YES

We show the termination of the relative TRS R/S:

  R:
  eq(|0|(),|0|()) -> true()
  eq(|0|(),s(x)) -> false()
  eq(s(x),|0|()) -> false()
  eq(s(x),s(y)) -> eq(x,y)
  le(|0|(),y) -> true()
  le(s(x),|0|()) -> false()
  le(s(x),s(y)) -> le(x,y)
  app(nil(),y) -> y
  app(add(n,x),y) -> add(n,app(x,y))
  min(add(n,nil())) -> n
  min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
  if_min(true(),add(n,add(m,x))) -> min(add(n,x))
  if_min(false(),add(n,add(m,x))) -> min(add(m,x))
  rm(n,nil()) -> nil()
  rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
  if_rm(true(),n,add(m,x)) -> rm(n,x)
  if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
  minsort(nil(),nil()) -> nil()
  minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
  if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
  if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))

  S:
  rand(x) -> x
  rand(x) -> rand(s(x))

-- SCC decomposition.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: eq#(s(x),s(y)) -> eq#(x,y)
p2: le#(s(x),s(y)) -> le#(x,y)
p3: app#(add(n,x),y) -> app#(x,y)
p4: min#(add(n,add(m,x))) -> if_min#(le(n,m),add(n,add(m,x)))
p5: min#(add(n,add(m,x))) -> le#(n,m)
p6: if_min#(true(),add(n,add(m,x))) -> min#(add(n,x))
p7: if_min#(false(),add(n,add(m,x))) -> min#(add(m,x))
p8: rm#(n,add(m,x)) -> if_rm#(eq(n,m),n,add(m,x))
p9: rm#(n,add(m,x)) -> eq#(n,m)
p10: if_rm#(true(),n,add(m,x)) -> rm#(n,x)
p11: if_rm#(false(),n,add(m,x)) -> rm#(n,x)
p12: minsort#(add(n,x),y) -> if_minsort#(eq(n,min(add(n,x))),add(n,x),y)
p13: minsort#(add(n,x),y) -> eq#(n,min(add(n,x)))
p14: minsort#(add(n,x),y) -> min#(add(n,x))
p15: if_minsort#(true(),add(n,x),y) -> minsort#(app(rm(n,x),y),nil())
p16: if_minsort#(true(),add(n,x),y) -> app#(rm(n,x),y)
p17: if_minsort#(true(),add(n,x),y) -> rm#(n,x)
p18: if_minsort#(false(),add(n,x),y) -> minsort#(x,add(n,y))

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The estimated dependency graph contains the following SCCs:

  {p12, p15, p18}
  {p8, p10, p11}
  {p1}
  {p4, p6, p7}
  {p2}
  {p3}


-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: if_minsort#(false(),add(n,x),y) -> minsort#(x,add(n,y))
p2: minsort#(add(n,x),y) -> if_minsort#(eq(n,min(add(n,x))),add(n,x),y)
p3: if_minsort#(true(),add(n,x),y) -> minsort#(app(rm(n,x),y),nil())

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          if_minsort#_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (5,1)
          false_A() = (6,5)
          add_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (8,7)
          minsort#_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (5,8)
          eq_A(x1,x2) = x2 + (7,18)
          min_A(x1) = x1 + (4,1)
          true_A() = (9,6)
          app_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(1,1)) x2 + (0,8)
          rm_A(x1,x2) = x2 + (0,10)
          nil_A() = (0,5)
          |0|_A() = (10,7)
          s_A(x1) = x1 + (0,19)
          le_A(x1,x2) = ((1,0),(1,1)) x1 + ((1,0),(1,1)) x2 + (17,20)
          if_min_A(x1,x2) = x2 + (0,2)
          if_rm_A(x1,x2,x3) = x3 + (0,1)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (10,4)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (10,3)
          rand_A(x1) = ((1,0),(0,0)) x1 + (1,1)
  
    precedence:
    
      false = add = eq = app = |0| = s = le = if_min = minsort = if_minsort = rand > min > if_minsort# = minsort# = rm > true = if_rm > nil
    
    partial status:
  
      pi(if_minsort#) = []
      pi(false) = []
      pi(add) = []
      pi(minsort#) = []
      pi(eq) = []
      pi(min) = [1]
      pi(true) = []
      pi(app) = []
      pi(rm) = []
      pi(nil) = []
      pi(|0|) = []
      pi(s) = []
      pi(le) = []
      pi(if_min) = []
      pi(if_rm) = [3]
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p1

We remove them from the problem.

