langrage


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Related to langrage: Lagrange, language

lan·grage

 (lăng′grĭj)
n.
A type of shot consisting of scrap iron loaded into a case and formerly used in naval warfare to damage sails and rigging.

[Origin unknown.]

langrage

(ˈlæŋɡrɪdʒ) ,

langrel

or

langridge

n
(Firearms, Gunnery, Ordnance & Artillery) shot consisting of scrap iron packed into a case, formerly used in naval warfare
[C18: of unknown origin]
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References in periodicals archive ?
Using the Langrage function and kernel K([x.sub.m], [x.sub.n]) [less than or equal to] [phi]([x.sub.m]), [phi]([x.sub.n]) > 0, we could get the dual form of problem (2):
We set the robot's execution time constraint t = 60s, the initial Langrage multiplier [[lambda].sup.0] = (0, 0,0), the initial penalty factor [r.sup.0] = (1000,1000, ..., 1000), the convergence accuracy [epsilon] = 0.0001, the constraint error accuracy [[epsilon].sub.g] = 0.0001, the Lagrange multiplier update maximal times [v.sub.max] = 100, the maximum number of iterations in the CPSO [k.sub.max] = 100, the swarm particle number [N.sub.d,max] = 40, and the elastic coefficient [alpha] = 100.
The result of Jarque-Bera (JB) normality test show that at 5% alpha value, the value of Langrage Multiplier (LM) statistics is 0.214118 with a corresponding probability value of 0.898472.
Next, a Breusch-Pagan Langrage Multiplier test was performed to know whether pooled OLS or random effect panel model shall be used.
In this case, we follow Tichy [39] and use the Breusch-Pagan Langrage multiplier test.
An in-depth examination of these results using the Langrage Multiplier test (LM test) showed that many changes in model parameters were suggested in order to reduce model discrepancy, reduce the chi-square value, and, therefore, achieve adequate RMSEA and SRMR indexes (for more information on the mathematical approach behind the SRMR and RMSEA, see Brown, 2006).
Accordingly, the items that presented Langrage Multipliers (LM) > 11 (p < 0.001), suggesting correlation between errors of measurement, were excluded.
t = y + b * j = f (0) + [f.sup.-] (0) j mod q by using Langrage equation to [d.sub.i].
Modification indices were calculated (Multiplier Langrage), which indicated that the model improved significantly when direct connections between task climate and discipline, and ego climate and indiscipline were considered.