Answer:
<h2>Option A = 1*3 = 3 unit² </h2>
<h3>Distance between f and G = distance between E and H </h3>
F = (3,7)
G = (6,4)
H = (5,3)
E= (2,6)
= (3 units)
<h3>
Distance between FE = GH </h3>
= 1 units
Area = 3* 1
= 3 units²
In monetary terms, you're making change. If you need more ones, you trade one 10 for 10 ones.
When I was in school, many years ago, we used small superscript-type numbers to show this:
7 3 0 ⇒ 7 2 ¹0 . . . . regrouping to gain 10 ones
7 2 ¹0 ⇒ 6 ¹2 ¹0 . . .regrouping again to gain 10 more tens
You'd have to look at the example problems that precede this page in order to see what the meaning of "magnifying glass" is.
Answer:
x = 47°
Step-by-step explanation:
Since the two angles on a line add up to 180 degrees:
x = 180-133
x = 47°
All are relations except for relation 3
Distributionally robust stochastic programs with side information based on trimmings
This is a research paper whose authors are Adrián Esteban-Pérez and Juan M. Morales.
Abstract:
- We look at stochastic programmes that are conditional on some covariate information, where the only knowledge of the possible relationship between the unknown parameters and the covariates is a limited data sample of their joint distribution. We build a data-driven Distributionally Robust Optimization (DRO) framework to hedge the decision against the inherent error in the process of inferring conditional information from limited joint data by leveraging the close relationship between the notion of trimmings of a probability measure and the partial mass transportation problem.
- We demonstrate that our technique is computationally as tractable as the usual (no side information) Wasserstein-metric-based DRO and provides performance guarantees. Furthermore, our DRO framework may be easily applied to data-driven decision-making issues involving tainted samples. Finally, using a single-item newsvendor problem and a portfolio allocation problem with side information, the theoretical findings are presented.
Conclusions:
- We used the relationship between probability reductions and partial mass transit in this study to give a straightforward, yet powerful and creative technique to expand the usual Wasserstein-metric-based DRO to the situation of conditional stochastic programming. In the process of inferring the conditional probability measure of the random parameters from a limited sample drawn from the genuine joint data-generating distribution, our technique generates judgments that are distributionally resilient to uncertainty. In a series of numerical tests based on the single-item newsvendor issue and a portfolio allocation problem, we proved that our strategy achieves much higher out-of-sample performance than several current options. We backed up these actual findings with theoretical analysis, demonstrating that our strategy had appealing performance guarantees.
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