refined hessian sparsity detection

Author: mlubin

src/Nonlinear/ReverseAD/graph_tools.jl

@@ -175,12 +175,76 @@ function _compute_gradient_sparsity!(
     return
 end
 
+"""
+    _get_nonlinear_child_interactions(
+        nod::Nonlinear.Node,
+        num_children::Int,
+    )
+
+Get the list of nonlinear child interaction pairs for a node.
+Returns empty list of tuples `(i, j)` where `i` and `j` are child indices (1-indexed)
+that have nonlinear interactions.
+
+For example, for `*` with 2 children, the result is `[(1, 2)]` because children 1
+and 2 interact nonlinearly, but children 1 and 1, or 2 and 2, do not.
+
+For functions like `+` or `-`, the result is `[]` since there are no nonlinear
+interactions between children.
+"""
+function _get_nonlinear_child_interactions(
+    nod::Nonlinear.Node,
+    num_children::Int,
+)::Vector{Tuple{Int,Int}}
+    if nod.type == Nonlinear.NODE_CALL_UNIVARIATE
+        @assert num_children == 1
+        op = get(Nonlinear.DEFAULT_UNIVARIATE_OPERATORS, nod.index, nothing)
+        # Univariate operators :+ and :- don't create interactions
+        if op in (:+, :-)
+            return Tuple{Int,Int}[]
+        else
+            return [(1, 1)]
+        end
+    elseif nod.type == Nonlinear.NODE_CALL_MULTIVARIATE
+        op = get(Nonlinear.DEFAULT_MULTIVARIATE_OPERATORS, nod.index, nothing)
+
+        if op in (:+, :-, :ifelse, :min, :max)
+            # No nonlinear interactions between children
+            return Tuple{Int,Int}[]
+        elseif op == :*
+            # All pairs of distinct children interact nonlinearly
+            result = Tuple{Int,Int}[]
+            for i in 1:num_children
+                for j in 1:(i-1)
+                    push!(result, (j, i))
+                end
+            end
+            return result
+        elseif op == :/
+            @assert num_children == 2
+            # The numerator doesn't have a nonlinear interaction with itself.
+            return [(1, 2), (2, 2)]
+        else
+            # Conservative: assume all pairs interact
+            result = Tuple{Int,Int}[]
+            for i in 1:num_children
+                for j in 1:i
+                    push!(result, (j, i))
+                end
+            end
+            return result
+        end
+    else
+        # Logic and comparison nodes don't generate hessian terms.
+        # Subexpression nodes are special cased.
+        return Tuple{Int,Int}[]
+    end
+end
+
 """
     _compute_hessian_sparsity(
         nodes::Vector{Nonlinear.Node},
         adj,
         input_linearity::Vector{Linearity},
-        indexedset::Coloring.IndexedSet,
         subexpression_edgelist::Vector{Set{Tuple{Int,Int}}},
         subexpression_variables::Vector{Vector{Int}},
     )
@@ -193,142 +257,129 @@ Compute the sparsity pattern the Hessian of an expression.
  * `subexpression_variables` is the list of all variables which appear in a
    subexpression (including recursively).
 
