α i + β ⊤ X i ℓ ) {\displaystyle \mathbb {P} (Y_{i\ell }=1|X_{i\ell })={\frac {\exp(\alpha _{i}+{\boldsymbol
Conditional logistic regression
Conditional_logistic_regression
Concept in calculus of variations
r_{\alpha _{1}}\partial r_{\alpha _{2}}\cdots \partial r_{\alpha _{i}}}}\qquad \qquad {\text{where}}\quad \alpha _{1},\alpha _{2},\dots ,\alpha _{i}=1
Functional_derivative
Convention for attaching reference frames to links of a kinematic chain
i , i + 1 0 0 0 0 1 ] , {\displaystyle [X_{i}]={\begin{bmatrix}1&0&0&r_{i,i+1}\\0&\cos \alpha _{i,i+1}&-\sin \alpha _{i,i+1}&0\\0&\sin \alpha _{i,i+1}&\cos
Denavit–Hartenberg_parameters
Process in linear algebra
that w = ∑ i = 1 m α i u i ⊗ v i {\textstyle w=\sum _{i=1}^{m}\alpha _{i}u_{i}\otimes v_{i}} , where the scalars α i {\displaystyle \alpha _{i}} are real
Schmidt_decomposition
Distributions in probability theory
{\displaystyle \alpha _{0}=\sum \alpha _{k}} and let p i = α i ∑ α k = α i α 0 {\displaystyle p_{i}={\frac {\alpha _{i}}{\sum \alpha _{k}}}={\frac {\alpha _{i}}{\alpha
Dirichlet-multinomial distribution
Dirichlet-multinomial_distribution
Condition in monotone comparative statics
and α i ′ > α i {\displaystyle \alpha ^{i'}>\alpha ^{i}} or if q < q ′ {\displaystyle q<q'} and α i ′ < α i {\displaystyle \alpha ^{i'}<\alpha ^{i}} , then
Single-crossing_condition
Linear combination of indicator functions of real intervals
[citation needed] f ( x ) = ∑ i = 0 n α i χ A i ( x ) {\displaystyle f(x)=\sum \limits _{i=0}^{n}\alpha _{i}\chi _{A_{i}}(x)} , for all real numbers x
Step_function
Concept in probability theory
\alpha } and β {\displaystyle \beta } we can compute the posterior hyperparameters α ′ = α + ∑ i x i = 2 + 3 + 4 + 1 = 10 {\textstyle \alpha '=\alpha +\sum
Conjugate_prior
Optimization algorithm
i := ρ i y i ⊤ z i {\displaystyle \beta _{i}:=\rho _{i}y_{i}^{\top }z_{i}} and z i + 1 = z i + ( α i − β i ) s i {\displaystyle z_{i+1}=z_{i}+(\alpha
Limited-memory_BFGS
w T w + c ∑ i = 1 N ξ i − ∑ i = 1 N α i { y i [ w T ϕ ( x i ) + b ] − 1 + ξ i } − ∑ i = 1 N β i ξ i , {\displaystyle L_{1}(w,b,\xi ,\alpha ,\beta )={\frac
Least-squares support vector machine
Least-squares_support_vector_machine
Operation in abstract algebra
{\displaystyle (\alpha _{i})} where α i ∈ M i {\displaystyle \alpha _{i}\in M_{i}} and α i = 0 {\displaystyle \alpha _{i}=0} for cofinitely many indices i. (The
Direct_sum_of_modules
First letter of the Greek alphabet
Alpha /ˈælfə/ ALF-ə (uppercase Α, lowercase α) is the first letter of the Greek alphabet. In the system of Greek numerals, it has a value of one. Alpha
Alpha
Linear combination whose coefficients sum to 1
