Theory Ordinary_Differential_Equations.ODE_Auxiliarities

section ‹Auxiliary Lemmas›
theory ODE_Auxiliarities
imports
  "HOL-Analysis.Analysis"
  "HOL-Library.Float"
  "List-Index.List_Index"
  Affine_Arithmetic.Affine_Arithmetic_Auxiliarities
  Affine_Arithmetic.Executable_Euclidean_Space
begin

instantiation prod :: (zero_neq_one, zero_neq_one) zero_neq_one
begin

definition "1 = (1, 1)"

instance by standard (simp add: zero_prod_def one_prod_def)
end

subsection ‹there is no inner product for type @{typ "'a L 'b"}

lemma (in real_inner) parallelogram_law: "(norm (x + y))2 + (norm (x - y))2 = 2 * (norm x)2 + 2 * (norm y)2"
proof -
  have "(norm (x + y))2 + (norm (x - y))2 = inner (x + y) (x + y) + inner (x - y) (x - y)"
    by (simp add: norm_eq_sqrt_inner)
  also have " = 2 * (norm x)2 + 2 * (norm y)2"
    by (simp add: algebra_simps norm_eq_sqrt_inner)
  finally show ?thesis .
qed

locale no_real_inner
begin

lift_definition fstzero::"(real*real) L (real*real)" is "λ(x, y). (x, 0)"
  by (auto intro!: bounded_linearI')

lemma [simp]: "fstzero (a, b) = (a, 0)"
  by transfer simp

lift_definition zerosnd::"(real*real) L (real*real)" is "λ(x, y). (0, y)"
  by (auto intro!: bounded_linearI')

lemma [simp]: "zerosnd (a, b) = (0, b)"
  by transfer simp

lemma fstzero_add_zerosnd: "fstzero + zerosnd = id_blinfun"
  by transfer auto

lemma norm_fstzero_zerosnd: "norm fstzero = 1" "norm zerosnd = 1" "norm (fstzero - zerosnd) = 1"
  by (rule norm_blinfun_eqI[where x="(1, 0)"]) (auto simp: norm_Pair blinfun.bilinear_simps
    intro: norm_blinfun_eqI[where x="(0, 1)"] norm_blinfun_eqI[where x="(1, 0)"])

text ‹compare with @{thm parallelogram_law}

lemma "(norm (fstzero + zerosnd))2 + (norm (fstzero - zerosnd))2 
    2 * (norm fstzero)2 + 2 * (norm zerosnd)2"
  by (simp add: fstzero_add_zerosnd norm_fstzero_zerosnd)

end

subsection ‹Topology›

subsection ‹Vector Spaces›

lemma ex_norm_eq_1: "x. norm (x::'a::{real_normed_vector, perfect_space}) = 1"
  by (metis vector_choose_size zero_le_one)

subsection ‹Reals›

subsection ‹Balls›

text ‹sometimes @{thm mem_ball} etc. are not good [simp]› rules (although they are often useful):
  not sure that inequalities are ``simpler'' than set membership (distorts automatic reasoning
  when only sets are involved)›
lemmas [simp del] = mem_ball mem_cball mem_sphere mem_ball_0 mem_cball_0


subsection ‹Boundedness›

lemma bounded_subset_cboxE:
  assumes "i. i  Basis  bounded ((λx. x  i) ` X)"
  obtains a b where "X  cbox a b"
proof -
  have "i. i  Basis  a b. ((λx. x  i) ` X)  {a..b}"
    by (metis box_real(2) box_subset_cbox subset_trans bounded_subset_box_symmetric[OF assms] )
  then obtain a b where bnds: "i. i  Basis  ((λx. x  i) ` X)  {a i .. b i}" 
    by metis
  then have "X  {x. iBasis. x  i  {a i .. b i}}"
    by force
  also have " = cbox (iBasis. a i *R i) (iBasis. b i *R i)"
    by (auto simp: cbox_def)
  finally show ?thesis ..
qed

