OPTIMASI

NIM : 230605110077

DOSEN PENGAMPU : Prof. Dr. SUHARTONO, M.Kom

LEMBAGA : UIN MAULANA MALIK IBRAHIM MALANG

library(mosaicCalc)

## Loading required package: mosaic

## Registered S3 method overwritten by 'mosaic':
##   method                           from   
##   fortify.SpatialPolygonsDataFrame ggplot2

## 
## The 'mosaic' package masks several functions from core packages in order to add 
## additional features.  The original behavior of these functions should not be affected by this.

## 
## Attaching package: 'mosaic'

## The following objects are masked from 'package:dplyr':
## 
##     count, do, tally

## The following object is masked from 'package:Matrix':
## 
##     mean

## The following object is masked from 'package:ggplot2':
## 
##     stat

## The following objects are masked from 'package:stats':
## 
##     binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
##     quantile, sd, t.test, var

## The following objects are masked from 'package:base':
## 
##     max, mean, min, prod, range, sample, sum

## Loading required package: mosaicCore

## 
## Attaching package: 'mosaicCore'

## The following objects are masked from 'package:dplyr':
## 
##     count, tally

## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
##      (status 2 uses the sf package in place of rgdal)

## 
## Attaching package: 'mosaicCalc'

## The following object is masked from 'package:stats':
## 
##     D

h <- rfun(~ x, seed=7293)
argM(h(x) ~ x, bounds(x=-5:5))

## # A tibble: 2 × 3
##        x .output. concavity
##    <dbl>    <dbl>     <dbl>
## 1 -1.68      1.93         1
## 2  0.173     8.25        -1

## # A tibble: 2 × 3
##        x .output. concavity
##    <dbl>    <dbl>     <dbl>
## 1 -1.68      1.93         1
## 2  0.173     8.25        -1

better <- makeFun((guess + x/guess)/2 ~ guess)

better(1, x=55)

## [1] 28

## [1] 28

better(28, x=55)

## [1] 14.98214

## [1] 14.98214

better(14.98214, x=55)

## [1] 9.326589

## [1] 9.326589
better(9.326589, x=55)

## [1] 7.611854

## [1] 7.611854
better(7.611854, x=55)

## [1] 7.418713

## [1] 7.418713
better(7.418713, x=55)

## [1] 7.416199

## [1] 7.416199
better(7.416199, x=55)

## [1] 7.416198

## [1] 7.416198

7.416198^2

## [1] 54.99999

## [1] 54.99999

# Install dan memuat paket ggplot2 jika belum terinstal
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
library(ggplot2)

# Membuat data frame untuk menyimpan hasil iterasi
results <- data.frame(
  Iteration = 1:8,
  Guess = c(1, 28, 14.98214, 9.326589, 7.611854, 7.418713, 7.416199, 7.416198),
  Squared = c(1^2, 28^2, 14.98214^2, 9.326589^2, 7.611854^2, 7.418713^2, 7.416199^2, 7.416198^2)
)

# Membuat grafik dengan ggplot2
ggplot(results, aes(x = Iteration, y = Guess)) +
  geom_line() +
  geom_point() +
  labs(title = "Iterative Square Root Approximation",
       x = "Iteration",
       y = "Guess") +
  theme_minimal()