We consider the system Applicative_first_order_05__#3.36.

  Alphabet:

    0 : [] --> a 
    cons : [c * d] --> d 
    f : [a] --> a 
    false : [] --> b 
    filter : [c -> b * d] --> d 
    filter2 : [b * c -> b * c * d] --> d 
    g : [a] --> a 
    map : [c -> c * d] --> d 
    minus : [a * a] --> a 
    nil : [] --> d 
    s : [a] --> a 
    true : [] --> b 

  Rules:

    minus(x, 0) => x 
    minus(s(x), s(y)) => minus(x, y) 
    f(0) => s(0) 
    f(s(x)) => minus(s(x), g(f(x))) 
    g(0) => 0 
    g(s(x)) => minus(s(x), f(g(x))) 
    map(h, nil) => nil 
    map(h, cons(x, y)) => cons(h x, map(h, y)) 
    filter(h, nil) => nil 
    filter(h, cons(x, y)) => filter2(h x, h, x, y) 
    filter2(true, h, x, y) => cons(x, filter(h, y)) 
    filter2(false, h, x, y) => filter(h, y) 

This AFS is converted to an AFSM simply by replacing all free variables by meta-variables (with arity 0).

We use the dependency pair framework as described in [Kop12, Ch. 6/7], with static dependency pairs (see [KusIsoSakBla09] and the adaptation for AFSMs and accessible arguments in [Kop13]).

We thus obtain the following dependency pair problem (P_0, R_0, static, formative):

  Dependency Pairs P_0:

    0] minus#(s(X), s(Y)) =#> minus#(X, Y)   
    1] f#(s(X)) =#> minus#(s(X), g(f(X)))   
    2] f#(s(X)) =#> g#(f(X))   
    3] f#(s(X)) =#> f#(X)   
    4] g#(s(X)) =#> minus#(s(X), f(g(X)))   
    5] g#(s(X)) =#> f#(g(X))   
    6] g#(s(X)) =#> g#(X)   
    7] map#(F, cons(X, Y)) =#> map#(F, Y)   
    8] filter#(F, cons(X, Y)) =#> filter2#(F X, F, X, Y)   
    9] filter2#(true, F, X, Y) =#> filter#(F, Y)   
    10] filter2#(false, F, X, Y) =#> filter#(F, Y)   

  Rules R_0:

    minus(X, 0) => X 
    minus(s(X), s(Y)) => minus(X, Y) 
    f(0) => s(0) 
    f(s(X)) => minus(s(X), g(f(X))) 
    g(0) => 0 
    g(s(X)) => minus(s(X), f(g(X))) 
    map(F, nil) => nil 
    map(F, cons(X, Y)) => cons(F X, map(F, Y)) 
    filter(F, nil) => nil 
    filter(F, cons(X, Y)) => filter2(F X, F, X, Y) 
    filter2(true, F, X, Y) => cons(X, filter(F, Y)) 
    filter2(false, F, X, Y) => filter(F, Y) 

Thus, the original system is terminating if (P_0, R_0, static, formative) is finite.

We consider the dependency pair problem (P_0, R_0, static, formative).

The formative rules of (P_0, R_0) are R_1 ::=

  minus(X, 0) => X 
  minus(s(X), s(Y)) => minus(X, Y) 
  f(0) => s(0) 
  f(s(X)) => minus(s(X), g(f(X))) 
  g(0) => 0 
  g(s(X)) => minus(s(X), f(g(X))) 
  map(F, cons(X, Y)) => cons(F X, map(F, Y)) 
  filter(F, cons(X, Y)) => filter2(F X, F, X, Y) 
  filter2(true, F, X, Y) => cons(X, filter(F, Y)) 
  filter2(false, F, X, Y) => filter(F, Y) 

By [Kop12, Thm. 7.17], we may replace the dependency pair problem (P_0, R_0, static, formative) by (P_0, R_1, static, formative).

Thus, the original system is terminating if (P_0, R_1, static, formative) is finite.

We consider the dependency pair problem (P_0, R_1, static, formative).

