Sentence examples for be a penalty function from inspiring English sources

The function, (2.5). is said to be a penalty function of the problem (2.2).

where ρ is a penalty function such as the ℓ 1 norm.

where l logK is a penalty function that prevents the GLRT statistic to monotically increase with increasing model orders.

The first term is the ML term for data encoding, and the second term is a penalty function that inhibits the number of free parameters of the model to become very large.

These requirements lead us to minimize the penalized log-likelihood function (3) where ln L is the log-likelihood function in (6), Q is a penalty function against the roughness of the ϕ-functions, and τ = (w1, w2) is a set of the weights for tuning parameters (hyper-parameters).

where s q j ( t ) is the score for RB q for user j at TS t, E k j ( t ) is the energy metric evaluated for user j on RB k at t, M is the total number of RBs and f j (m j ) is a penalty function based on the number of already allocated RBs for the user.

The recently proposed angle penalized distance (APD) in RVEA [21] adopts the acute angle between the reference vector and solution vectors to replace the Euclidean distance as shown in Eq. (5), where (p alpha )) is a penalty function related to the angle (alpha ).

More precisely, we define m_{i} u)=Ebigl{ v^{T_{i}}omega bigl(U(T_{i}-1),biglvert U(T_{i} bigrvert bigr)I(T_{i}< infty)vert u(0)=ubigr},quad uin mathbb{N}, i=1,2, (1.3) where (0< vleq1) is the discount factor, (omega:mathbb{N}timesmathbb {N}^rightarrowmathbb{N}) is a penalty function, (I(A)) is the indicator function of an event A, and (T_{i}), (i=1,2), is the time to ruin for Class i.

^ ) ) + ω 2 | d | τ | d | (r 0 ) p | d | (c | ⋅ ) = 1 ∕ (1 + e x p (− y | d | ) ) ω { 0, 1 } | d | are parameters that affect the scaling and offset of the log probability, whereas ω 2 | d | τ | d | (r 0 ) is a penalty function that ensures that the DoG value at the center of the nucleus is above a certain baseline value.

Then, the penalized estimator β ^ is a solution to the regularization problem (5) β ^ = arg ⁡ min ⁡ β ∈ R p { Q ≡ L + ∑ j = 1 p p λ (| β j | ) }, where L is the likelihood of beta for additive model, p λ, θ ≥ 0 is a penalty function based on the regularization parameter λ ≥ 0 and is often rewritten as p λ [ 4].

The loading ω k, k ≥ 1, is obtained as γ / ‖ γ ‖ which together with α minimizes (2) ‖ γ − X ˜ k T Y Y T X ˜ k α ‖ 2 * g λ,     subject to ‖ α ‖ = 1, where g λ is a penalty function defined by a proper regularization on γ with tuning parameter λ.