combination ∑ i = 1 n α i ⋅ x i = α 1 x 1 + α 2 x 2 + ⋯ + α n x n , {\displaystyle \sum _{i=1}^{n}{\alpha _{i}\cdot x_{i}}=\alpha _{1}x_{1}+\alpha _{2}x_{2}+\cdots
Affine_combination
Lie algebra, usually infinite-dimensional
C} , i.e. a triple ( h , { α i } i = 1 n , { α i ∨ } i = 1 n , {\displaystyle ({\mathfrak {h}},\{\alpha _{i}\}_{i=1}^{n},\{\alpha _{i}^{\vee }\}_{i=1}^{n}
Kac–Moody_algebra
Covert operation by Indonesian military
Alpha (Indonesian: Operasi Alpha) (in Israel known as "פולינז" or Polines for Operation Alpha I in 1980 and "מזורקה" or Mazurka for Operation Alpha II
Operation_Alpha_(Indonesia)
i ( t + d t ) = α i d t + σ i d z i {\displaystyle r_{i}(t+dt)=\alpha _{i}dt+\sigma _{i}dz_{i}} with: E ( r i ) = α i d t ; E ( r i 2 ) = v a r ( r i
Intertemporal_CAPM
Algorithm for finding a local minimum of a function
1 , x 0 + ∑ i = 1 2 α i s i , … , x 0 + ∑ i = 1 N α i s i } {\textstyle \{x_{0}+\alpha _{1}s_{1},{x}_{0}+\sum _{i=1}^{2}\alpha _{i}{s}_{i},\dots ,{x}_{0}+\sum
Powell's_method
Concept in public economics
method: L = ∑ i α i u i ( x i , y ) + λ ( w − z − ∑ i = 1 I x i ) + μ ( g ( z ) − y ) {\displaystyle L=\sum _{i}\alpha ^{i}u^{i}(x^{i},y)+\lambda \left(w-z-\sum
Samuelson_condition
Time series model
_{i=1}^{q}\alpha _{i}\epsilon _{t-i}^{2}} , where α 0 > 0 {\displaystyle ~\alpha _{0}>0~} and α i ≥ 0 , i > 0 {\displaystyle \alpha _{i}\geq 0,~i>0}
Autoregressive conditional heteroskedasticity
Autoregressive_conditional_heteroskedasticity
Concept in mathematics
M={\begin{bmatrix}\alpha _{1}&\alpha _{1}^{q}&\dots &\alpha _{1}^{q^{n-1}}\\\alpha _{2}&\alpha _{2}^{q}&\dots &\alpha _{2}^{q^{n-1}}\\\alpha _{3}&\alpha _{3}^{q}&\dots
Moore_matrix
Polynomial whose roots are the eigenvalues of a matrix
= ∑ i α i t i . {\textstyle f(t)=\sum _{i}\alpha _{i}t^{i}.} Then f ( A ) = ∑ α i ( S − 1 U S ) i = ∑ α i S − 1 U S S − 1 U S ⋯ S − 1 U S = ∑ α i S −
Characteristic_polynomial
{\displaystyle {\begin{aligned}{\frac {\alpha _{i}}{\alpha _{i'}}}={\frac {I_{i',j_{i'}}(c_{j_{i'}})}{I_{i,j_{i}}(c_{j_{i}})}},i,i'\in \{1,2,...,k\}.\end{aligned}}}
Optimal computing budget allocation
Optimal_computing_budget_allocation
Algebraic construct of interest in theoretical physics
i k λ − 1 = q ( λ , α i ) e i k λ f i k λ − 1 = q − ( λ , α i ) f i [ e i , f j ] = δ i j k i − k i − 1 q i − q i − 1 k i = k α i , q i = q 1 2 ( α i
Quantum_group
Isomorphism between the tangent and cotangent bundles of a manifold
covector α = α i e i {\displaystyle \alpha =\alpha _{i}e^{i}} with the inverse of g {\displaystyle g} gives a vector with components α i = g i j α j . {\displaystyle
Musical_isomorphism
Statistical measure in mathematical model