-- SCC decomposition.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: minsort#(add(n,x),y) -> if_minsort#(eq(n,min(add(n,x))),add(n,x),y)
p2: if_minsort#(true(),add(n,x),y) -> minsort#(app(rm(n,x),y),nil())

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The estimated dependency graph contains the following SCCs:

  {p1, p2}


-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: minsort#(add(n,x),y) -> if_minsort#(eq(n,min(add(n,x))),add(n,x),y)
p2: if_minsort#(true(),add(n,x),y) -> minsort#(app(rm(n,x),y),nil())

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          minsort#_A(x1,x2) = x1 + ((1,0),(0,0)) x2 + (2,0)
          add_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (7,2)
          if_minsort#_A(x1,x2,x3) = ((1,0),(0,0)) x2 + ((1,0),(0,0)) x3 + (1,1)
          eq_A(x1,x2) = ((1,0),(0,0)) x1 + (6,8)
          min_A(x1) = ((1,0),(0,0)) x1 + (6,10)
          true_A() = (3,3)
          app_A(x1,x2) = ((1,0),(0,0)) x1 + x2 + (0,9)
          rm_A(x1,x2) = ((1,0),(1,1)) x2 + (0,9)
          nil_A() = (0,9)
          |0|_A() = (4,0)
          s_A(x1) = x1
          false_A() = (3,1)
          le_A(x1,x2) = ((0,0),(1,0)) x1 + (4,11)
          if_min_A(x1,x2) = x1 + ((1,0),(1,0)) x2 + (1,1)
          if_rm_A(x1,x2,x3) = ((1,0),(1,0)) x3 + (0,3)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + x2 + (8,2)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (8,1)
          rand_A(x1) = ((1,0),(1,1)) x1 + (1,1)
  
    precedence:
    
      |0| > rand > rm = if_rm > minsort# > add = if_minsort# = min = app = if_min = minsort = if_minsort > eq = false > true > nil > s = le
    
    partial status:
  
      pi(minsort#) = []
      pi(add) = []
      pi(if_minsort#) = []
      pi(eq) = []
      pi(min) = []
      pi(true) = []
      pi(app) = [2]
      pi(rm) = []
      pi(nil) = []
      pi(|0|) = []
      pi(s) = []
      pi(false) = []
      pi(le) = []
      pi(if_min) = []
      pi(if_rm) = []
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = [1]

The next rules are strictly ordered:

  p1

We remove them from the problem.

-- SCC decomposition.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: if_minsort#(true(),add(n,x),y) -> minsort#(app(rm(n,x),y),nil())

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The estimated dependency graph contains the following SCCs:

  (no SCCs)

-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: if_rm#(false(),n,add(m,x)) -> rm#(n,x)
p2: rm#(n,add(m,x)) -> if_rm#(eq(n,m),n,add(m,x))
p3: if_rm#(true(),n,add(m,x)) -> rm#(n,x)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          if_rm#_A(x1,x2,x3) = ((1,0),(1,1)) x2 + ((1,0),(1,0)) x3 + (10,1)
          false_A() = (10,0)
          add_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (4,4)
          rm#_A(x1,x2) = ((1,0),(1,1)) x1 + ((1,0),(1,0)) x2 + (13,2)
          eq_A(x1,x2) = x1 + ((1,0),(0,0)) x2
          true_A() = (12,3)
          |0|_A() = (13,4)
          s_A(x1) = x1 + (0,2)
          le_A(x1,x2) = x1 + (11,1)
          app_A(x1,x2) = ((1,0),(1,0)) x1 + x2 + (0,5)
          nil_A() = (0,0)
          min_A(x1) = ((1,0),(1,0)) x1 + (5,0)
          if_min_A(x1,x2) = ((1,0),(0,0)) x2 + (5,1)
          rm_A(x1,x2) = ((1,0),(1,1)) x2
          if_rm_A(x1,x2,x3) = ((1,0),(0,0)) x3 + (0,8)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + x2 + (7,5)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (7,5)
          rand_A(x1) = ((1,0),(1,0)) x1 + (1,3)
  