lemma
  bounded_euclideanI:
  assumes "i. i  Basis  bounded ((λx. x  i) ` X)"
  shows "bounded X"
proof -
  from bounded_subset_cboxE[OF assms] obtain a b where "X  cbox a b" .
  with bounded_cbox show ?thesis by (rule bounded_subset)
qed

subsection ‹Intervals›

notation closed_segment ((1{_--_}))
notation open_segment ((1{_<--<_}))

lemma min_zero_mult_nonneg_le: "0  h'  h'  h  min 0 (h * k::real)  h' * k"
  by (metis dual_order.antisym le_cases min_le_iff_disj mult_eq_0_iff mult_le_0_iff
      mult_right_mono_neg)

lemma max_zero_mult_nonneg_le: "0  h'  h'  h  h' * k  max 0 (h * k::real)"
  by (metis dual_order.antisym le_cases le_max_iff_disj mult_eq_0_iff mult_right_mono
      zero_le_mult_iff)

lemmas closed_segment_eq_real_ivl = closed_segment_eq_real_ivl

lemma bdd_above_is_intervalI: "bdd_above I" if "is_interval I" "a  b" "a  I" "b  I" for I::"real set"
  by (meson bdd_above_def is_interval_1 le_cases that)

lemma bdd_below_is_intervalI: "bdd_below I" if "is_interval I" "a  b" "a  I" "b  I" for I::"real set"
  by (meson bdd_below_def is_interval_1 le_cases that)


subsection ‹Extended Real Intervals›

subsection ‹Euclidean Components›

subsection ‹Operator Norm›

subsection ‹Limits›

lemma eventually_open_cball:
  assumes "open X"
  assumes "x  X"
  shows "eventually (λe. cball x e  X) (at_right 0)"
proof -
  from open_contains_cball_eq[OF assms(1)] assms(2)
  obtain e where "e > 0" "cball x e  X" by auto
  thus ?thesis
    by (auto simp: eventually_at dist_real_def mem_cball intro!: exI[where x=e])
qed

subsection ‹Continuity›

subsection ‹Derivatives›

lemma
  if_eventually_has_derivative:
  assumes "(f has_derivative F') (at x within S)"
  assumes "F x in at x within S. P x" "P x" "x  S"
  shows "((λx. if P x then f x else g x) has_derivative F') (at x within S)"
  using assms(1)
  apply (rule has_derivative_transform_eventually)
  subgoal using assms(2) by eventually_elim auto
  by (auto simp: assms)


lemma norm_le_in_cubeI: "norm x  norm y"
  if "i. i  Basis  abs (x  i)  abs (y  i)" for x y
  unfolding norm_eq_sqrt_inner
  apply (subst euclidean_inner)
  apply (subst (3) euclidean_inner)
  using that
  by (auto intro!: sum_mono simp: abs_le_square_iff power2_eq_square[symmetric])

lemma has_derivative_partials_euclidean_convexI:
  fixes f::"'a::euclidean_space  'b::real_normed_vector"
  assumes f': "i x xi. i  Basis  (jBasis. x  j  X j)  xi = x  i 
    ((λp. f (x + (p - x  i) *R i)) has_vector_derivative f' i x) (at xi within X i)"
  assumes df_cont: "i. i  Basis  (f' i  (f' i x)) (at x within {x. jBasis. x  j  X j})"
  assumes "i. i  Basis  x  i  X i"
  assumes "i. i  Basis  convex (X i)"
  shows "(f has_derivative (λh. jBasis. (h  j) *R f' j x)) (at x within {x. jBasis. x  j  X j})"
    (is "_ (at x within ?S)")
proof (rule has_derivativeI)
  show "bounded_linear (λh. jBasis. (h  j) *R f' j x)"
    by (auto intro!: bounded_linear_intros)

