YES We show the termination of the TRS R: app(app(filter(),f),nil()) -> nil() app(app(filter(),f),app(app(cons(),y),ys)) -> app(app(app(filtersub(),app(f,y)),f),app(app(cons(),y),ys)) app(app(app(filtersub(),true()),f),app(app(cons(),y),ys)) -> app(app(cons(),y),app(app(filter(),f),ys)) app(app(app(filtersub(),false()),f),app(app(cons(),y),ys)) -> app(app(filter(),f),ys) 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(filter_0(),y) -> filter_1(y) app(filter_1(x1),y) -> filter_2(x1,y) app(filtersub_0(),y) -> filtersub_1(y) app(filtersub_1(x1),y) -> filtersub_2(x1,y) app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) filter_2(f,nil_0()) -> nil_0() filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: app#(filter_1(x1),y) -> filter_2#(x1,y) p2: app#(filtersub_2(x1,x2),y) -> filtersub_3#(x1,x2,y) p3: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p4: filter_2#(f,cons_2(y,ys)) -> app#(f,y) p5: filtersub_3#(true_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) p6: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The estimated dependency graph contains the following SCCs: {p1, p2, p3, p4, p5, p6} -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: app#(filter_1(x1),y) -> filter_2#(x1,y) p2: filter_2#(f,cons_2(y,ys)) -> app#(f,y) p3: app#(filtersub_2(x1,x2),y) -> filtersub_3#(x1,x2,y) p4: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) p5: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p6: filtersub_3#(true_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The set of usable rules consists of r8 Take the reduction pair: generalized weighted path order (>=MG, >MG) base order 1: matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: app#_A(x1,x2) = x1 + 2 filter_1_A(x1) = x1 + 4 filter_2#_A(x1,x2) = x1 + 3 cons_2_A(x1,x2) = x1 + x2 + 5 filtersub_2_A(x1,x2) = x1 + x2 + 5 filtersub_3#_A(x1,x2,x3) = x2 + 3 false_0_A() = 1 app_A(x1,x2) = x1 + 2 true_0_A() = 1 filter_2_A(x1,x2) = x1 + x2 + 7 nil_0_A() = 1 base order 2: precedence: app# = filter_2 > filter_1 = cons_2 = nil_0 > filtersub_2 > filter_2# = filtersub_3# > false_0 = app = true_0 partial status: pi(app#) = [1] pi(filter_1) = [1] pi(filter_2#) = [] pi(cons_2) = [2] pi(filtersub_2) = [] pi(filtersub_3#) = [] pi(false_0) = [] pi(app) = [1] pi(true_0) = [] pi(filter_2) = [] pi(nil_0) = [] The following rules are removed: p1 -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: filter_2#(f,cons_2(y,ys)) -> app#(f,y) p2: app#(filtersub_2(x1,x2),y) -> filtersub_3#(x1,x2,y) p3: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) p4: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p5: filtersub_3#(true_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The estimated dependency graph contains the following SCCs: {p1, p2, p3, p4, p5} -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: filter_2#(f,cons_2(y,ys)) -> app#(f,y) p2: app#(filtersub_2(x1,x2),y) -> filtersub_3#(x1,x2,y) p3: filtersub_3#(true_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) p4: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p5: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The set of usable rules consists of r8 Take the reduction pair: generalized weighted path order (>=MG, >MG) base order 1: matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: filter_2#_A(x1,x2) = x1 + 3 cons_2_A(x1,x2) = x1 + x2 + 7 app#_A(x1,x2) = x1 filtersub_2_A(x1,x2) = x1 + x2 + 9 filtersub_3#_A(x1,x2,x3) = x2 + 3 true_0_A() = 1 app_A(x1,x2) = x1 + 2 false_0_A() = 4 filter_2_A(x1,x2) = x1 + x2 + 9 nil_0_A() = 1 base order 2: precedence: filter_2 > app# = app > filter_2# = cons_2 = filtersub_2 = filtersub_3# = nil_0 > true_0 = false_0 partial status: pi(filter_2#) = [] pi(cons_2) = [2] pi(app#) = [] pi(filtersub_2) = [2] pi(filtersub_3#) = [] pi(true_0) = [] pi(app) = [1] pi(false_0) = [] pi(filter_2) = [] pi(nil_0) = [] The following rules are