We will use the reduction pair processor [Kop12, Thm. 7.16].  It suffices to find a standard reduction pair [Kop12, Def. 6.69].  Thus, we must orient:

  minus#(s(X), s(Y)) >? minus#(X, Y) 
  f#(s(X)) >? minus#(s(X), g(f(X))) 
  f#(s(X)) >? g#(f(X)) 
  f#(s(X)) >? f#(X) 
  g#(s(X)) >? minus#(s(X), f(g(X))) 
  g#(s(X)) >? f#(g(X)) 
  g#(s(X)) >? g#(X) 
  map#(F, cons(X, Y)) >? map#(F, Y) 
  filter#(F, cons(X, Y)) >? filter2#(F X, F, X, Y) 
  filter2#(true, F, X, Y) >? filter#(F, Y) 
  filter2#(false, F, X, Y) >? filter#(F, Y) 
  minus(X, 0) >= X 
  minus(s(X), s(Y)) >= minus(X, Y) 
  f(0) >= s(0) 
  f(s(X)) >= minus(s(X), g(f(X))) 
  g(0) >= 0 
  g(s(X)) >= minus(s(X), f(g(X))) 
  map(F, cons(X, Y)) >= cons(F X, map(F, Y)) 
  filter(F, cons(X, Y)) >= filter2(F X, F, X, Y) 
  filter2(true, F, X, Y) >= cons(X, filter(F, Y)) 
  filter2(false, F, X, Y) >= filter(F, Y) 

We orient these requirements with a polynomial interpretation in the natural numbers.

The following interpretation satisfies the requirements:

  0 = 0 
  cons = \y0y1.0 
  f = \y0.2 + y0 
  f# = \y0.3 + 3y0 
  false = 3 
  filter = \G0y1.0 
  filter2 = \y0G1y2y3.0 
  filter2# = \y0G1y2y3.0 
  filter# = \G0y1.0 
  g = \y0.2y0 
  g# = \y0.2 + 3y0 
  map = \G0y1.0 
  map# = \G0y1.0 
  minus = \y0y1.y0 
  minus# = \y0y1.0 
  s = \y0.2 + 2y0 
  true = 3 

Using this interpretation, the requirements translate to:

  [[minus#(s(_x0), s(_x1))]] = 0 >= 0 = [[minus#(_x0, _x1)]] 
  [[f#(s(_x0))]] = 9 + 6x0 > 0 = [[minus#(s(_x0), g(f(_x0)))]] 
  [[f#(s(_x0))]] = 9 + 6x0 > 8 + 3x0 = [[g#(f(_x0))]] 
  [[f#(s(_x0))]] = 9 + 6x0 > 3 + 3x0 = [[f#(_x0)]] 
  [[g#(s(_x0))]] = 8 + 6x0 > 0 = [[minus#(s(_x0), f(g(_x0)))]] 
  [[g#(s(_x0))]] = 8 + 6x0 > 3 + 6x0 = [[f#(g(_x0))]] 
  [[g#(s(_x0))]] = 8 + 6x0 > 2 + 3x0 = [[g#(_x0)]] 
  [[map#(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[map#(_F0, _x2)]] 
  [[filter#(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[filter2#(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2#(true, _F0, _x1, _x2)]] = 0 >= 0 = [[filter#(_F0, _x2)]] 
  [[filter2#(false, _F0, _x1, _x2)]] = 0 >= 0 = [[filter#(_F0, _x2)]] 
  [[minus(_x0, 0)]] = x0 >= x0 = [[_x0]] 
  [[minus(s(_x0), s(_x1))]] = 2 + 2x0 >= x0 = [[minus(_x0, _x1)]] 
  [[f(0)]] = 2 >= 2 = [[s(0)]] 
  [[f(s(_x0))]] = 4 + 2x0 >= 2 + 2x0 = [[minus(s(_x0), g(f(_x0)))]] 
  [[g(0)]] = 0 >= 0 = [[0]] 
  [[g(s(_x0))]] = 4 + 4x0 >= 2 + 2x0 = [[minus(s(_x0), f(g(_x0)))]] 
  [[map(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[cons(_F0 _x1, map(_F0, _x2))]] 
  [[filter(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[filter2(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2(true, _F0, _x1, _x2)]] = 0 >= 0 = [[cons(_x1, filter(_F0, _x2))]] 
  [[filter2(false, _F0, _x1, _x2)]] = 0 >= 0 = [[filter(_F0, _x2)]] 

