YES We show the termination of the TRS 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)) -- SCC decomposition. Consider the 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)) The estimated dependency graph contains the following SCCs: {p12, p15, p18} {p8, p10, p11} {p1} {p4, p6, p7} {p2} {p3} -- Reduction pair. Consider the 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)) 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 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: if_minsort#_A(x1,x2,x3) = x2 + x3 + 8 false_A() = 1 add_A(x1,x2) = x1 + x2 + 6 minsort#_A(x1,x2) = x1 + x2 + 8 eq_A(x1,x2) = x2 + 5 min_A(x1) = x1 + 2 true_A() = 2 app_A(x1,x2) = x1 + x2 + 1 rm_A(x1,x2) = x1 + x2 + 3 nil_A() = 1 |0|_A() = 3 s_A(x1) = x1 + 6 le_A(x1,x2) = x1 + x2 + 13 if_min_A(x1,x2) = x2 + 1 if_rm_A(x1,x2,x3) = x2 + x3 + 3 minsort_A(x1,x2) = x2 + 2 base order 2: precedence: if_minsort# = minsort# = if_min > min = rm > app = le = if_rm > add = eq = |0| = minsort > true = s > false = nil partial status: pi(if_minsort#) = [] pi(false) = [] pi(add) = [1] pi(minsort#) = [] pi(eq) = [] pi(min) = [] pi(true) = [] pi(app) = [] pi(rm) = [] pi(nil) = [] pi(|0|) = [] pi(s) = [1] pi(le) = [] pi(if_min) = [2] pi(if_rm) = [3] pi(minsort) = [] The following rules are removed: p3 -- SCC decomposition. Consider the 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) 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)) The estimated dependency graph contains the following SCCs: {p1, p2} -- Reduction pair. Consider the 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) 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)) The set of usable rules consists of r1, r2, r3, r4, r5, r6, r7, r10, r11, r12, r13, r14, r18 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: if_minsort#_A(x1,x2,x3) = x2 false_A() = 5 add_A(x1,x2) = x1 + x2 + 7 minsort#_A(x1,x2) = x1 + 6 eq_A(x1,x2) = x1 + 8 min_A(x1) = x1 + 2 |0|_A() = 6 true_A() = 3 s_A(x1) = x1 + 9 le_A(x1,x2) = x1 + x2 + 15 nil_A() = 1 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = 4 minsort_A(x1,x2) = x2 + 1 base order 2: precedence: minsort# = if_min > min > eq = |0| = le > true > add = s = minsort > if_minsort# = false > rm > nil partial status: pi(if_minsort#) = [] pi(false) = [] pi(add) = [] pi(minsort#) = [1] pi(eq) = [] pi(min) = [] pi(|0|) = [] pi(true) = [] pi(s) = [1] pi(le) = [1] pi(nil) = [] pi(if_min) = [2] pi(rm) = [] pi(minsort) = [] The following rules are removed: p1 -- SCC decomposition. Consider the 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) 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)) The estimated dependency graph contains the following SCCs: (no SCCs) -- Reduction pair. Consider the 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)) The set of usable rules consists of r1, r2, r3, r4, r5, r6, r7, r10, r11, r12, r13, r14, r18 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: if_rm#_A(x1,x2,x3) = x3 false_A() = 1 add_A(x1,x2) = x1 + x2 + 5 rm#_A(x1,x2) = x2 + 4 eq_A(x1,x2) = x1 + x2 + 8 true_A() = 8 |0|_A() = 9 s_A(x1) = x1 + 2 le_A(x1,x2) = x1 + x2 + 11 min_A(x1) = x1 + 4 nil_A() = 1 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = x1 + x2 + 2 minsort_A(x1,x2) = x2 + 6 base order 2: precedence: if_min > min > eq = |0| = le > true > minsort > add > if_rm# > rm# = s = rm > false = nil partial status: pi(if_rm#) = [] pi(false) = [] pi(add) = [2] pi(rm#) = [] pi(eq) = [] pi(true) = [] pi(|0|) = [] pi(s) = [] pi(le) = [] pi(min) = [] pi(nil) = [] pi(if_min) = [2] pi(rm) = [] pi(minsort) = [] The following rules are removed: p1 -- SCC decomposition. Consider the 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)) The estimated dependency graph contains the following SCCs: {p1, p2} -- Reduction pair. Consider the 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)) The set of usable rules consists of r2, r3, r5, r6, r10, r11, r12, r13, r14, r18 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: rm#_A(x1,x2) = x2 + 1 add_A(x1,x2) = x1 + x2 + 6 if_rm#_A(x1,x2,x3) = x3 + 1 eq_A(x1,x2) = x1 + 1 true_A() = 12 |0|_A() = 10 s_A(x1) = 10 false_A() = 9 le_A(x1,x2) = x1 + x2 + 5 min_A(x1) = x1 + 7 nil_A() = 1 if_min_A(x1,x2) = x2 + 7 rm_A(x1,x2) = x1 + 2 minsort_A(x1,x2) = x2 + 4 base order 2: precedence: |0| = le = min > false > add = if_min > rm# > if_rm# = true = s > eq > minsort > rm > nil partial status: pi(rm#) = [] pi(add) = [1, 2] pi(if_rm#) = [3] pi(eq) = [] pi(true) = [] pi(|0|) = [] pi(s) = [] pi(false) = [] pi(le) = [2] pi(min) = [1] pi(nil) = [] pi(if_min) = [2] pi(rm) = [1] pi(minsort) = [] The following rules are removed: p1 -- SCC decomposition. Consider the 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)) The estimated dependency graph contains the following SCCs: (no SCCs) -- Reduction pair. Consider the 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)) The set of usable rules consists of r1, r2, r3, r4, r5, r6, r7, r10, r11, r12, r13, r14, r18 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: eq#_A(x1,x2) = x1 + x2 + 11 s_A(x1) = x1 + 10 eq_A(x1,x2) = x1 + x2 + 11 |0|_A() = 2 true_A() = 1 false_A() = 1 le_A(x1,x2) = x1 + 9 min_A(x1) = x1 + 2 add_A(x1,x2) = x1 + x2 + 4 nil_A() = 5 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = x1 + x2 + 2 minsort_A(x1,x2) = x2 + 1 base order 2: precedence: min = nil = if_min > minsort > eq# = eq = le > s = add > |0| = rm > true = false partial status: pi(eq#) = [2] pi(s) = [] pi(eq) = [] pi(|0|) = [] pi(true) = [] pi(false) = [] pi(le) = [] pi(min) = [1] pi(add) = [] pi(nil) = [] pi(if_min) = [2] pi(rm) = [] pi(minsort) = [] The following rules are removed: p1 Then no dependency pair remains. -- Reduction pair. Consider the 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)) The set of usable rules consists of r1, r2, r3, r4, r5, r6, r7, r10, r11, r12, r13, r14, r18 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: if_min#_A(x1,x2) = x2 + 1 false_A() = 1 add_A(x1,x2) = x1 + x2 + 5 min#_A(x1) = x1 + 2 le_A(x1,x2) = x1 + x2 + 6 true_A() = 8 eq_A(x1,x2) = x1 + x2 + 8 |0|_A() = 9 s_A(x1) = x1 + 2 min_A(x1) = x1 + 4 nil_A() = 4 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = x1 + x2 + 2 minsort_A(x1,x2) = x2 + 2 base order 2: precedence: if_min# > min# = eq = |0| > true > minsort > add = min > le = if_min > s = rm > false = nil partial status: pi(if_min#) = [] pi(false) = [] pi(add) = [1] pi(min#) = [] pi(le) = [] pi(true) = [] pi(eq) = [] pi(|0|) = [] pi(s) = [1] pi(min) = [] pi(nil) = [] pi(if_min) = [2] pi(rm) = [] pi(minsort) = [] The