-Idea: consider the (non)linearity of a node *with respect to the output*. The
-children of any node which is nonlinear with respect to the output should have
-nonlinear interactions, hence nonzeros in the hessian. This is not true in
-general, but holds for everything we consider.
-
-A counter example is `f(x, y, z) = x + y * z`, but we don't have any functions
-like that. By "nonlinear with respect to the output", we mean that the output
-depends nonlinearly on the value of the node, regardless of how the node itself
-depends on the input.
+Returns a `Set{Tuple{Int,Int}}` containing the nonzero entries of the Hessian.
 """
 function _compute_hessian_sparsity(
     nodes::Vector{Nonlinear.Node},
     adj,
     input_linearity::Vector{Linearity},
-    indexedset::Coloring.IndexedSet,
     subexpression_edgelist::Vector{Set{Tuple{Int,Int}}},
     subexpression_variables::Vector{Vector{Int}},
 )
-    # So start at the root of the tree and classify the linearity wrt the output.
-    # For each nonlinear node, do a mini DFS and collect the list of children.
-    # Add a nonlinear interaction between all children of a nonlinear node.
     edge_list = Set{Tuple{Int,Int}}()
-    nonlinear_wrt_output = fill(false, length(nodes))
     children_arr = SparseArrays.rowvals(adj)
-    stack = Int[]
-    stack_ignore = Bool[]
-    nonlinear_group = indexedset
-    if length(nodes) == 1 && nodes[1].type == Nonlinear.NODE_SUBEXPRESSION
-        # Subexpression comes in linearly, so append edge_list
-        for ij in subexpression_edgelist[nodes[1].index]
-            push!(edge_list, ij)
-        end
-    end
-    for k in 2:length(nodes)
+
+    # Stack entry: (node_index, child_group_index)
+    stack = Tuple{Int,Int}[]
+    # Map from child_group_index to variable indices
+    child_group_variables = Dict{Int,Set{Int}}()
+
+    for k in 1:length(nodes)
         nod = nodes[k]
         @assert nod.type != Nonlinear.NODE_MOI_VARIABLE
-        if nonlinear_wrt_output[k]
-            continue # already seen this node one way or another
-        elseif input_linearity[k] == CONSTANT
-            continue # definitely not nonlinear
+
+        if input_linearity[k] == CONSTANT
+            continue  # No hessian contribution from constant nodes
         end
-        @assert !nonlinear_wrt_output[nod.parent]
-        # check if the parent depends nonlinearly on the value of this node
-        par = nodes[nod.parent]
-        if par.type == Nonlinear.NODE_CALL_UNIVARIATE
-            op = get(Nonlinear.DEFAULT_UNIVARIATE_OPERATORS, par.index, nothing)
-            if op === nothing || (op != :+ && op != :-)
-                nonlinear_wrt_output[k] = true
+
+        # Check if this node has nonlinear child interactions
+        children_idx = SparseArrays.nzrange(adj, k)
+        num_children = length(children_idx)
+        interactions = _get_nonlinear_child_interactions(nod, num_children)
+
+        if !isempty(interactions)
+            # This node has nonlinear child interactions, so collect variables from its children
+            empty!(child_group_variables)
+
+            # DFS from all children, tracking child index
+            for (child_position, cidx) in enumerate(children_idx)
+                child_node_idx = children_arr[cidx]
+                push!(stack, (child_node_idx, child_position))
             end
-        elseif par.type == Nonlinear.NODE_CALL_MULTIVARIATE
-            op = get(
-                Nonlinear.DEFAULT_MULTIVARIATE_OPERATORS,
-                par.index,
-                nothing,
-            )
-            if op === nothing
-                nonlinear_wrt_output[k] = true
-            elseif op in (:+, :-, :ifelse)
-                # pass
-            elseif op == :*
-                # check if all siblings are constant
-                sibling_idx = SparseArrays.nzrange(adj, nod.parent)
-                if !all(
-                    i ->
-                        input_linearity[children_arr[i]] == CONSTANT ||
-                        children_arr[i] == k,
-                    sibling_idx,
-                )
-                    # at least one sibling isn't constant
-                    nonlinear_wrt_output[k] = true
+
+            while length(stack) > 0
+                r, child_group_idx = pop!(stack)
+
+                # Don't traverse into logical conditions or comparisons
+                if nodes[r].type == Nonlinear.NODE_LOGIC ||
+                   nodes[r].type == Nonlinear.NODE_COMPARISON
+                    continue
                 end
-            elseif op == :/
-                # check if denominator is nonconstant
-                sibling_idx = SparseArrays.nzrange(adj, nod.parent)
-                if input_linearity[children_arr[last(sibling_idx)]] != CONSTANT
-                    nonlinear_wrt_output[k] = true
+
+                r_children_idx = SparseArrays.nzrange(adj, r)
+                for cidx in r_children_idx
+                    push!(stack, (children_arr[cidx], child_group_idx))
+                end
+
+                if nodes[r].type == Nonlinear.NODE_VARIABLE
+                    if !haskey(child_group_variables, child_group_idx)
+                        child_group_variables[child_group_idx] = Set{Int}()
+                    end
+                    push!(
+                        child_group_variables[child_group_idx],
+                        nodes[r].index,
+                    )
+                elseif nodes[r].type == Nonlinear.NODE_SUBEXPRESSION
+                    sub_vars = subexpression_variables[nodes[r].index]
+                    if !haskey(child_group_variables, child_group_idx)
+                        child_group_variables[child_group_idx] = Set{Int}()
+                    end
+                    union!(child_group_variables[child_group_idx], sub_vars)
                 end
-            else
-                nonlinear_wrt_output[k] = true
             end
-        end
-        if nod.type == Nonlinear.NODE_SUBEXPRESSION && !nonlinear_wrt_output[k]
-            # subexpression comes in linearly, so append edge_list
+
+            println(child_group_variables)
+            _add_hessian_edges!(edge_list, interactions, child_group_variables)
+        elseif nod.type == Nonlinear.NODE_SUBEXPRESSION
             for ij in subexpression_edgelist[nod.index]
                 push!(edge_list, ij)
             end
         end
-        if !nonlinear_wrt_output[k]
-            continue
-        end
-        # do a DFS from here, including all children
-        @assert isempty(stack)
-        @assert isempty(stack_ignore)
-        sibling_idx = SparseArrays.nzrange(adj, nod.parent)
-        for sidx in sibling_idx
-            push!(stack, children_arr[sidx])
-            push!(stack_ignore, false)
-        end
-        empty!(nonlinear_group)
-        while length(stack) > 0
-            r = pop!(stack)
-            should_ignore = pop!(stack_ignore)
-            nonlinear_wrt_output[r] = true
-            if nodes[r].type == Nonlinear.NODE_LOGIC ||
-               nodes[r].type == Nonlinear.NODE_COMPARISON
-                # don't count the nonlinear interactions inside
-                # logical conditions or comparisons
-                should_ignore = true
-            end
-            children_idx = SparseArrays.nzrange(adj, r)
-            for cidx in children_idx
-                push!(stack, children_arr[cidx])
-                push!(stack_ignore, should_ignore)
-            end
-            if should_ignore
-                continue
-            end
-            if nodes[r].type == Nonlinear.NODE_VARIABLE
-                push!(nonlinear_group, nodes[r].index)
-            elseif nodes[r].type == Nonlinear.NODE_SUBEXPRESSION
-                # append all variables in subexpression
-                union!(nonlinear_group, subexpression_variables[nodes[r].index])
+    end
+    return edge_list
+end
+
+"""
+    _add_hessian_edges!(
+        edge_list::Set{Tuple{Int,Int}},
+        interactions::Vector{Tuple{Int,Int}},
+        child_variables::Dict{Int,Set{Int}},
+    )
+
+Add hessian edges based on the operator's nonlinear interaction pattern.
+"""
+function _add_hessian_edges!(
+    edge_list::Set{Tuple{Int,Int}},
+    interactions::Vector{Tuple{Int,Int}},
+    child_variables::Dict{Int,Set{Int}},
+)
+    for (child_i, child_j) in interactions
+        if child_i == child_j
+            # Within-child interactions: add all pairs from a single child
+            if haskey(child_variables, child_i)
+                vars = child_variables[child_i]
+                for vi in vars
+                    for vj in vars
+                        i, j = minmax(vi, vj)
+                        push!(edge_list, (j, i))
+                    end
+                end
             end
-        end
-        for i_ in 1:nonlinear_group.nnz
-            i = nonlinear_group.nzidx[i_]
-            for j_ in 1:nonlinear_group.nnz
-                j = nonlinear_group.nzidx[j_]
-                if j > i
-                    continue  # Only lower triangle.
+        else
+            # Between-child interactions: add pairs from different children
+            if haskey(child_variables, child_i) &&
+               haskey(child_variables, child_j)
+                vars_i = child_variables[child_i]
+                vars_j = child_variables[child_j]
+                for vi in vars_i
+                    for vj in vars_j
+                        i, j = minmax(vi, vj)
+                        push!(edge_list, (j, i))
+                    end
                 end
-                push!(edge_list, (i, j))
             end
         end
     end
-    return edge_list
 end
 