  obtain E where [simp]: "set E = (Basis::'a set)" "distinct E"
    using finite_distinct_list[OF finite_Basis] by blast

  have [simp]: "length E = DIM('a)"
    using distinct E distinct_card by fastforce
  have [simp]: "E ! j  Basis" if "j < DIM('a)" for j
    by (metis length E = DIM('a) set E = Basis nth_mem that)
  have "convex ?S"
    by (rule convex_prod) (use assms in auto)

  have sum_Basis_E: "sum g Basis = (j<DIM('a). g (E ! j))" for g
    apply (rule sum.reindex_cong[OF _ _ refl])
    apply (auto simp: inj_on_nth)
    by (metis distinct E length E = DIM('a) set E = Basis bij_betw_def bij_betw_nth)

  have segment: "F x' in at x within ?S. x'  ?S" "F x' in at x within ?S. x'  x"
    unfolding eventually_at_filter by auto


  show "((λy. (f y - f x - (jBasis. ((y - x)  j) *R f' j x)) /R norm (y - x))  0) (at x within {x. jBasis. x  j  X j})"
    apply (rule tendstoI)
    unfolding norm_conv_dist[symmetric]
  proof -
    fix e::real
    assume "e > 0"
    define B where "B = e / norm (2*DIM('a) + 1)"
    with e > 0 have B_thms: "B > 0" "2 * DIM('a) * B < e" "B  0"
      by (auto simp: divide_simps algebra_simps B_def)
    define B' where "B' = B / 2"
    have "B' > 0" by (simp add: B'_def 0 < B)
    have "i  Basis. F xa in at x within {x. jBasis. x  j  X j}. dist (f' i xa) (f' i x) < B'"
      apply (rule ballI)
      subgoal premises prems using df_cont[OF prems, THEN tendstoD, OF 0 < B'] .
      done
    from eventually_ball_finite[OF finite_Basis this]
    have "F x' in at x within {x. jBasis. x  j  X j}. jBasis. dist (f' j x') (f' j x) < B'" .
    then obtain d where "d > 0"
      and "x' j. x'  {x. jBasis. x  j  X j}  x'  x  dist x' x < d  j  Basis  dist (f' j x') (f' j x) < B'"
      using 0 < B'
      by (auto simp: eventually_at)
    then have B': "x'  {x. jBasis. x  j  X j}  dist x' x < d  j  Basis  dist (f' j x') (f' j x) < B'" for x' j
      by (cases "x' = x", auto simp add: 0 < B')
    then have B: "norm (f' j x' - f' j y) < B" if
      "(j. j  Basis  x'  j  X j)"
      "(j. j  Basis  y  j  X j)"
      "dist x' x < d"
      "dist y x < d"
      "j  Basis"
      for x' y j
    proof -
      have "dist (f' j x') (f' j x) < B'" "dist (f' j y) (f' j x) < B'"
        using that
        by (auto intro!: B')
      then have "dist (f' j x') (f' j y) < B' + B'" by norm
      also have " = B" by (simp add: B'_def)
      finally show ?thesis by (simp add: dist_norm)
    qed
    have "F x' in at x within {x. jBasis. x  j  X j}. dist x' x < d"
      by (rule tendstoD[OF tendsto_ident_at d > 0])
    with segment
    show "F x' in at x within {x. jBasis. x  j  X j}.
      norm ((f x' - f x - (jBasis. ((x' - x)  j) *R f' j x)) /R norm (x' - x)) < e"
    proof eventually_elim
      case (elim x')
      then have os_subset: "open_segment x x'  ?S"
        using convex ?S assms(3)
        unfolding convex_contains_open_segment
        by auto
      then have cs_subset: "closed_segment x x'  ?S"
        using elim assms(3) by (auto simp add: open_segment_def)
      have csc_subset: "closed_segment (x'  i) (x  i)  X i" if i: "i  Basis" for i
        apply (rule closed_segment_subset)
        using cs_subset elim assms(3,4) that
        by (auto )
      have osc_subset: "open_segment (x'  i) (x  i)  X i" if i: "i  Basis" for i
        using segment_open_subset_closed csc_subset[OF i]
        by (rule order_trans)