By the observations in [Kop12, Sec. 6.6], this reduction pair suffices; we may thus replace the dependency pair problem (P_0, R_1, static, formative) by (P_1, R_1, static, formative), where P_1 consists of:

  minus#(s(X), s(Y)) =#> minus#(X, Y)   
  map#(F, cons(X, Y)) =#> map#(F, Y)   
  filter#(F, cons(X, Y)) =#> filter2#(F X, F, X, Y)   
  filter2#(true, F, X, Y) =#> filter#(F, Y)   
  filter2#(false, F, X, Y) =#> filter#(F, Y)   

Thus, the original system is terminating if (P_1, R_1, static, formative) is finite.

We consider the dependency pair problem (P_1, R_1, static, formative).

We will use the reduction pair processor [Kop12, Thm. 7.16].  It suffices to find a standard reduction pair [Kop12, Def. 6.69].  Thus, we must orient:

  minus#(s(X), s(Y)) >? minus#(X, Y) 
  map#(F, cons(X, Y)) >? map#(F, Y) 
  filter#(F, cons(X, Y)) >? filter2#(F X, F, X, Y) 
  filter2#(true, F, X, Y) >? filter#(F, Y) 
  filter2#(false, F, X, Y) >? filter#(F, Y) 
  minus(X, 0) >= X 
  minus(s(X), s(Y)) >= minus(X, Y) 
  f(0) >= s(0) 
  f(s(X)) >= minus(s(X), g(f(X))) 
  g(0) >= 0 
  g(s(X)) >= minus(s(X), f(g(X))) 
  map(F, cons(X, Y)) >= cons(F X, map(F, Y)) 
  filter(F, cons(X, Y)) >= filter2(F X, F, X, Y) 
  filter2(true, F, X, Y) >= cons(X, filter(F, Y)) 
  filter2(false, F, X, Y) >= filter(F, Y) 

We orient these requirements with a polynomial interpretation in the natural numbers.

The following interpretation satisfies the requirements:

  0 = 0 
  cons = \y0y1.0 
  f = \y0.2 + 2y0 
  false = 3 
  filter = \G0y1.0 
  filter2 = \y0G1y2y3.0 
  filter2# = \y0G1y2y3.0 
  filter# = \G0y1.0 
  g = \y0.2 + y0 
  map = \G0y1.0 
  map# = \G0y1.0 
  minus = \y0y1.y0 
  minus# = \y0y1.y0 
  s = \y0.2 + y0 
  true = 3 

Using this interpretation, the requirements translate to:

  [[minus#(s(_x0), s(_x1))]] = 2 + x0 > x0 = [[minus#(_x0, _x1)]] 
  [[map#(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[map#(_F0, _x2)]] 
  [[filter#(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[filter2#(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2#(true, _F0, _x1, _x2)]] = 0 >= 0 = [[filter#(_F0, _x2)]] 
  [[filter2#(false, _F0, _x1, _x2)]] = 0 >= 0 = [[filter#(_F0, _x2)]] 
  [[minus(_x0, 0)]] = x0 >= x0 = [[_x0]] 
  [[minus(s(_x0), s(_x1))]] = 2 + x0 >= x0 = [[minus(_x0, _x1)]] 
  [[f(0)]] = 2 >= 2 = [[s(0)]] 
  [[f(s(_x0))]] = 6 + 2x0 >= 2 + x0 = [[minus(s(_x0), g(f(_x0)))]] 
  [[g(0)]] = 2 >= 0 = [[0]] 
  [[g(s(_x0))]] = 4 + x0 >= 2 + x0 = [[minus(s(_x0), f(g(_x0)))]] 
  [[map(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[cons(_F0 _x1, map(_F0, _x2))]] 
  [[filter(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[filter2(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2(true, _F0, _x1, _x2)]] = 0 >= 0 = [[cons(_x1, filter(_F0, _x2))]] 
  [[filter2(false, _F0, _x1, _x2)]] = 0 >= 0 = [[filter(_F0, _x2)]] 

By the observations in [Kop12, Sec. 6.6], this reduction pair suffices; we may thus replace the dependency pair problem (P_1, R_1, static, formative) by (P_2, R_1, static, formative), where P_2 consists of:

  map#(F, cons(X, Y)) =#> map#(F, Y)   
  filter#(F, cons(X, Y)) =#> filter2#(F X, F, X, Y)   
  filter2#(true, F, X, Y) =#> filter#(F, Y)   
  filter2#(false, F, X, Y) =#> filter#(F, Y)   

Thus, the original system is terminating if (P_2, R_1, static, formative) is finite.

