On $$\Delta $$ Δ -modular integer linear problems in the canonical form and equivalent problems

Many papers in the field of integer linear programming (ILP, for short) are devoted to problems of the type $$\max \{c^\top x :A x = b,\, x \in {{\,\mathrm{\mathbb {Z}}\,}}^n_{\ge 0}\}$$ max { c ⊤ x : A x = b , x ∈ Z ≥ 0 n } , where all the entries of A, b, c are integer, parameterized by the number of rows of A and $$\Vert A\Vert _{\max }$$ ‖ A ‖ max . This class of problems is known under the name of ILP problems in the standard form, adding the word ”bounded” if $$x \le u$$ x ≤ u , for some integer vector u. Recently, many new sparsity, proximity, and complexity results were obtained for bounded and unbounded ILP problems in the standard form. In this paper, we consider ILP problems in the canonical form $$\begin{aligned} \max \{c^\top x :b_l \le A x \le b_r,\, x \in {{\,\mathrm{\mathbb {Z}}\,}}^n\}, \end{aligned}$$ max { c ⊤ x : b l ≤ A x ≤ b r , x ∈ Z n } , where $$b_l$$ b l and $$b_r$$ b r are integer vectors. We assume that the integer matrix A has the rank n, $$(n + m)$$ ( n + m ) rows, n columns, and parameterize the problem by m and $$\Delta (A)$$ Δ ( A ) , where $$\Delta (A)$$ Δ ( A ) is the maximum of $$n \times n$$ n × n sub-determinants of A, taken in the absolute value. We show that any ILP problem in the standard form can be polynomially reduced to some ILP problem in the canonical form, preserving m and $$\Delta (A)$$ Δ ( A ) , but the reverse reduction is not always possible. More precisely, we define the class of generalized ILP problems in the standard form, which includes an additional group constraint, and prove the equivalence to ILP problems in the canonical form. We generalize known sparsity, proximity, and complexity bounds for ILP problems in the canonical form. Additionally, sometimes, we strengthen previously known results for ILP problems in the canonical form, and, sometimes, we give shorter proofs. Finally, we consider the special cases of $$m \in \{0,1\}$$ m ∈ { 0 , 1 } . By this way, we give specialised sparsity, proximity, and complexity bounds for the problems on simplices, Knapsack problems and Subset-Sum problems.