      define h where "h = x' - x"
      define z where "z j = (k<j. (h  E ! k) *R (E ! k))" for j
      define g where "g j t = (f (x + z j + (t - x  E ! j) *R E ! j))" for j t
      have z: "z j  E ! j = 0" if "j < DIM('a)" for j
        using that
        by (auto simp: z_def h_def algebra_simps inner_sum_left inner_Basis if_distrib
            nth_eq_iff_index_eq
            sum.delta
            intro!: euclidean_eqI[where 'a='a]
            cong: if_cong)
      from distinct_Ex1[OF distinct E, unfolded set E = Basis Ex1_def length E = _]
      obtain index where
        index: "i. i  Basis  i = E ! index i" "i. i  Basis  index i < DIM('a)"
        and unique: "i j. i  Basis  j < DIM('a)  E ! j = i  j = index i"
        by metis
      have nth_eq_iff_index: "E ! k = i  index i = k" if "i  Basis" "k < DIM('a)" for k i
        using unique[OF that] index[OF i  Basis]
        by auto
      have z_inner: "z j  i = (if j  index i then 0 else h  i)" if "j < DIM('a)" "i  Basis" for j i
        using that index[of i]
        by (auto simp: z_def h_def algebra_simps inner_sum_left inner_Basis if_distrib
            sum.delta nth_eq_iff_index
            intro!: euclidean_eqI[where 'a='a]
            cong: if_cong)
      have z_mem: "j < DIM('a)  ja  Basis  x  ja + z j  ja  X ja" for j ja
        using csc_subset
        by (auto simp: z_inner h_def algebra_simps)
      have "norm (x' - x) < d"
        using elim by (simp add: dist_norm)
      have norm_z': "y  closed_segment (x  E ! j) (x'  E ! j)  norm (z j + y *R E ! j - (x  E ! j) *R E ! j) < d"
        if "j < DIM('a)"
        for j y
        apply (rule le_less_trans[OF _ norm (x' - x) < d])
        apply (rule norm_le_in_cubeI)
        apply (auto simp: inner_diff_left inner_add_left inner_Basis that z)
        subgoal by (auto simp: closed_segment_eq_real_ivl split: if_splits)
        subgoal for i
          using that
          by (auto simp: z_inner h_def algebra_simps)
        done
      have norm_z: "norm (z j) < d" if "j < DIM('a)" for j
        using norm_z'[OF that ends_in_segment(1)]
        by (auto simp: z_def)
      {
        fix j
        assume j: "j < DIM('a)"
        have eq: "(x + z j + ((y - (x + z j))  E ! j) *R E ! j +
          (p - (x + z j + ((y - (x + z j))  E ! j) *R E ! j)  E ! j) *R
          E ! j) = (x + z j + (p - (x  E ! j)) *R E ! j)" for y p
          by (auto simp: algebra_simps j z)
        have f_has_derivative: "((λp. f (x + z j + (p - x  E ! j) *R E ! j)) has_derivative (λxa. xa *R f' (E ! j) (x + z j + ((y *R E ! j - (x + z j))  E ! j) *R E ! j)))
            (at y within closed_segment (x  E ! j) (x'  E ! j))"
          if "y  closed_segment (x  E ! j) (x'  E ! j)"
          for y
          apply (rule has_derivative_subset)
           apply (rule f'[unfolded has_vector_derivative_def,
                where x= "x + z j + ((y *R E!j - (x + z j)) E!j) *R E ! j" and i="E ! j", unfolded eq])
          subgoal by (simp add: j)
          subgoal
            using that
            apply (auto simp: algebra_simps z j inner_Basis)
            using closed_segment_commute E ! j  Basis csc_subset apply blast
            by (simp add: z_mem j)
          subgoal by (auto simp: algebra_simps z j inner_Basis)
          subgoal
            apply (auto simp: algebra_simps z j inner_Basis)
            using closed_segment_commute j. j < DIM('a)  E ! j  Basis csc_subset j apply blast
            done
          done
        have *: "((xa *R E ! j - (x + z j))  E ! j) = xa - x  E ! j" for xa
          by (auto simp: algebra_simps z j)
        have g': "(g j has_vector_derivative (f' (E ! j) (x + z j + (xa - x  E ! j) *R E ! j)))
          (at xa within (closed_segment (xE!j) (x'E!j)))"
          (is "(_ has_vector_derivative ?g' j xa) _")
          if "xa  closed_segment (xE!j) (x'E!j)" for xa
          using that
          by (auto simp: has_vector_derivative_def g_def[abs_def] *
              intro!: derivative_eq_intros f_has_derivative[THEN has_derivative_eq_rhs])
