YES We show the termination of the TRS R: app(f(),app(app(cons(),nil()),y)) -> y app(f(),app(app(cons(),app(f(),app(app(cons(),nil()),y))),z)) -> app(app(app(copy(),n()),y),z) app(app(app(copy(),|0|()),y),z) -> app(f(),z) app(app(app(copy(),app(s(),x)),y),z) -> app(app(app(copy(),x),y),app(app(cons(),app(f(),y)),z)) app(app(map(),|fun|),nil()) -> nil() app(app(map(),|fun|),app(app(cons(),x),xs)) -> app(app(cons(),app(|fun|,x)),app(app(map(),|fun|),xs)) app(app(filter(),|fun|),nil()) -> nil() app(app(filter(),|fun|),app(app(cons(),x),xs)) -> app(app(app(app(filter2(),app(|fun|,x)),|fun|),x),xs) app(app(app(app(filter2(),true()),|fun|),x),xs) -> app(app(cons(),x),app(app(filter(),|fun|),xs)) app(app(app(app(filter2(),false()),|fun|),x),xs) -> app(app(filter(),|fun|),xs) Instead we prove termination of the uncurried system: app(cons_0(),y) -> cons_1(y) app(cons_1(x1),y) -> cons_2(x1,y) app(copy_0(),y) -> copy_1(y) app(copy_1(x1),y) -> copy_2(x1,y) app(copy_2(x1,x2),y) -> copy_3(x1,x2,y) app(f_0(),y) -> f_1(y) app(filter_0(),y) -> filter_1(y) app(filter_1(x1),y) -> filter_2(x1,y) app(filter2_0(),y) -> filter2_1(y) app(filter2_1(x1),y) -> filter2_2(x1,y) app(filter2_2(x1,x2),y) -> filter2_3(x1,x2,y) app(filter2_3(x1,x2,x3),y) -> filter2_4(x1,x2,x3,y) app(map_0(),y) -> map_1(y) app(map_1(x1),y) -> map_2(x1,y) app(s_0(),y) -> s_1(y) f_1(cons_2(nil_0(),y)) -> y f_1(cons_2(f_1(cons_2(nil_0(),y)),z)) -> copy_3(n_0(),y,z) copy_3(|0|_0(),y,z) -> f_1(z) copy_3(s_1(x),y,z) -> copy_3(x,y,cons_2(f_1(y),z)) map_2(|fun|,nil_0()) -> nil_0() map_2(|fun|,cons_2(x,xs)) -> cons_2(app(|fun|,x),map_2(|fun|,xs)) filter_2(|fun|,nil_0()) -> nil_0() filter_2(|fun|,cons_2(x,xs)) -> filter2_4(app(|fun|,x),|fun|,x,xs) filter2_4(true_0(),|fun|,x,xs) -> cons_2(x,filter_2(|fun|,xs)) filter2_4(false_0(),|fun|,x,xs) -> filter_2(|fun|,xs) -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: app#(copy_2(x1,x2),y) -> copy_3#(x1,x2,y) p2: app#(f_0(),y) -> f_1#(y) p3: app#(filter_1(x1),y) -> filter_2#(x1,y) p4: app#(filter2_3(x1,x2,x3),y) -> filter2_4#(x1,x2,x3,y) p5: app#(map_1(x1),y) -> map_2#(x1,y) p6: f_1#(cons_2(f_1(cons_2(nil_0(),y)),z)) -> copy_3#(n_0(),y,z) p7: copy_3#(|0|_0(),y,z) -> f_1#(z) p8: copy_3#(s_1(x),y,z) -> copy_3#(x,y,cons_2(f_1(y),z)) p9: copy_3#(s_1(x),y,z) -> f_1#(y) p10: map_2#(|fun|,cons_2(x,xs)) -> app#(|fun|,x) p11: map_2#(|fun|,cons_2(x,xs)) -> map_2#(|fun|,xs) p12: filter_2#(|fun|,cons_2(x,xs)) -> filter2_4#(app(|fun|,x),|fun|,x,xs) p13: filter_2#(|fun|,cons_2(x,xs)) -> app#(|fun|,x) p14: filter2_4#(true_0(),|fun|,x,xs) -> filter_2#(|fun|,xs) p15: filter2_4#(false_0(),|fun|,x,xs) -> filter_2#(|fun|,xs) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(copy_0(),y) -> copy_1(y) r4: app(copy_1(x1),y) -> copy_2(x1,y) r5: app(copy_2(x1,x2),y) -> copy_3(x1,x2,y) r6: app(f_0(),y) -> f_1(y) r7: app(filter_0(),y) -> filter_1(y) r8: app(filter_1(x1),y) -> filter_2(x1,y) r9: app(filter2_0(),y) -> filter2_1(y) r10: app(filter2_1(x1),y) -> filter2_2(x1,y) r11: app(filter2_2(x1,x2),y) -> filter2_3(x1,x2,y) r12: app(filter2_3(x1,x2,x3),y) -> filter2_4(x1,x2,x3,y) r13: app(map_0(),y) -> map_1(y) r14: app(map_1(x1),y) -> map_2(x1,y) r15: app(s_0(),y) -> s_1(y) r16: f_1(cons_2(nil_0(),y)) -> y r17: f_1(cons_2(f_1(cons_2(nil_0(),y)),z)) -> copy_3(n_0(),y,z) r18: copy_3(|0|_0(),y,z) -> f_1(z) r19: copy_3(s_1(x),y,z) -> copy_3(x,y,cons_2(f_1(y),z)) r20: map_2(|fun|,nil_0()) -> nil_0() r21: map_2(|fun|,cons_2(x,xs)) -> cons_2(app(|fun|,x),map_2(|fun|,xs)) r22: filter_2(|fun|,nil_0()) -> nil_0() r23: filter_2(|fun|,cons_2(x,xs)) -> filter2_4(app(|fun|,x),|fun|,x,xs) r24: filter2_4(true_0(),|fun|,x,xs) -> cons_2(x,filter_2(|fun|,xs)) r25: filter2_4(false_0(),|fun|,x,xs) -> filter_2(|fun|,xs) The estimated dependency graph contains the following SCCs: {p3, p4, p5, p10, p11, p12, p13, p14, p15} {p8} -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: map_2#(|fun|,cons_2(x,xs)) -> app#(|fun|,x) p2: app#(map_1(x1),y) -> map_2#(x1,y) p3: map_2#(|fun|,cons_2(x,xs)) -> map_2#(|fun|,xs) p4: app#(filter2_3(x1,x2,x3),y) -> filter2_4#(x1,x2,x3,y) p5: filter2_4#(false_0(),|fun|,x,xs) -> filter_2#(|fun|,xs) p6: filter_2#(|fun|,cons_2(x,xs)) -> app#(|fun|,x) p7: app#(filter_1(x1),y) -> filter_2#(x1,y) p8: filter_2#(|fun|,cons_2(x,xs)) -> filter2_4#(app(|fun|,x),|fun|,x,xs) p9: filter2_4#(true_0(),|fun|,x,xs) -> filter_2#(|fun|,xs) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(copy_0(),y) -> copy_1(y) r4: app(copy_1(x1),y) -> copy_2(x1,y) r5: app(copy_2(x1,x2),y) -> copy_3(x1,x2,y) r6: app(f_0(),y) -> f_1(y) r7: app(filter_0(),y) -> filter_1(y) r8: app(filter_1(x1),y) -> filter_2(x1,y) r9: app(filter2_0(),y) -> filter2_1(y) r10: app(filter2_1(x1),y) -> filter2_2(x1,y) r11: app(filter2_2(x1,x2),y) -> filter2_3(x1,x2,y) r12: app(filter2_3(x1,x2,x3),y) -> filter2_4(x1,x2,x3,y) r13: app(map_0(),y) -> map_1(y) r14: app(map_1(x1),y) -> map_2(x1,y) r15: app(s_0(),y) -> s_1(y) r16: f_1(cons_2(nil_0(),y)) -> y r17: f_1(cons_2(f_1(cons_2(nil_0(),y)),z)) -> copy_3(n_0(),y,z) r18: copy_3(|0|_0(),y,z) -> f_1(z) r19: copy_3(s_1(x),y,z) -> copy_3(x,y,cons_2(f_1(y),z)) r20: map_2(|fun|,nil_0()) -> nil_0() r21: map_2(|fun|,cons_2(x,xs)) -> cons_2(app(|fun|,x),map_2(|fun|,xs)) r22: filter_2(|fun|,nil_0()) -> nil_0() r23: filter_2(|fun|,cons_2(x,xs)) -> filter2_4(app(|fun|,x),|fun|,x,xs) r24: filter2_4(true_0(),|fun|,x,xs) -> cons_2(x,filter_2(|fun|,xs)) r25: filter2_4(false_0(),|fun|,x,xs) -> filter_2(|fun|,xs) The set of usable rules consists of r22 Take the reduction pair: generalized weighted path order (>=MG, >G) base order 1: matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: map_2#_A(x1,x2) = x2 cons_2_A(x1,x2) = x1 + x2 + 2 app#_A(x1,x2) = x2 + 1 map_1_A(x1) = x1 + 1 filter2_3_A(x1,x2,x3) = x1 + x2 + x3 + 4 filter2_4#_A(x1,x2,x3,x4) = x4 false_0_A() = 1 filter_2#_A(x1,x2) = x2 filter_1_A(x1) = x1 app_A(x1,x2) = x1 + x2 + 6 true_0_A() = 1 filter_2_A(x1,x2) = x1 + x2 + 3 nil_0_A() = 0 base order 2: marked-algebra reduction triple (>=A, >=A!, >A!): matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: map_2#_A(x1,x2) = x1 + x2 cons_2_A(x1,x2) = x1 + x2 + 5 app#_A(x1,x2) = x1 + x2 map_1_A(x1) = x1 + 3 filter2_3_A(x1,x2,x3) = x1 + x2 + x3 + 7 filter2_4#_A(x1,x2,x3,x4) = x2 + x4 false_0_A() = 1 filter_2#_A(x1,x2) = x1 + x2 filter_1_A(x1) = x1 + 3 app_A(x1,x2) = x1 + x2 + 1 true_0_A() = 1 filter_2_A(x1,x2) = x1 + x2 + 10 nil_0_A() = 6 map_2#!