α ^ i {\displaystyle {\hat {\alpha }}_{i}} with the following formula : V I F i = 1 1 − R i 2 {\displaystyle \mathrm {VIF} _{i}={\frac {1}{1-R_{i}^{2}}}}
Variance_inflation_factor
Independent parameter describing the state of a physical system
E_{i}\rangle =\int dX_{i}\,\alpha _{i}X_{i}^{2}\,p_{i}(X_{i})={\frac {\displaystyle \int dX_{i}\,\alpha _{i}X_{i}^{2}\,e^{-{\frac {\alpha _{i}X_{i
Degrees of freedom (physics and chemistry)
Degrees_of_freedom_(physics_and_chemistry)
Sequence of differential equation solutions
L_{n}^{(\alpha ')}(x)=(\alpha '-\alpha ){\alpha '+n \choose \alpha '-\alpha }\int _{0}^{x}{\frac {t^{\alpha }(x-t)^{\alpha '-\alpha -1}}{x^{\alpha '}}}L_{n}^{(\alpha
Laguerre_polynomials
Edible mixture of glucose and fructose, obtained from sucrose hydrolysis
i ) [ α ] i = ∑ i = 1 N χ i [ α ] i {\displaystyle \displaystyle \alpha ={\frac {\sum _{i=1}^{N}C_{i}[\alpha ]_{i}}{\sum _{i=1}^{N}C_{i}}}=\sum _{i=1}^{N}\left({\frac
Inverted_sugar_syrup
Compact operator for which a finite trace can be defined
u i ) i {\displaystyle (u_{i})_{i}} and ( v i ) i {\displaystyle (v_{i})_{i}} and a sequence ( α i ) i {\displaystyle \left(\alpha _{i}\right)_{i}} of
Trace_class
Representation of a quantum group
α i {\displaystyle \alpha _{i}} and non-negative integer n {\displaystyle n} , define e i ( 0 ) = f i ( 0 ) = 1 e i ( n ) = e i n [ n ] q i ! f i ( n
Crystal_base
Probability multivariate distribution
(x_{\bullet },\alpha _{\bullet })}{\mathrm {B} (x_{0},\alpha _{0})}}\prod _{i=1}^{m}{\frac {\Gamma (x_{i}+\alpha _{i})}{x_{i}!\Gamma (\alpha _{i})}}.} To obtain
Dirichlet negative multinomial distribution
Dirichlet_negative_multinomial_distribution
Relates the topology of a complete non-archimedean field to its algebraic extensions
α − β | < | α − α i | for i = 2 , … , n {\displaystyle \left|\alpha -\beta \right|<\left|\alpha -\alpha _{i}\right|{\text{ for }}i=2,\dots ,n} then K(α) ⊆ K(β)
Krasner's_lemma
Undecidable decision problem introduced by Emil Post
( i 1 , … , i K ) ↦ α i 1 … α i K {\displaystyle g:(i_{1},\ldots ,i_{K})\mapsto \alpha _{i_{1}}\ldots \alpha _{i_{K}}} h : ( i 1 , … , i K ) ↦ β i 1 …
Post_correspondence_problem
Statistical measure of reliability
Cronbach's alpha (Cronbach's α {\displaystyle \alpha } ) or coefficient alpha (coefficient α {\displaystyle \alpha } ), is a reliability coefficient and
Cronbach's_alpha
Random model in mathematics
X_{n}=x_{n})&={\frac {\prod _{i=1}^{k}\left(\alpha +i-1\right)\times \prod _{i=1}^{n-k}\left(\gamma +i-1\right)}{\prod _{i=1}^{n}\left(\gamma +\alpha +i-1\right)}}\\&={\frac
Pólya_urn_model
Generating function in integrable systems
:= e ∑ i = 1 ∞ t i α k i + γ k e ∑ i = 1 ∞ t i β k i k = 1 , … , N , {\displaystyle y_{k}({\bf {t}}):=e^{\sum _{i=1}^{\infty }t_{i}\alpha _{k}^{i}}+\gamma