        define g' where "g' j = ?g' j" for j
        with g' have g': "(g j has_vector_derivative g' j t) (at t within closed_segment (xE!j) (x'E!j))"
          if "t  closed_segment (xE!j) (x'E!j)"
          for t
          by (simp add: that)
        have cont_bound: "y. yclosed_segment (x  E ! j) (x'  E ! j)  norm (g' j y - g' j (x  E ! j))  B"
          apply (auto simp add: g'_def j algebra_simps inner_Basis z dist_norm
              intro!: less_imp_le B z_mem norm_z norm_z')
          using closed_segment_commute j. j < DIM('a)  E ! j  Basis csc_subset j apply blast
          done
        from vector_differentiable_bound_linearization[OF g' order_refl cont_bound ends_in_segment(1)]
        have n: "norm (g j (x'  E ! j) - g j (x  E ! j) - (x'  E ! j - x  E ! j) *R g' j (x  E ! j))  norm (x'  E ! j - x  E ! j) * B"
          .
        have "z (Suc j) = z j + (x'  E ! j - x  E ! j) *R E ! j"
          by (auto simp: z_def h_def algebra_simps)
        then have "f (x + z (Suc j)) = f (x + z j + (x'  E ! j - x  E ! j) *R E ! j) "
          by (simp add: ac_simps)
        with n have "norm (f (x + z (Suc j)) - f (x + z j) - (x'  E ! j - x  E ! j) *R f' (E ! j) (x + z j))  ¦x'  E ! j - x  E ! j¦ * B"
          by (simp add: g_def g'_def)
      } note B_le = this
      have B': "norm (f' (E ! j) (x + z j) - f' (E ! j) x)  B" if "j < DIM('a)" for j
        using that assms(3)
        by (auto simp add: algebra_simps inner_Basis z dist_norm 0 < d
            intro!: less_imp_le B z_mem norm_z)
      have "(jDIM('a) - 1. f (x + z j) - f (x + z (Suc j))) = f (x + z 0) - f (x + z (Suc (DIM('a) - 1)))"
        by (rule sum_telescope)
      moreover have "z DIM('a) = h"
        using index
        by (auto simp: z_def h_def algebra_simps inner_sum_left inner_Basis if_distrib
            nth_eq_iff_index 
            sum.delta 
            intro!: euclidean_eqI[where 'a='a]
            cong: if_cong)
      moreover have "z 0 = 0"
        by (auto simp: z_def)
      moreover have "{..DIM('a) - 1} = {..<DIM('a)}"
        using le_imp_less_Suc by fastforce
      ultimately have "f x - f (x + h) = (j<DIM('a). f (x + z j) - f (x + z (Suc j)))"
        by auto
      then have "norm (f (x + h) - f x - (jBasis. ((x' - x)  j) *R f' j x)) =
        norm(
          (j<DIM('a). f (x + z (Suc j)) - f (x + z j) - (x'  E ! j - x  E ! j) *R f' (E ! j) (x + z j)) +
          (j<DIM('a). (x'  E ! j - x  E ! j) *R (f' (E ! j) (x + z j) - f' (E ! j) x)))"
        (is "_ = norm (sum ?a ?E + sum ?b ?E)")
        by (intro arg_cong[where f=norm]) (simp add: sum_negf sum_subtractf sum.distrib algebra_simps sum_Basis_E)
      also have "  norm (sum ?a ?E) + norm (sum ?b ?E)" by (norm)
      also have "norm (sum ?a ?E)  sum (λx. norm (?a x)) ?E"
        by (rule norm_sum)
      also have "  sum (λj. norm ¦x'  E ! j - x  E ! j¦ * B) ?E"
        by (auto intro!: sum_mono B_le)
      also have "  sum (λj. norm (x' - x) * B) ?E"
        apply (rule sum_mono)
        apply (auto intro!: mult_right_mono 0  B)
        by (metis (full_types) j. j < DIM('a)  E ! j  Basis inner_diff_left norm_bound_Basis_le order_refl)
      also have " = norm (x' - x) * DIM('a) * B"
        by simp
      also have "norm (sum ?b ?E)  sum (λx. norm (?b x)) ?E"
        by (rule norm_sum)
      also have "  sum (λj. norm (x' - x) * B) ?E"
        apply (intro sum_mono)
        apply (auto intro!: mult_mono B')
         apply (metis (full_types) j. j < DIM('a)  E ! j  Basis inner_diff_left norm_bound_Basis_le order_refl)
        done
      also have " = norm (x' - x) * DIM('a) * B"
        by simp
      finally have "norm (f (x + h) - f x - (jBasis. ((x' - x)  j) *R f' j x)) 
          norm (x' - x) * real DIM('a) * B + norm (x' - x) * real DIM('a) * B"
        by arith
      also have " /R norm (x' - x)  norm (2 * DIM('a) * B)"
        using B  0
        by (simp add: divide_simps abs_mult)
      also have " < e" using B_thms by simp
      finally show ?case by (auto simp: divide_simps abs_mult h_def)
    qed
  qed
qed