    precedence:
    
      eq = true = |0| = app = min = if_min > rm = if_rm > add = nil > if_rm# = false = rm# = s = le = minsort = if_minsort = rand
    
    partial status:
  
      pi(if_rm#) = []
      pi(false) = []
      pi(add) = []
      pi(rm#) = []
      pi(eq) = []
      pi(true) = []
      pi(|0|) = []
      pi(s) = []
      pi(le) = []
      pi(app) = []
      pi(nil) = []
      pi(min) = []
      pi(if_min) = []
      pi(rm) = []
      pi(if_rm) = []
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p1

We remove them from the problem.

-- SCC decomposition.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: rm#(n,add(m,x)) -> if_rm#(eq(n,m),n,add(m,x))
p2: if_rm#(true(),n,add(m,x)) -> rm#(n,x)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The estimated dependency graph contains the following SCCs:

  {p1, p2}


-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: rm#(n,add(m,x)) -> if_rm#(eq(n,m),n,add(m,x))
p2: if_rm#(true(),n,add(m,x)) -> rm#(n,x)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          rm#_A(x1,x2) = ((1,0),(1,1)) x1 + ((1,0),(0,0)) x2 + (6,10)
          add_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (3,1)
          if_rm#_A(x1,x2,x3) = ((1,0),(0,0)) x2 + ((1,0),(0,0)) x3 + (4,11)
          eq_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (4,9)
          true_A() = (5,4)
          |0|_A() = (4,3)
          s_A(x1) = x1
          false_A() = (2,2)
          le_A(x1,x2) = x1 + ((1,0),(0,0)) x2 + (2,3)
          app_A(x1,x2) = ((1,0),(1,1)) x1 + x2 + (0,2)
          nil_A() = (0,2)
          min_A(x1) = ((1,0),(0,0)) x1 + (1,9)
          if_min_A(x1,x2) = ((1,0),(0,0)) x2 + (0,2)
          rm_A(x1,x2) = ((0,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (0,7)
          if_rm_A(x1,x2,x3) = ((0,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (0,6)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (0,5)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (0,3)
          rand_A(x1) = ((1,0),(1,1)) x1 + (1,1)
  
    precedence:
    
      rm > eq = false > add = app = min = if_min = if_rm = minsort = if_minsort = rand > if_rm# > rm# > true > |0| > le > nil > s
    
    partial status:
  
      pi(rm#) = [1]
      pi(add) = []
      pi(if_rm#) = []
      pi(eq) = []
      pi(true) = []
      pi(|0|) = []
      pi(s) = []
      pi(false) = []
      pi(le) = []
      pi(app) = [2]
      pi(nil) = []
      pi(min) = []
      pi(if_min) = []
      pi(rm) = []
      pi(if_rm) = []
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p1

We remove them from the problem.

-- SCC decomposition.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: if_rm#(true(),n,add(m,x)) -> rm#(n,x)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The estimated dependency graph contains the following SCCs:

  (no SCCs)

-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: eq#(s(x),s(y)) -> eq#(x,y)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          eq#_A(x1,x2) = x2 + (1,2)
          s_A(x1) = x1 + (0,1)
          eq_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (3,8)
          |0|_A() = (9,5)
          true_A() = (0,0)
          false_A() = (8,4)
          le_A(x1,x2) = ((1,0),(1,0)) x2
          app_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(1,1)) x2 + (0,3)
          nil_A() = (0,3)
          add_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (2,2)
          min_A(x1) = ((1,0),(0,0)) x1 + (1,6)
          if_min_A(x1,x2) = ((1,0),(0,0)) x2 + (0,1)
          rm_A(x1,x2) = ((0,0),(1,0)) x1 + ((1,0),(1,0)) x2 + (0,5)
          if_rm_A(x1,x2,x3) = ((1,0),(0,0)) x3 + (0,3)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (5,3)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (5,2)
          rand_A(x1) = ((1,0),(0,0)) x1 + (1,2)
  
    precedence:
    
      le = rm = if_rm = rand > eq# = s = eq = |0| = false = app = add = min = if_min = minsort = if_minsort > true > nil
    
    partial status:
  
      pi(eq#) = []
      pi(s) = []
      pi(eq) = []
      pi(|0|) = []
      pi(true) = []
      pi(false) = []
      pi(le) = []
      pi(app) = []
      pi(nil) = []
      pi(add) = []
      pi(min) = []
      pi(if_min) = []
      pi(rm) = []
      pi(if_rm) = []
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p1

We remove them from the problem.  Then no dependency pair remains.