We consider the dependency pair problem (P_2, R_1, static, formative).

The formative rules of (P_2, R_1) are R_2 ::=

  map(F, cons(X, Y)) => cons(F X, map(F, Y)) 
  filter(F, cons(X, Y)) => filter2(F X, F, X, Y) 
  filter2(true, F, X, Y) => cons(X, filter(F, Y)) 
  filter2(false, F, X, Y) => filter(F, Y) 

By [Kop12, Thm. 7.17], we may replace the dependency pair problem (P_2, R_1, static, formative) by (P_2, R_2, static, formative).

Thus, the original system is terminating if (P_2, R_2, static, formative) is finite.

We consider the dependency pair problem (P_2, R_2, static, formative).

We will use the reduction pair processor [Kop12, Thm. 7.16].  It suffices to find a standard reduction pair [Kop12, Def. 6.69].  Thus, we must orient:

  map#(F, cons(X, Y)) >? map#(F, Y) 
  filter#(F, cons(X, Y)) >? filter2#(F X, F, X, Y) 
  filter2#(true, F, X, Y) >? filter#(F, Y) 
  filter2#(false, F, X, Y) >? filter#(F, Y) 
  map(F, cons(X, Y)) >= cons(F X, map(F, Y)) 
  filter(F, cons(X, Y)) >= filter2(F X, F, X, Y) 
  filter2(true, F, X, Y) >= cons(X, filter(F, Y)) 
  filter2(false, F, X, Y) >= filter(F, Y) 

We orient these requirements with a polynomial interpretation in the natural numbers.

The following interpretation satisfies the requirements:

  cons = \y0y1.3 + 2y1 
  false = 3 
  filter = \G0y1.y1 
  filter2 = \y0G1y2y3.3 + 2y3 
  filter2# = \y0G1y2y3.0 
  filter# = \G0y1.0 
  map = \G0y1.2y1 + 3y1G0(y1) 
  map# = \G0y1.y1 
  true = 3 

Using this interpretation, the requirements translate to:

  [[map#(_F0, cons(_x1, _x2))]] = 3 + 2x2 > x2 = [[map#(_F0, _x2)]] 
  [[filter#(_F0, cons(_x1, _x2))]] = 0 >= 0 = [[filter2#(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2#(true, _F0, _x1, _x2)]] = 0 >= 0 = [[filter#(_F0, _x2)]] 
  [[filter2#(false, _F0, _x1, _x2)]] = 0 >= 0 = [[filter#(_F0, _x2)]] 
  [[map(_F0, cons(_x1, _x2))]] = 6 + 4x2 + 6x2F0(3 + 2x2) + 9F0(3 + 2x2) >= 3 + 4x2 + 6x2F0(x2) = [[cons(_F0 _x1, map(_F0, _x2))]] 
  [[filter(_F0, cons(_x1, _x2))]] = 3 + 2x2 >= 3 + 2x2 = [[filter2(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2(true, _F0, _x1, _x2)]] = 3 + 2x2 >= 3 + 2x2 = [[cons(_x1, filter(_F0, _x2))]] 
  [[filter2(false, _F0, _x1, _x2)]] = 3 + 2x2 >= x2 = [[filter(_F0, _x2)]] 

By the observations in [Kop12, Sec. 6.6], this reduction pair suffices; we may thus replace the dependency pair problem (P_2, R_2, static, formative) by (P_3, R_2, static, formative), where P_3 consists of:

  filter#(F, cons(X, Y)) =#> filter2#(F X, F, X, Y)   
  filter2#(true, F, X, Y) =#> filter#(F, Y)   
  filter2#(false, F, X, Y) =#> filter#(F, Y)   

Thus, the original system is terminating if (P_3, R_2, static, formative) is finite.

We consider the dependency pair problem (P_3, R_2, static, formative).