Tau function (integrable systems)
Tau_function_(integrable_systems)
Probability distribution
∑ i = 0 α − 1 ( β x ) i i ! e − β x = e − β x ∑ i = α ∞ ( β x ) i i ! . {\displaystyle {\begin{aligned}F(x;\alpha ,\beta )&=1-\sum _{i=0}^{\alpha -1}{\frac
Gamma_distribution
Algorithm for solving the quadratic programming problem from training SVMs
follows: max α ∑ i = 1 n α i − 1 2 ∑ i = 1 n ∑ j = 1 n y i y j K ( x i , x j ) α i α j , {\displaystyle \max _{\alpha }\sum _{i=1}^{n}\alpha _{i}-{\frac {1}{2}}\sum
Sequential minimal optimization
Sequential_minimal_optimization
Constraint equations of a mechanical system
[X_{i}]={\begin{bmatrix}1&0&0&a_{i,i+1}\\0&\cos \alpha _{i,i+1}&-\sin \alpha _{i,i+1}&0\\0&\sin \alpha _{i,i+1}&\cos \alpha _{i,i+1}&0\\0&0&0&1\end{bmatrix}}
Kinematics_equations
Concept in regression analysis mathematics
i = 1 n α i K x i ( x ) , f ∈ H {\textstyle f(x)=\sum _{i=1}^{n}\alpha _{i}K_{x_{i}}(x),\,f\in {\mathcal {H}}} , where all α i {\displaystyle \alpha _{i}}
Regularized_least_squares
Type of complex number
{Q} (\alpha )} can be written as a sum ∑ i = 1 k a i q i {\displaystyle \textstyle \sum _{i=1}^{k}a_{i}q_{i}} for some rational coefficients { q i } {\displaystyle
Algebraic_number
Exterior algebraic map taking tensors from p forms to n-p forms
α i 1 , … , i k d x i 1 ∧ ⋯ ∧ d x i k = ∑ i 1 < ⋯ < i k α i 1 , … , i k d x i 1 ∧ ⋯ ∧ d x i k . {\displaystyle \alpha \ =\ {\frac {1}{k!}}\alpha _{i_{1}
Hodge_star_operator
Statistical model used in time series analysis
− ∑ i = 1 p ′ α i L i ) X t = ( 1 + ∑ i = 1 q θ i L i ) ε t {\displaystyle \left(1-\sum _{i=1}^{p'}\alpha _{i}L^{i}\right)X_{t}=\left(1+\sum _{i=1}^{q}\theta
Autoregressive integrated moving average
Autoregressive_integrated_moving_average
Frame field in general relativity
α V I = ∂ α V I + ω α I J V J . {\displaystyle {\mathcal {D}}_{\alpha }V_{I}=\partial _{\alpha }V_{I}+{\omega _{\alpha I}}^{J}V_{J}.} Where ω α I J {\displaystyle
Tetradic_Palatini_action
α i , j , α i , k , α i , l {\displaystyle \alpha _{i,j},\alpha _{i,k},\alpha _{i,l}} and the respective opposite spherical angles are given by θ i j
Trigonometry_of_a_tetrahedron
About simultaneous modular congruences
condition ∑ i ∈ I α i f i = 0 , {\displaystyle \sum _{i\in I}\alpha _{i}f_{i}=0,} yields ∑ i ∈ I α i F i = 0. {\displaystyle \sum _{i\in I}\alpha _{i}F_{i}=0.}
Chinese_remainder_theorem
Distribution of variables which satisfies a stability property under linear combinations
exp ( i t μ − | c t | α ( 1 − i β sgn ( t ) Φ ) ) {\displaystyle \varphi (t;\alpha ,\beta ,c,\mu )=\exp \left(it\mu -|ct|^{\alpha }\left(1-i\beta \operatorname