lemma
  frechet_derivative_equals_partial_derivative:
  fixes f::"'a::euclidean_space  'a"
  assumes Df: "x. (f has_derivative Df x) (at x)"
  assumes f': "((λp. f (x + (p - x  i) *R i)  b) has_real_derivative f' x i b) (at (x  i))"
  shows "Df x i  b = f' x i b"
proof -
  define Dfb where "Dfb x = Blinfun (Df x)" for x
  have Dfb_apply: "blinfun_apply (Dfb x) = Df x" for x
    unfolding Dfb_def
    apply (rule bounded_linear_Blinfun_apply)
    apply (rule has_derivative_bounded_linear)
    apply (rule assms)
    done
  have "Dfb x = blinfun_of_matrix (λi b. Dfb x b  i)" for x
    using blinfun_of_matrix_works[of "Dfb x"] by auto
  have Dfb: "x. (f has_derivative Dfb x) (at x)"
    by (auto simp: Dfb_apply Df)
  note [derivative_intros] = diff_chain_at[OF _ Dfb, unfolded o_def]
  have "((λp. f (x + (p - x  i) *R i)  b) has_real_derivative Dfb x i  b) (at (x  i))"
    by (auto intro!: derivative_eq_intros ext simp: has_field_derivative_def blinfun.bilinear_simps)
  from DERIV_unique[OF f' this]
  show ?thesis by (simp add: Dfb_apply)
qed