_A(x1,x2) = x1 + x2 + 18 cons_2!_A(x1,x2) = x2 + 13 app#!_A(x1,x2) = x1 + 14 map_1!_A(x1) = 3 filter2_3!_A(x1,x2,x3) = 15 filter2_4#!_A(x1,x2,x3,x4) = x2 + 14 false_0!_A() = 0 filter_2#!_A(x1,x2) = 1 filter_1!_A(x1) = 0 app!_A(x1,x2) = x2 + 30 true_0!_A() = 0 filter_2!_A(x1,x2) = 12 nil_0!_A() = 11 partial status: pi(map_2#) = [2] pi(cons_2) = [] pi(app#) = [2] pi(map_1) = [1] pi(filter2_3) = [3] pi(filter2_4#) = [4] pi(false_0) = [] pi(filter_2#) = [2] pi(filter_1) = [] pi(app) = [2] pi(true_0) = [] pi(filter_2) = [] pi(nil_0) = [] The following rules are removed: p1, p2, p3, p4, p5, p6, p7, p8, p9 Then no dependency pair remains. -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: copy_3#(s_1(x),y,z) -> copy_3#(x,y,cons_2(f_1(y),z)) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(copy_0(),y) -> copy_1(y) r4: app(copy_1(x1),y) -> copy_2(x1,y) r5: app(copy_2(x1,x2),y) -> copy_3(x1,x2,y) r6: app(f_0(),y) -> f_1(y) r7: app(filter_0(),y) -> filter_1(y) r8: app(filter_1(x1),y) -> filter_2(x1,y) r9: app(filter2_0(),y) -> filter2_1(y) r10: app(filter2_1(x1),y) -> filter2_2(x1,y) r11: app(filter2_2(x1,x2),y) -> filter2_3(x1,x2,y) r12: app(filter2_3(x1,x2,x3),y) -> filter2_4(x1,x2,x3,y) r13: app(map_0(),y) -> map_1(y) r14: app(map_1(x1),y) -> map_2(x1,y) r15: app(s_0(),y) -> s_1(y) r16: f_1(cons_2(nil_0(),y)) -> y r17: f_1(cons_2(f_1(cons_2(nil_0(),y)),z)) -> copy_3(n_0(),y,z) r18: copy_3(|0|_0(),y,z) -> f_1(z) r19: copy_3(s_1(x),y,z) -> copy_3(x,y,cons_2(f_1(y),z)) r20: map_2(|fun|,nil_0()) -> nil_0() r21: map_2(|fun|,cons_2(x,xs)) -> cons_2(app(|fun|,x),map_2(|fun|,xs)) r22: filter_2(|fun|,nil_0()) -> nil_0() r23: filter_2(|fun|,cons_2(x,xs)) -> filter2_4(app(|fun|,x),|fun|,x,xs) r24: filter2_4(true_0(),|fun|,x,xs) -> cons_2(x,filter_2(|fun|,xs)) r25: filter2_4(false_0(),|fun|,x,xs) -> filter_2(|fun|,xs) The set of usable rules consists of r16, r17, r22 Take the reduction pair: generalized weighted path order (>=MG, >G) base order 1: matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: copy_3#_A(x1,x2,x3) = x1 + x2 s_1_A(x1) = x1 + 9 cons_2_A(x1,x2) = x1 + x2 + 3 f_1_A(x1) = x1 + 7 nil_0_A() = 9 copy_3_A(x1,x2,x3) = 8 n_0_A() = 1 filter_2_A(x1,x2) = x1 + x2 + 3 base order 2: marked-algebra reduction triple (>=A, >=A!, >A!): matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: copy_3#_A(x1,x2,x3) = x1 + x3 s_1_A(x1) = x1 + 2 cons_2_A(x1,x2) = x2 + 2 f_1_A(x1) = x1 + 7 nil_0_A() = 8 copy_3_A(x1,x2,x3) = x1 + x3 + 2 n_0_A() = 2 filter_2_A(x1,x2) = x1 + x2 + 3 copy_3#!_A(x1,x2,x3) = x1 + x3 + 4 s_1!_A(x1) = 1 cons_2!_A(x1,x2) = x2 + 7 f_1!_A(x1) = 5 nil_0!_A() = 8 copy_3!_A(x1,x2,x3) = x1 + 2 n_0!_A() = 7 filter_2!_A(x1,x2) = 9 partial status: pi(copy_3#) = [1, 2] pi(s_1) = [1] pi(cons_2) = [1] pi(f_1) = [] pi(nil_0) = [] pi(copy_3) = [] pi(n_0) = [] pi(filter_2) = [] The following rules are removed: p1 Then no dependency pair remains.