Stable_distribution
Assignment of displacement vectors for all points in a region
{\text{or}}\qquad {\frac {\partial u_{i}}{\partial X_{K}}}={\frac {\partial x_{i}}{\partial X_{K}}}-\alpha _{iK}=F_{iK}-\alpha _{iK}} where F {\displaystyle \mathbf
Displacement field (mechanics)
Displacement_field_(mechanics)
Smallest affine subspace that contains a subset
∑ i = 1 k α i x i | k > 0 , x i ∈ S , α i ∈ R , ∑ i = 1 k α i = 1 } . {\displaystyle \operatorname {aff} (S)=\left\{\sum _{i=1}^{k}\alpha _{i}x_{i}\,{\Bigg
Affine_hull
Mathematical notation
{\displaystyle \alpha +\beta =(\alpha _{1}+\beta _{1},\,\alpha _{2}+\beta _{2},\ldots ,\,\alpha _{n}+\beta _{n})} Partial order α ≤ β ⇔ α i ≤ β i ∀ i ∈ { 1 ,
Multi-index_notation
Probability distribution
I α , c I β , a + I a , c 2 I α , α I β , a + 2 I c , c I α , a I α , β I β , a − 2 I a , c I α , c I α , β I β , a + I α , c 2 I β , a 2 − I c , c I
Beta_distribution
_{i=1}^{n}{w_{i}}}}\left|{w_{i}\in W_{\alpha }^{i},\;a_{i}}\right.\in A_{\alpha }^{i},\;i=1,\ldots ,n}\right\}} where W α i = { w | μ W i ( w ) ≥ α } , A α i = {
Ordered_weighted_averaging
Continuous probability distribution
^{\alpha }\sum _{i=0}^{\infty }(-1)^{i}{\begin{pmatrix}\beta -1\\i\end{pmatrix}}\mathrm {e} ^{\alpha i}(i+1)\Gamma \left[-1,\left(i+1\right)\alpha \right]-\mu
Modified Kumaraswamy distribution
Modified_Kumaraswamy_distribution
Quantum state of multiple particles represented as complex matrices
})_{\alpha _{i},(\alpha _{i-1}s_{i})}U_{(\alpha _{i-1}s_{i}),\alpha _{j}}=\sum _{\alpha _{i-1}s_{i}}(A^{s_{i}\dagger })_{\alpha _{i},\alpha _{i-1}}A_{\alpha
Matrix_product_state
Algrabreic search algorithm
_{1}(\alpha ^{i})\,\alpha &+&\lambda _{2}(\alpha ^{i})^{2}\,\alpha ^{2}&+&\cdots &+&\lambda _{t}(\alpha ^{i})^{t}\,\alpha ^{t}\\&=&\gamma _{0,i}&+&\gamma
Chien_search
Method of mathematical differentiation
{\prod _{i}(f_{i}(x))^{\alpha _{i}(x)}} ^{f(x)}\times \overbrace {\sum _{i}\left\{\alpha _{i}'(x)\cdot \ln(f_{i}(x))+\alpha _{i}(x)\cdot {\frac {f_{i
Logarithmic_differentiation
Second-order deterministic global optimization algorithm
i = 1 i = n α i ( x i L − x i ) ( x i U − x i ) {\displaystyle L({\boldsymbol {x}})=f({\boldsymbol {x}})+\sum _{i=1}^{i=n}\alpha _{i}(x_{i}^{L}-x_{i})(x_{i}^{U}-x_{i})}
ΑΒΒ
Direct sum of simple Lie algebras
{\displaystyle [e_{\alpha },f_{\alpha }]=h_{\alpha },[h_{\alpha },e_{\alpha }]=2e_{\alpha },[h_{\alpha },f_{\alpha }]=-2f_{\alpha }} ; i.e., the h α , e α
Semisimple_Lie_algebra
Probability distribution
= ∑ i = 1 n α i λ i e − λ i x = ∑ i = 1 n α i f X i ( x ) , {\displaystyle f(x)=\sum _{i=1}^{n}\alpha _{i}\lambda _{i}e^{-\lambda _{i}x}=\sum _{i=1}^{n}\alpha
Phase-type_distribution
