Report absent solution values instead of returning success - #1734
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cuOptGetPrimalSolution, cuOptGetDualSolution and cuOptGetReducedCosts copy into a caller-allocated buffer and report no length. When the underlying vector is empty the memcpy copies nothing, the function returns CUOPT_SUCCESS, and the caller's buffer keeps whatever it already held. "No values" is therefore indistinguishable from "the values are all zero", and a caller that zeroed its buffer first reads zeros and believes them. Confirmed against an infeasible LP (x >= 2 and x <= 1): all three return CUOPT_SUCCESS and leave a -12345 sentinel untouched. It affects the primal accessor too, not only the dual and reduced-cost ones as originally reported. Return CUOPT_INVALID_ARGUMENT when there are no values to copy, matching what cuOptGetDualSolution already returns when the underlying call throws, so the two paths agree. cuOptGetPrimalSolution had no exception guard at all and now has one. Existing callers are unaffected on a solve that produced values, and the in-tree C examples already branch on the status code, so they report the failure rather than printing uninitialised memory. Tests cover both directions: an infeasible solve must report the absence and leave the buffers untouched, and a solved LP must still return values. Fixes #1706. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
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Included review availability: Your plan includes up to 12 reviews per rolling hour; 9 remain after this review. 📝 WalkthroughWalkthroughThe C API solution accessors now reject unavailable solution data. Tests cover infeasible and constraint-free optimal problems. ChangesSolution accessor behavior
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to The getters now report an error when no solution values exist while preserving the caller’s buffer, without changing successful solves. No actionable merge-blocking risk remains beyond normal checks and review. Suggested reviewers: 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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ramakrishnap-nv
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CI Test Summary✅ All 31 test job(s) passed. |
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| // x >= 2 and x <= 1 has no feasible point, so the solve returns no primal, dual, or reduced | ||
| // cost values. | ||
| cuOptSolution solve_infeasible_problem() |
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It is only used within the test solution_accessors_report_absent_values. We should unroll the function into the test.
| // An empty vector means the solve produced no values, not that every value is zero. Copying | ||
| // nothing and reporting success would leave the caller's buffer at whatever it held and give | ||
| // them no way to tell the difference. | ||
| if (solution_host.empty()) { return CUOPT_INVALID_ARGUMENT; } |
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Shall we define a new status constant, like CUOPT_INVALID_OUTPUT, rather than using CUOPT_INVALID_ARGUMENT?
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It is the solution that is invalid here. So INVALID_ARGUMENT is a fine return.
| * num_variables that will contain the solution values. | ||
| * | ||
| * @return A status code indicating success or failure. | ||
| * @return A status code indicating success or failure. Returns CUOPT_INVALID_ARGUMENT if the |
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Better to keep the original comment.
| * num_constraints that will contain the dual solution. | ||
| * | ||
| * @return A status code indicating success or failure. | ||
| * @return A status code indicating success or failure. Returns CUOPT_INVALID_ARGUMENT if the |
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Better to keep the original comment.
| * num_variables that will contain the reduced cost. | ||
| * | ||
| * @return A status code indicating success or failure. | ||
| * @return A status code indicating success or failure. Returns CUOPT_INVALID_ARGUMENT if the |
They belong to NVIDIA#1734 and were swept in by a git add -A while that fix was copied into this worktree to build against. This branch should carry only the Java module; the C API arrives from main once NVIDIA#1734 merges. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
Per review: solve_infeasible_problem had one caller, so it does not earn a helper. solve_tiny_problem stays, having three. Verified: 71/71 C_API_TEST cases pass. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
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Thanks @yuwenchen95, @chris-maes — all three points addressed. Helper unrolled (
Status code — you two land on opposite sides, so I have followed @chris-maes and kept Wiring the Java bindings against this fix, the JNI has to treat So a distinct constant would let callers separate the two, which is @yuwenchen95's point. Against it: it is a new public constant, and callers that branch on One consequence of reverting the documentation: the fact that these accessors can now return |
Per review, the @return lines go back to 'A status code indicating success or failure.' with nothing appended. No code change; the tests still cover the behaviour. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
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Inline comments:
In `@cpp/src/pdlp/cuopt_c.cpp`:
- Line 1272: Update the result-validation checks around solution_host and the
corresponding primal, dual, and reduced-cost vectors to use explicit solution
availability rather than vector empty() checks, so valid zero-variable and
zero-constraint results are accepted; alternatively, explicitly reject those
model shapes before solving.
In `@cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp`:
- Around line 1299-1301: Strengthen the output-buffer assertions in
cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp at lines 1299-1301 by
asserting dual[1] remains sentinel, and at lines 1321-1323 by asserting
primal[1] and reduced[1] no longer equal sentinel. Use the existing test
assertions and preserve the current checks.