subsection ‹Integration›

lemmas content_real[simp]
lemmas integrable_continuous[intro, simp]
  and integrable_continuous_real[intro, simp]


lemma integral_eucl_le:
  fixes f g::"'a::euclidean_space  'b::ordered_euclidean_space"
  assumes "f integrable_on s"
    and "g integrable_on s"
    and "x. x  s  f x  g x"
  shows "integral s f  integral s g"
  using assms
  by (auto intro!: integral_component_le simp: eucl_le[where 'a='b])

lemma
  integral_ivl_bound:
  fixes l u::"'a::ordered_euclidean_space"
  assumes "x h'. h'  {t0 .. h}  x  {t0 .. h}  (h' - t0) *R f x  {l .. u}"
  assumes "t0  h"
  assumes f_int: "f integrable_on {t0 .. h}"
  shows "integral {t0 .. h} f  {l .. u}"
proof -
  from assms(1)[of t0 t0] assms(2) have "0  {l .. u}" by auto
  have "integral {t0 .. h} f = integral {t0 .. h} (λt. if t  {t0, h} then 0 else f t)"
    by (rule integral_spike[where S="{t0, h}"]) auto
  also
  {
    fix x
    assume 1: "x  {t0 <..< h}"
    with assms have "(h - t0) *R f x  {l .. u}" by auto
    then have "(if x  {t0, h} then 0 else f x)  {l /R (h - t0) .. u /R (h - t0)}"
      using x  _
      by (auto simp: inverse_eq_divide
        simp: eucl_le[where 'a='a] field_simps algebra_simps)
  }
  then have "  {integral {t0..h} (λ_. l /R (h - t0)) .. integral {t0..h} (λ_. u /R (h - t0))}"
    unfolding atLeastAtMost_iff
    apply (safe intro!: integral_eucl_le)
    using 0  {l .. u}
    apply (auto intro!: assms
      intro: integrable_continuous_real  integrable_spike[where S="{t0, h}", OF f_int]
      simp: eucl_le[where 'a='a] divide_simps
      split: if_split_asm)
    done
  also have "  {l .. u}"
    using assms 0  {l .. u}
    by (auto simp: inverse_eq_divide)
  finally show ?thesis .
qed

lemma
  add_integral_ivl_bound:
  fixes l u::"'a::ordered_euclidean_space"
  assumes "x h'. h'  {t0 .. h}  x  {t0 .. h}  (h' - t0) *R f x  {l - x0 .. u - x0}"
  assumes "t0  h"
  assumes f_int: "f integrable_on {t0 .. h}"
  shows "x0 + integral {t0 .. h} f  {l .. u}"
  using integral_ivl_bound[OF assms]
  by (auto simp: algebra_simps)

subsection ‹conditionally complete lattice›


subsection ‹Lists›

lemma
  Ball_set_Cons[simp]: "(aset_Cons x y. P a)  (ax. by. P (a#b))"
  by (auto simp: set_Cons_def)

lemma set_cons_eq_empty[iff]: "set_Cons a b = {}  a = {}  b = {}"
  by (auto simp: set_Cons_def)

lemma listset_eq_empty_iff[iff]: "listset XS = {}  {}  set XS"
  by (induction XS) auto

lemma sing_in_sings[simp]: "[x]  (λx. [x]) ` xd  x  xd"
  by auto

lemma those_eq_None_set_iff: "those xs = None  None  set xs"
  by (induction xs) (auto split: option.split)

lemma those_eq_Some_lengthD: "those xs = Some ys  length xs = length ys"
  by (induction xs arbitrary: ys) (auto split: option.splits)

lemma those_eq_Some_map_Some_iff: "those xs = Some ys  (xs = map Some ys)" (is "?l  ?r")
proof safe
  assume ?l
  then have "length xs = length ys"
    by (rule those_eq_Some_lengthD)
  then show ?r using ?l
    by (induction xs ys rule: list_induct2) (auto split: option.splits)
next
  assume ?r
  then have "length xs = length ys"
    by simp
  then show "those (map Some ys) = Some ys" using ?r
    by (induction xs ys rule: list_induct2) (auto split: option.splits)
qed