-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: if_min#(false(),add(n,add(m,x))) -> min#(add(m,x))
p2: min#(add(n,add(m,x))) -> if_min#(le(n,m),add(n,add(m,x)))
p3: if_min#(true(),add(n,add(m,x))) -> min#(add(n,x))

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          if_min#_A(x1,x2) = ((0,0),(1,0)) x1 + ((1,0),(1,0)) x2
          false_A() = (5,2)
          add_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (6,16)
          min#_A(x1) = ((1,0),(0,0)) x1 + (1,15)
          le_A(x1,x2) = x2 + (2,25)
          true_A() = (1,4)
          eq_A(x1,x2) = ((1,0),(0,0)) x1 + ((0,0),(1,0)) x2 + (15,47)
          |0|_A() = (6,3)
          s_A(x1) = ((1,0),(1,1)) x1 + (0,1)
          app_A(x1,x2) = ((1,0),(1,1)) x1 + x2 + (0,17)
          nil_A() = (0,1)
          min_A(x1) = ((1,0),(1,0)) x1 + (7,12)
          if_min_A(x1,x2) = x2 + (2,1)
          rm_A(x1,x2) = ((1,0),(1,1)) x2 + (0,24)
          if_rm_A(x1,x2,x3) = ((1,0),(1,1)) x3 + (0,1)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (8,41)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (8,36)
          rand_A(x1) = ((1,0),(1,0)) x1 + (1,2)
  
    precedence:
    
      false = min# = le = true = eq = |0| = s = app = min = if_min = rand > if_min# = add = rm = if_rm > nil = minsort = if_minsort
    
    partial status:
  
      pi(if_min#) = []
      pi(false) = []
      pi(add) = []
      pi(min#) = []
      pi(le) = []
      pi(true) = []
      pi(eq) = []
      pi(|0|) = []
      pi(s) = []
      pi(app) = []
      pi(nil) = []
      pi(min) = []
      pi(if_min) = []
      pi(rm) = []
      pi(if_rm) = []
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p2

We remove them from the problem.

-- SCC decomposition.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: if_min#(false(),add(n,add(m,x))) -> min#(add(m,x))
p2: if_min#(true(),add(n,add(m,x))) -> min#(add(n,x))

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The estimated dependency graph contains the following SCCs:

  (no SCCs)

-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: le#(s(x),s(y)) -> le#(x,y)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          le#_A(x1,x2) = x2 + (0,1)
          s_A(x1) = ((1,0),(1,1)) x1 + (0,2)
          eq_A(x1,x2) = ((0,0),(1,0)) x1 + (4,5)
          |0|_A() = (4,4)
          true_A() = (3,5)
          false_A() = (3,3)
          le_A(x1,x2) = x1 + ((1,0),(1,1)) x2 + (1,13)
          app_A(x1,x2) = ((1,0),(1,0)) x1 + x2 + (0,1)
          nil_A() = (0,0)
          add_A(x1,x2) = ((1,0),(1,0)) x1 + x2 + (5,0)
          min_A(x1) = x1 + (6,12)
          if_min_A(x1,x2) = ((0,0),(1,0)) x1 + x2 + (1,10)
          rm_A(x1,x2) = x2 + (0,4)
          if_rm_A(x1,x2,x3) = x3 + (0,4)
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(1,0)) x2 + (7,6)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(1,0)) x3 + (7,6)
          rand_A(x1) = ((1,0),(0,0)) x1 + (1,1)
  
    precedence:
    
      |0| = le = minsort = if_minsort = rand > app = rm > if_rm > le# = s = eq = true = nil = add = min = if_min > false
    
    partial status:
  
      pi(le#) = [2]
      pi(s) = [1]
      pi(eq) = []
      pi(|0|) = []
      pi(true) = []
      pi(false) = []
      pi(le) = []
      pi(app) = [2]
      pi(nil) = []
      pi(add) = []
      pi(min) = [1]
      pi(if_min) = [2]
      pi(rm) = []
      pi(if_rm) = [3]
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p1

We remove them from the problem.  Then no dependency pair remains.