removed: p1 -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: app#(filtersub_2(x1,x2),y) -> filtersub_3#(x1,x2,y) p2: filtersub_3#(true_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) p3: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p4: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The estimated dependency graph contains the following SCCs: {p2, p3, p4} -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: filtersub_3#(true_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) p2: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p3: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The set of usable rules consists of r1, r3, r5, r6, r8 Take the reduction pair: generalized weighted path order (>=MG, >MG) base order 1: matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: filtersub_3#_A(x1,x2,x3) = x2 + x3 + 2 true_0_A() = 1 cons_2_A(x1,x2) = x1 + x2 + 4 filter_2#_A(x1,x2) = x1 + x2 + 5 app_A(x1,x2) = x2 + 3 false_0_A() = 1 cons_0_A() = 2 cons_1_A(x1) = 1 filter_0_A() = 2 filter_1_A(x1) = 1 filtersub_0_A() = 3 filtersub_1_A(x1) = 2 filtersub_2_A(x1,x2) = 1 filter_2_A(x1,x2) = x1 + x2 + 7 nil_0_A() = 1 base order 2: precedence: filtersub_3# > filter_2# = filter_2 > cons_2 > true_0 = app = false_0 = cons_0 = filter_0 = filtersub_0 = nil_0 > cons_1 = filter_1 = filtersub_1 > filtersub_2 partial status: pi(filtersub_3#) = [] pi(true_0) = [] pi(cons_2) = [1] pi(filter_2#) = [] pi(app) = [2] pi(false_0) = [] pi(cons_0) = [] pi(cons_1) = [] pi(filter_0) = [] pi(filter_1) = [] pi(filtersub_0) = [] pi(filtersub_1) = [] pi(filtersub_2) = [] pi(filter_2) = [] pi(nil_0) = [] The following rules are removed: p1 -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p2: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The estimated dependency graph contains the following SCCs: {p1, p2} -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: filter_2#(f,cons_2(y,ys)) -> filtersub_3#(app(f,y),f,cons_2(y,ys)) p2: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The set of usable rules consists of r1, r2, r3, r4, r5, r6, r7, r8, r9, r10, r11 Take the reduction pair: generalized weighted path order (>=MG, >MG) base order 1: matrix interpretation with standard point-wise order: carrier: N^1 order: standard order interpretations: filter_2#_A(x1,x2) = 4 cons_2_A(x1,x2) = 1 filtersub_3#_A(x1,x2,x3) = x1 app_A(x1,x2) = 3 false_0_A() = 5 cons_0_A() = 0 cons_1_A(x1) = 0 filter_0_A() = 0 filter_1_A(x1) = 0 filter_2_A(x1,x2) = 2 filtersub_0_A() = 0 filtersub_1_A(x1) = 3 filtersub_2_A(x1,x2) = 0 filtersub_3_A(x1,x2,x3) = 2 nil_0_A() = 0 true_0_A() = 2 base order 2: precedence: cons_1 > app = cons_0 = filtersub_2 > filter_2# = filtersub_1 > filtersub_3# = filtersub_0 > cons_2 = filter_2 = filtersub_3 > false_0 = filter_1 = nil_0 = true_0 > filter_0 partial status: pi(filter_2#) = [] pi(cons_2) = [] pi(filtersub_3#) = [1] pi(app) = [] pi(false_0) = [] pi(cons_0) = [] pi(cons_1) = [] pi(filter_0) = [] pi(filter_1) = [] pi(filter_2) = [] pi(filtersub_0) = [] pi(filtersub_1) = [] pi(filtersub_2) = [] pi(filtersub_3) = [] pi(nil_0) = [] pi(true_0) = [] The following rules are removed: p1 -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: filtersub_3#(false_0(),f,cons_2(y,ys)) -> filter_2#(f,ys) and R consists of: r1: app(cons_0(),y) -> cons_1(y) r2: app(cons_1(x1),y) -> cons_2(x1,y) r3: app(filter_0(),y) -> filter_1(y) r4: app(filter_1(x1),y) -> filter_2(x1,y) r5: app(filtersub_0(),y) -> filtersub_1(y) r6: app(filtersub_1(x1),y) -> filtersub_2(x1,y) r7: app(filtersub_2(x1,x2),y) -> filtersub_3(x1,x2,y) r8: filter_2(f,nil_0()) -> nil_0() r9: filter_2(f,cons_2(y,ys)) -> filtersub_3(app(f,y),f,cons_2(y,ys)) r10: filtersub_3(true_0(),f,cons_2(y,ys)) -> cons_2(y,filter_2(f,ys)) r11: filtersub_3(false_0(),f,cons_2(y,ys)) -> filter_2(f,ys) The estimated dependency graph contains the following SCCs: (no SCCs)