following rules are removed: p1 -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: min#(add(n,add(m,x))) -> if_min#(le(n,m),add(n,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)) The estimated dependency graph contains the following SCCs: {p1, p2} -- Reduction pair. Consider the dependency pair problem (P, R), where P consists of p1: min#(add(n,add(m,x))) -> if_min#(le(n,m),add(n,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)) The set of usable rules consists of r1, r2, r3, r4, r6, r10, r11, r12, r13, r14, r18 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: min#_A(x1) = x1 + 1 add_A(x1,x2) = x1 + x2 + 7 if_min#_A(x1,x2) = x2 le_A(x1,x2) = x1 + 3 true_A() = 6 eq_A(x1,x2) = x1 + x2 + 16 |0|_A() = 2 s_A(x1) = x1 + 2 false_A() = 1 min_A(x1) = x1 + 2 nil_A() = 5 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = x2 + 8 minsort_A(x1,x2) = x2 + 3 base order 2: precedence: eq > min# = |0| = rm = minsort > true > min = if_min > add > if_min# = le > s = nil > false partial status: pi(min#) = [1] pi(add) = [1] pi(if_min#) = [] pi(le) = [] pi(true) = [] pi(eq) = [] pi(|0|) = [] pi(s) = [1] pi(false) = [] pi(min) = [] pi(nil) = [] pi(if_min) = [2] pi(rm) = [] pi(minsort) = [] The following rules are removed: p1 -- SCC decomposition. Consider the dependency pair problem (P, R), where P consists of p1: 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)) The estimated dependency graph contains the following SCCs: (no SCCs) -- Reduction pair. Consider the 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)) The set of usable rules consists of r1, r2, r3, r4, r5, r6, r7, r10, r11, r12, r13, r14, r18 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: le#_A(x1,x2) = x1 + x2 s_A(x1) = x1 + 10 eq_A(x1,x2) = x1 + x2 + 11 |0|_A() = 2 true_A() = 1 false_A() = 1 le_A(x1,x2) = x1 + 9 min_A(x1) = x1 + 2 add_A(x1,x2) = x1 + x2 + 4 nil_A() = 5 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = x1 + x2 + 2 minsort_A(x1,x2) = x2 + 1 base order 2: precedence: min = nil = if_min > minsort > le# = eq = le > s = add > |0| = rm > true = false partial status: pi(le#) = [2] pi(s) = [] pi(eq) = [] pi(|0|) = [] pi(true) = [] pi(false) = [] pi(le) = [] pi(min) = [1] pi(add) = [] pi(nil) = [] pi(if_min) = [2] pi(rm) = [] pi(minsort) = [] The following rules are removed: p1 Then no dependency pair remains. -- Reduction pair. Consider the 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)) 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 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 + x2 + 6 add_A(x1,x2) = x1 + x2 + 5 eq_A(x1,x2) = x1 + x2 + 6 |0|_A() = 9 true_A() = 8 s_A(x1) = x1 + 2 false_A() = 1 le_A(x1,x2) = x1 + x2 + 6 app_A(x1,x2) = x1 + x2 + 7 nil_A() = 1 min_A(x1) = x1 + 2 if_min_A(x1,x2) = x2 + 1 rm_A(x1,x2) = x1 + x2 + 4 if_rm_A(x1,x2,x3) = x2 + x3 + 4 minsort_A(x1,x2) = x2 + 3 base order 2: precedence: if_min > |0| = app = min = rm > true > minsort > app# = eq = if_rm > add > le = nil > s = false partial status: pi(app#) = [2] pi(add) = [] pi(eq) = [2] pi(|0|) = [] pi(true) = [] pi(s) = [1] pi(false) = [] pi(le) = [1] pi(app) = [] pi(nil) = [] pi(min) = [1] pi(if_min) = [2] pi(rm) = [] pi(if_rm) = [3] pi(minsort) = [] The following rules are removed: p1 Then no dependency pair remains.