 """

src/Nonlinear/ReverseAD/mathoptinterface_api.jl

@@ -93,7 +93,6 @@ function MOI.initialize(d::NLPEvaluator, requested_features::Vector{Symbol})
                 subex.nodes,
                 subex.adj,
                 linearity,
-                coloring_storage,
                 subexpression_edgelist,
                 subexpression_variables,
             )

src/Nonlinear/ReverseAD/types.jl

@@ -91,7 +91,6 @@ struct _FunctionStorage
                 nodes,
                 adj,
                 linearity,
-                coloring_storage,
                 subexpression_edgelist,
                 subexpression_variables,
             )

test/Nonlinear/ReverseAD.jl

@@ -561,7 +561,6 @@ function test_linearity()
             nodes,
             adj,
             ret,
-            indexed_set,
             Set{Tuple{Int,Int}}[],
             Vector{Int}[],
         )
@@ -585,12 +584,7 @@ function test_linearity()
         [1, 2],
     )
     _test_linearity(:(3 * 4 * ($x + $y)), ReverseAD.LINEAR)
-    _test_linearity(
-        :($z * $y),
-        ReverseAD.NONLINEAR,
-        Set([(3, 2), (3, 3), (2, 2)]),
-        [2, 3],
-    )
+    _test_linearity(:($z * $y), ReverseAD.NONLINEAR, Set([(3, 2)]), [2, 3])
     _test_linearity(:(3 + 4), ReverseAD.CONSTANT)
     _test_linearity(:(sin(3) + $x), ReverseAD.LINEAR)
     _test_linearity(
@@ -635,6 +629,12 @@ function test_linearity()
         Set([(1, 1)]),
         [1],
     )
+    _test_linearity(
+        :(($x + $y)/$z),
+        ReverseAD.NONLINEAR,
+        Set([(3, 3), (3, 2), (3, 1)]),
+        [1, 2, 3],
+    )
     return
 end
 
@@ -1416,7 +1416,7 @@ function test_hessian_reinterpret_unsafe()
     x_v = ones(5)
     MOI.eval_hessian_lagrangian(evaluator, H, x_v, 0.0, [1.0, 1.0])
     @test count(isapprox.(H, 1.0; atol = 1e-8)) == 3
-    @test count(isapprox.(H, 0.0; atol = 1e-8)) == 6
+    @test count(isapprox.(H, 0.0; atol = 1e-8)) == 5
     @test sort(H_s[round.(Bool, H)]) == [(3, 1), (3, 2), (5, 4)]
     return
 end