We will use the reduction pair processor [Kop12, Thm. 7.16].  It suffices to find a standard reduction pair [Kop12, Def. 6.69].  Thus, we must orient:

  filter#(F, cons(X, Y)) >? filter2#(F X, F, X, Y) 
  filter2#(true, F, X, Y) >? filter#(F, Y) 
  filter2#(false, F, X, Y) >? filter#(F, Y) 
  map(F, cons(X, Y)) >= cons(F X, map(F, Y)) 
  filter(F, cons(X, Y)) >= filter2(F X, F, X, Y) 
  filter2(true, F, X, Y) >= cons(X, filter(F, Y)) 
  filter2(false, F, X, Y) >= filter(F, Y) 

We orient these requirements with a polynomial interpretation in the natural numbers.

The following interpretation satisfies the requirements:

  cons = \y0y1.2 + y0 + y1 
  false = 3 
  filter = \G0y1.y1 + 2y1G0(y1) 
  filter2 = \y0G1y2y3.1 + y0 + y2 + y3 + 2y3G1(y3) 
  filter2# = \y0G1y2y3.2 + 2y0 + 2y3 + 2G1(y3) + y3G1(y3) 
  filter# = \G0y1.1 + 2y1 + 2G0(y1) + y1G0(y1) 
  map = \G0y1.y1 + 2y1G0(y1) 
  true = 3 

Using this interpretation, the requirements translate to:

  [[filter#(_F0, cons(_x1, _x2))]] = 5 + 2x1 + 2x2 + 4F0(2 + x1 + x2) + x1F0(2 + x1 + x2) + x2F0(2 + x1 + x2) > 2 + 2x2 + 2F0(x1) + 2F0(x2) + x2F0(x2) = [[filter2#(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2#(true, _F0, _x1, _x2)]] = 8 + 2x2 + 2F0(x2) + x2F0(x2) > 1 + 2x2 + 2F0(x2) + x2F0(x2) = [[filter#(_F0, _x2)]] 
  [[filter2#(false, _F0, _x1, _x2)]] = 8 + 2x2 + 2F0(x2) + x2F0(x2) > 1 + 2x2 + 2F0(x2) + x2F0(x2) = [[filter#(_F0, _x2)]] 
  [[map(_F0, cons(_x1, _x2))]] = 2 + x1 + x2 + 2x1F0(2 + x1 + x2) + 2x2F0(2 + x1 + x2) + 4F0(2 + x1 + x2) >= 2 + x2 + F0(x1) + 2x2F0(x2) = [[cons(_F0 _x1, map(_F0, _x2))]] 
  [[filter(_F0, cons(_x1, _x2))]] = 2 + x1 + x2 + 2x1F0(2 + x1 + x2) + 2x2F0(2 + x1 + x2) + 4F0(2 + x1 + x2) >= 1 + x1 + x2 + F0(x1) + 2x2F0(x2) = [[filter2(_F0 _x1, _F0, _x1, _x2)]] 
  [[filter2(true, _F0, _x1, _x2)]] = 4 + x1 + x2 + 2x2F0(x2) >= 2 + x1 + x2 + 2x2F0(x2) = [[cons(_x1, filter(_F0, _x2))]] 
  [[filter2(false, _F0, _x1, _x2)]] = 4 + x1 + x2 + 2x2F0(x2) >= x2 + 2x2F0(x2) = [[filter(_F0, _x2)]] 

By the observations in [Kop12, Sec. 6.6], this reduction pair suffices; we may thus replace a dependency pair problem (P_3, R_2) by ({}, R_2).  By the empty set processor [Kop12, Thm. 7.15] this problem may be immediately removed.

As all dependency pair problems were succesfully simplified with sound (and complete) processors until nothing remained, we conclude termination.


+++ Citations +++

[Kop12]  C. Kop.  Higher Order Termination.  PhD Thesis, 2012.
[Kop13]  C. Kop.  Static Dependency Pairs with Accessibility.  Unpublished manuscript, http://cl-informatik.uibk.ac.at/users/kop/static.pdf, 2013.
[KusIsoSakBla09]  K. Kusakari, Y. Isogai, M. Sakai, and F. Blanqui.  Static Dependency Pair Method Based On Strong Computability for Higher-Order Rewrite Systems.  In volume 92(10) of IEICE Transactions on Information and Systems.  2007--2015, 2009.