-- Reduction pair.

Consider the non-minimal dependency pair problem (P, R), where P consists of

p1: app#(add(n,x),y) -> app#(x,y)

and R consists of:

r1: eq(|0|(),|0|()) -> true()
r2: eq(|0|(),s(x)) -> false()
r3: eq(s(x),|0|()) -> false()
r4: eq(s(x),s(y)) -> eq(x,y)
r5: le(|0|(),y) -> true()
r6: le(s(x),|0|()) -> false()
r7: le(s(x),s(y)) -> le(x,y)
r8: app(nil(),y) -> y
r9: app(add(n,x),y) -> add(n,app(x,y))
r10: min(add(n,nil())) -> n
r11: min(add(n,add(m,x))) -> if_min(le(n,m),add(n,add(m,x)))
r12: if_min(true(),add(n,add(m,x))) -> min(add(n,x))
r13: if_min(false(),add(n,add(m,x))) -> min(add(m,x))
r14: rm(n,nil()) -> nil()
r15: rm(n,add(m,x)) -> if_rm(eq(n,m),n,add(m,x))
r16: if_rm(true(),n,add(m,x)) -> rm(n,x)
r17: if_rm(false(),n,add(m,x)) -> add(m,rm(n,x))
r18: minsort(nil(),nil()) -> nil()
r19: minsort(add(n,x),y) -> if_minsort(eq(n,min(add(n,x))),add(n,x),y)
r20: if_minsort(true(),add(n,x),y) -> add(n,minsort(app(rm(n,x),y),nil()))
r21: if_minsort(false(),add(n,x),y) -> minsort(x,add(n,y))
r22: rand(x) -> x
r23: rand(x) -> rand(s(x))

The set of usable rules consists of

  r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11, r12, r13, r14, r15, r16, r17, r18, r19, r20, r21, r22, r23

Take the reduction pair:

  weighted path order
  
    base order:
  
      matrix interpretations:
      
        carrier: N^2
        order: lexicographic order
        interpretations:
          app#_A(x1,x2) = x1 + (0,1)
          add_A(x1,x2) = ((1,0),(0,0)) x1 + ((1,0),(0,0)) x2 + (8,5)
          eq_A(x1,x2) = x2 + (7,13)
          |0|_A() = (11,5)
          true_A() = (10,4)
          s_A(x1) = x1
          false_A() = (6,0)
          le_A(x1,x2) = ((0,0),(1,0)) x1 + ((0,0),(1,0)) x2 + (11,12)
          app_A(x1,x2) = x1 + ((1,0),(1,1)) x2
          nil_A() = (0,3)
          min_A(x1) = ((1,0),(0,0)) x1 + (13,11)
          if_min_A(x1,x2) = x1 + x2 + (1,7)
          rm_A(x1,x2) = ((1,0),(1,0)) x2 + (0,4)
          if_rm_A(x1,x2,x3) = ((0,0),(1,0)) x1 + ((1,0),(0,0)) x3
          minsort_A(x1,x2) = ((1,0),(1,0)) x1 + ((1,0),(0,0)) x2 + (1,2)
          if_minsort_A(x1,x2,x3) = ((1,0),(1,0)) x2 + ((1,0),(0,0)) x3 + (1,1)
          rand_A(x1) = ((1,0),(1,1)) x1 + (1,1)
  
    precedence:
    
      app# > |0| > eq = s = rand > min = if_min > add = true = false = le = app = nil = rm = if_rm = minsort = if_minsort
    
    partial status:
  
      pi(app#) = [1]
      pi(add) = []
      pi(eq) = []
      pi(|0|) = []
      pi(true) = []
      pi(s) = []
      pi(false) = []
      pi(le) = []
      pi(app) = [1, 2]
      pi(nil) = []
      pi(min) = []
      pi(if_min) = []
      pi(rm) = []
      pi(if_rm) = []
      pi(minsort) = []
      pi(if_minsort) = []
      pi(rand) = []

The next rules are strictly ordered:

  p1

We remove them from the problem.  Then no dependency pair remains.