Adding to the complexity, tasks are not linear: the foundation of the home can be started before the full design of the kitchen is complete, for instance.
In this method, the constrained non-linear optimization task is transformed into an unconstrained problem by adding a penalty term to the Lagrangian function as follows: ϕ ( Γ, p, μ, ϵ ) = L ( Γ, p, μ ) + ϵ ∑ k c k ( γ k tgt ) − c m 2 = ∑ k P k + μ ∑ k c k ( γ k tgt ) − c m + ϵ ∑ k c k ( γ k tgt ) − c m 2, where μ is the Lagrange multiplier and ϵ is the so called penalty parameter.
Optimal linear estimators for this task are given by (4) and (9).
Taking account of a two-class linear classifier problem, the task is praised as "optimal" separating hyper plane Fig. 1 Schematic diagram of linear classification wx+b=0 (1).
In order to examine the reasoning processes, we used linear syllogism (linear order) tasks, which are classified as a "nonmatch" order type (Foos et al. 1976).
Searching for the proper object context (pc.submit object, …) on the host executing the submitted task is linear in the number of objects of that type.
The energy consumption of a task is linear, and every task consumes E i /C i energy units per time unit.
The behavior in the task was linear for intermediate morphs but essentially categorical for the morphs most similar to the choice images.
Workflow modelling is a linear task-oriented approach that can be used to represent the day-to-day performance of workflow functions and to assist with work process redesign [ 16].
The statistical method used for this task is a multiple linear regressionmultiple linear regression that uses as input the following variables: number of Facebook shares, number of tweets and retweets, entropy of tweet vocabulary, and the mean number of followers sharing the articles on Twitter.
Neurophysiological responses recorded in a visual lexical decision task were submitted to linear regression analysis.