subsection ‹Set(sum)›

subsection ‹Max›

subsection ‹Uniform Limit›

subsection ‹Bounded Linear Functions›

lift_definition comp3::― ‹TODO: name?›
  "('c::real_normed_vector L 'd::real_normed_vector)  ('b::real_normed_vector L 'c) L 'b L 'd" is
  "λ(cd::('c L 'd)) (bc::'b L 'c). (cd oL bc)"
  by (rule bounded_bilinear.bounded_linear_right[OF bounded_bilinear_blinfun_compose])

lemma blinfun_apply_comp3[simp]: "blinfun_apply (comp3 a) b = (a oL b)"
  by (simp add: comp3.rep_eq)

lemma bounded_linear_comp3[bounded_linear]: "bounded_linear comp3"
  by transfer (rule bounded_bilinear_blinfun_compose)

lift_definition comp12::― ‹TODO: name?›
  "('a::real_normed_vector L 'c::real_normed_vector)  ('b::real_normed_vector L 'c)  ('a × 'b) L 'c"
  is "λf g (a, b). f a + g b"
  by (auto intro!: bounded_linear_intros
    intro: bounded_linear_compose
    simp: split_beta')

lemma blinfun_apply_comp12[simp]: "blinfun_apply (comp12 f g) b = f (fst b) + g (snd b)"
  by (simp add: comp12.rep_eq split_beta)


subsection ‹Order Transitivity Attributes›

attribute_setup le = Scan.succeed (Thm.rule_attribute [] (fn context => fn thm => thm RS @{thm order_trans}))
  "transitive version of inequality (useful for intro)"
attribute_setup ge = Scan.succeed (Thm.rule_attribute [] (fn context => fn thm => thm RS @{thm order_trans[rotated]}))
  "transitive version of inequality (useful for intro)"


subsection ‹point reflection›

definition preflect::"'a::real_vector  'a  'a" where "preflect  λt0 t. 2 *R t0 - t"

lemma preflect_preflect[simp]: "preflect t0 (preflect t0 t) = t"
  by (simp add: preflect_def algebra_simps)

lemma preflect_preflect_image[simp]: "preflect t0 ` preflect t0 ` S = S"
  by (simp add: image_image)

lemma is_interval_preflect[simp]: "is_interval (preflect t0 ` S)  is_interval S"
  by (auto simp: preflect_def[abs_def])

lemma iv_in_preflect_image[intro, simp]: "t0  T  t0  preflect t0 ` T"
  by (auto intro!: image_eqI simp: preflect_def algebra_simps scaleR_2)

lemma preflect_tendsto[tendsto_intros]:
  fixes l::"'a::real_normed_vector"
  shows "(g  l) F  (h  m) F  ((λx. preflect (g x) (h x))  preflect l m) F"
  by (auto intro!: tendsto_eq_intros simp: preflect_def)

lemma continuous_preflect[continuous_intros]:
  fixes a::"'a::real_normed_vector"
  shows "continuous (at a within A) (preflect t0)"
  by (auto simp: continuous_within intro!: tendsto_intros)

lemma
  fixes t0::"'a::ordered_real_vector"
  shows preflect_le[simp]: "t0  preflect t0 b  b  t0"
    and le_preflect[simp]: "preflect t0 b  t0  t0  b"
    and antimono_preflect: "antimono (preflect t0)"
    and preflect_le_preflect[simp]: "preflect t0 a  preflect t0 b  b  a"
    and preflect_eq_cancel[simp]: "preflect t0 a = preflect t0 b  a = b"
  by (auto intro!: antimonoI simp: preflect_def scaleR_2)

lemma preflect_eq_point_iff[simp]: "t0 = preflect t0 s  t0 = s" "preflect t0 s = t0  t0 = s"
  by (auto simp: preflect_def algebra_simps scaleR_2)

lemma preflect_minus_self[simp]: "preflect t0 s - t0 = t0 - s"
  by (simp add: preflect_def scaleR_2)

end