Spline function
i − 1 t i + k − t i B i , k − 1 on [ t r , t s ] , {\displaystyle {\frac {d}{dx}}\sum _{i}\alpha _{i}B_{i,k}=\sum _{i=r-k+2}^{s-1}k{\frac {\alpha _{i}-\alpha
B-spline
Class of numerical techniques
S ] , α i > 0 , α C = ∑ i ∈ { N , E , S , W } α i . {\displaystyle {\begin{bmatrix}&\alpha _{N}\\\alpha _{W}&-\alpha _{C}&\alpha _{E}\\&\alpha _{S}\end{bmatrix}}\
Finite_difference_method
Aspect of probability theory
(\alpha _{1}\lambda ,\alpha _{2}\lambda ,\ldots )\in \mathbb {R} ^{\infty }} (where ∑ i = 1 ∞ α i = 1 {\textstyle \sum _{i=1}^{\infty }\alpha _{i}=1}
Compound_Poisson_distribution
: α i α j + α j α i = 2 δ i j I N , β 2 = I N , α i β + β α i = 0 , 1 ≤ i , j ≤ n . {\displaystyle \alpha ^{i}\alpha ^{j}+\alpha ^{j}\alpha ^{i}=2\delta
Pokhozhaev's_identity
Statistical description for the behavior of fermions
= ∏ i w ( n i , g i ) = ∏ i g i ! n i ! ( g i − n i ) ! . {\displaystyle W=\prod _{i}w(n_{i},g_{i})=\prod _{i}{\frac {g_{i}!}{n_{i}!(g_{i}-n_{i})!}}
Fermi–Dirac_statistics
Relativistic quantum mechanical wave equation
satisfy α i 2 = 1 {\displaystyle \alpha _{i}^{2}=1} and α i α j + α j α i = 0 {\displaystyle \alpha _{i}\alpha _{j}+\alpha _{j}\alpha _{i}=0} if i ≠ j {\displaystyle
Dirac_equation
Mathematical theorem used in cryptography
satisfying g i ( α i ) = 1 {\displaystyle g_{i}(\alpha _{i})=1} and g i ( α j ) = 0 {\displaystyle g_{i}(\alpha _{j})=0} for i ≠ j {\displaystyle i\neq j} :
Normal_basis
Family of solutions to related differential equations
{J_{-\alpha }(x)-e^{-\alpha \pi i}J_{\alpha }(x)}{i\sin \alpha \pi }},\\[5pt]H_{\alpha }^{(2)}(x)&={\frac {J_{-\alpha }(x)-e^{\alpha \pi i}J_{\alpha }(x)}{-i\sin
Bessel_function
= { ∑ i = 1 k α i x i : x i ∈ S , α i ∈ R ≥ 0 , k ∈ N } . {\displaystyle \operatorname {coni} (S)=\left\{\sum _{i=1}^{k}\alpha _{i}x_{i}:x_{i}\in S,\
Conical_combination
Theory of computer sciences
A\alpha } where α {\displaystyle \alpha } is a sequence of nonterminals and terminals . For example, the rule E x p r e s s i o n → E x p r e s s i o
Left_recursion
Generalized method of moments estimator in econometrics
i t = X i t β + α i + u i t {\displaystyle y_{it}=X_{it}\mathbf {\beta } +\alpha _{i}+u_{it}} for t = 1 , … , T {\displaystyle t=1,\ldots ,T} and i =
Arellano–Bond_estimator
Search auction mechanism
bidder i {\displaystyle i} who is allocated to slot i {\displaystyle i} is u i = α i ( v i − p i ) {\displaystyle u_{i}=\alpha _{i}(v_{i}-p_{i})} . The
Generalized second-price auction
Generalized_second-price_auction
= ( i 1 , … , i r ) ∈ N r , and x α = x 1 i 1 ⋯ x r i r , {\displaystyle f(x)=\sum _{\alpha }a_{\alpha }x^{\alpha }{\text{, where }}\alpha =(i_{1},\dots
