test_that("Golden Section search finds minimum of quadratic function", {
  f <- function(x) (x - 2)^2 + 1
  res <- sm.golden_section(f, lower = 0, upper = 5, tol = 1e-6, max_iter = 100, verbose = FALSE)

  # Check convergence
  expect_true(res$converged)

  # Check estimated minimum close to true minimum
  expect_equal(res$x_min, 2, tolerance = 1e-5)

  # Check function value at minimum
  expect_equal(res$f_min, 1, tolerance = 1e-5)

  # Iteration data frame should not be empty
  expect_true(nrow(res$iter_df) > 0)
})

# Test 2: Minimum at interval boundary
test_that("Golden Section search handles minimum at boundary", {
  f <- function(x) (x)^2
  res <- sm.golden_section(f, lower = 0, upper = 5, tol = 1e-6, verbose = FALSE)

  expect_true(res$converged)
  expect_equal(res$x_min, 0, tolerance = 1e-5)
  expect_equal(res$f_min, 0, tolerance = 1e-5)
})

# Test 3: Nonlinear function
test_that("Golden Section search works on nonlinear function", {
  f <- function(x) sin(x) + (x - 1)^2
  res <- sm.golden_section(f, lower = 0, upper = 5, tol = 1e-6, verbose = FALSE)

  expect_true(res$converged)
  # Minimum should lie within the interval
  expect_true(res$x_min >= 0 && res$x_min <= 5)
  # Function value should be finite
  expect_true(is.finite(res$f_min))
})

# Test 4: Verbose mode runs without error
test_that("Golden Section search verbose mode runs without error", {
  f <- function(x) (x - 1)^2
  expect_error(res <- sm.golden_section(f, lower = 0, upper = 2, verbose = TRUE), NA)
})