Quasi-homogeneous_polynomial
Frequencies in coupled oscillators
{\alpha }}_{2}&=i\Delta _{2}\alpha _{2}-\gamma _{2}(\alpha _{2}-\alpha _{2}^{*}e^{2i\omega t})-ig{\tfrac {\omega _{2}}{\omega _{1}}}(\alpha _{1}+\alpha
Antiresonance
Supersymmetric generalization of the Poincaré algebra
Q_{\alpha }^{I}]=0} and { Q α I , Q ¯ α ˙ J } = 2 σ α α ˙ μ P μ δ I J {\displaystyle \{Q_{\alpha }^{I},{\bar {Q}}_{\dot {\alpha }}^{J}\}=2\sigma _{\alpha
Super-Poincaré_algebra
Type of two-dimensional barcode
{\displaystyle g(x)=x^{7}+\alpha ^{87}x^{6}+\alpha ^{229}x^{5}+\alpha ^{146}x^{4}+\alpha ^{149}x^{3}+\alpha ^{238}x^{2}+\alpha ^{102}x+\alpha ^{21}} . This is obtained
QR_code
Statistical test
combination of the explanatory variables: μ i j = μ + α i + β j + γ i j {\displaystyle \mu _{ij}=\mu +\alpha _{i}+\beta _{j}+\gamma _{ij}} , Where: μ {\displaystyle
Two-way_analysis_of_variance
1998 action-adventure video game
the initial invaders he is wounded in the process. He is ready to board Alpha I, the time traveling vehicle developed at Station Omega, when more aliens
Body_Harvest
Mathematical logic hierarchy
each A i {\displaystyle A_{i}} is in Π α i 0 {\displaystyle \mathbf {\Pi } _{\alpha _{i}}^{0}} for some α i < α {\displaystyle \alpha _{i}<\alpha } and
Borel_hierarchy
travel, tourism, insurance
ALPHA I
ALPHA I
Boy/Male
Hindu
First letter of the greek alphabet
Male
African
(ox); the first letter of the Greek alphabet.
Boy/Male
Indian
Faith, Belief, Faith in Allah
Surname or Lastname
Northern Irish, Scottish, and English
Northern Irish, Scottish, and English : variant of Irvin.English : from the Middle English personal name Irwyn, Erwyn, or Everwyn, Old English Eoforwine, composed of the elements eofor ‘wild boar’ + wine ‘friend’.From the Welsh personal name Urien (see Uren).
Girl/Female
Greek American
Firstbom.' The first letter of the Greek alphabet.
Boy/Male
Indian
Intelligent
Girl/Female
American, British, English, Greek
Healer; With Healing Power
Boy/Male
Indian
From isbahan
Girl/Female
English American
Healer.
Boy/Male
Indian
Honor of the religion (Islam)
Boy/Male
Tamil
First letter of the greek alphabet
Girl/Female
Indian
Little
Boy/Male
African, Australian, Chinese, French, Latin, Swedish
First Letter of the Greek Alphabet; Leader
Girl/Female
Indian
Loving
Boy/Male
Indian
Insist, Never gives up
Boy/Male
Indian
A Man of early Islam
Boy/Male
Indian
A prophet, The biblical ishm
Girl/Female
Tamil
Little
Girl/Female
Arabic
Respectable
Girl/Female
Gujarati, Hindu, Indian, Jain, Kannada, Malayalam, Marathi, Oriya, Sanskrit, Tamil, Telugu
Little; Collection of Many Small Things
ALPHA I
ALPHA I
ALPHA I
ALPHA I
ALPHA I
ALPHA I
ALPHA I
travel, tourism, insurance