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| // An empty vector means the solve produced no values, not that every value is zero. Copying | ||
| // nothing and reporting success would leave the caller's buffer at whatever it held and give | ||
| // them no way to tell the difference. | ||
| if (solution_host.empty()) { return CUOPT_INVALID_ARGUMENT; } |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
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#!/bin/bash
set -euo pipefail
ast-grep outline cpp/src/pdlp/cuopt_c.cpp \
--match 'cuOptCreateProblem|cuOptGetPrimalSolution|cuOptGetDualSolution|cuOptGetReducedCosts' \
--view expanded
rg -n -C 5 --glob '*.{cpp,hpp,h,cu,cuh}' \
'\bnum_variables\b.*(==|<=|<)\s*0|\bnum_variables\b.*(>|>=)\s*0|cuOptCreateProblem\(0' \
cppRepository: NVIDIA/cuopt
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#!/bin/bash
set -euo pipefail
printf '%s\n' '--- cuOptCreateProblem and nearby code ---'
sed -n '300,470p' cpp/src/pdlp/cuopt_c.cpp
printf '%s\n' '--- solution accessors and nearby code ---'
sed -n '1230,1405p' cpp/src/pdlp/cuopt_c.cpp
printf '%s\n' '--- relevant symbols and declarations ---'
rg -n -C 4 \
'\b(cuOptCreateProblem|cuOptGetPrimalSolution|cuOptGetDualSolution|cuOptGetReducedCosts|solution_host|primal_solution|reduced_cost)\b' \
cpp/src/pdlp cpp/src/tests python 2>/dev/null | head -n 500Repository: NVIDIA/cuopt
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printf '%s\n' '--- model setter declarations and definitions ---'
rg -n -C 5 \
'set_objective_coefficients|set_variable_lower_bounds|set_variable_upper_bounds|set_variable_types|get_n_variables' \
cpp/src cpp/include --glob '*.{cpp,cu,h,hpp,cuh,cuh}' 2>/dev/null | head -n 800
printf '%s\n' '--- zero-dimension validation and solver guards ---'
rg -n -C 5 \
'n_variables|num_variables|get_n_variables|variable_count|number_of_variables' \
cpp/src --glob '*.{cpp,cu,h,hpp,cuh}' 2>/dev/null \
| rg -C 3 '0|empty|invalid|expect|assert|check|size' \
| head -n 800Repository: NVIDIA/cuopt
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printf '%s\n' '--- explicit zero-variable handling ---'
rg -n -C 5 \
'n_variables\s*(==|<=|<)\s*0|num_variables\s*(==|<=|<)\s*0|n_cols\s*==\s*0|n_vars\s*==\s*0' \
cpp/src cpp/include --glob '*.{cpp,cu,h,hpp,cuh}' 2>/dev/null | head -n 1000
printf '%s\n' '--- zero-variable tests and model construction ---'
rg -n -C 6 \
'zero[-_ ]variable|num_variables\s*=\s*0|n_variables\s*=\s*0|CreateProblem\s*\(' \
cpp/src/tests cpp/tests python --glob '*.{cpp,cu,h,hpp,py}' 2>/dev/null | head -n 1000
printf '%s\n' '--- solution vector initialization and empty-result paths ---'
rg -n -C 5 \
'optimization_problem_solution_t|primal_solution_|dual_solution_|reduced_cost_|get_solution_host|get_dual_solution|get_reduced_costs' \
cpp/src/pdlp --glob '*.{cpp,cu,h,hpp,cuh}' 2>/dev/null | head -n 1000Repository: NVIDIA/cuopt
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printf '%s\n' '--- empty-problem solve paths ---'
rg -n -C 12 '\bproblem\.empty\b|\bempty\b.*solution|solution.*empty|n_variables == 0' \
cpp/src/pdlp cpp/src/mip_heuristics --glob '*.{cpp,cu,h,hpp,cuh}' 2>/dev/null | head -n 1200
printf '%s\n' '--- solution implementation and getters ---'
rg -n -C 8 \
'optimization_problem_solution_t|solution_host_|get_solution_host|get_dual_solution|get_reduced_costs|primal_solution_' \
cpp/include cpp/src --glob '*.{cpp,cu,h,hpp,cuh}' 2>/dev/null | head -n 1600Repository: NVIDIA/cuopt
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#!/bin/bash
set -euo pipefail
python3 - <<'PY'
from pathlib import Path
c_api = Path("cpp/src/pdlp/cuopt_c.cpp").read_text()
problem = Path("cpp/src/mip_heuristics/problem/problem.cu").read_text()
solve = Path("cpp/src/mip_heuristics/solve.cu").read_text()
trivial = Path("cpp/src/mip_heuristics/presolve/trivial_presolve.cuh").read_text()
data_model = Path("cpp/src/io/data_model_view.cpp").read_text()
solution = Path("cpp/include/cuopt/mathematical_optimization/cpu_optimization_problem_solution.hpp").read_text()
checks = {
"C API creation has no num_variables nonpositive guard": "if (num_variables <= 0)" not in c_api[c_api.index("cuopt_int_t cuOptCreateProblem"):c_api.index("cuopt_int_t cuOptCreateRangedProblem")],
"model representation accepts zero variables": "if (n_variables == 0)" in problem and "objective_coefficients.is_empty()" in problem,
"zero-variable problem is marked empty": "if (problem.n_variables == 0) { problem.empty = true; }" in trivial,
"empty solve constructs a solution": "if (problem.empty)" in solve and "mip::solution_t<i_t, f_t> solution(problem);" in solve,
"host getters expose stored vectors": "get_primal_solution_host() const override { return primal_solution_; }" in solution,
"primal accessor rejects empty": "if (solution_host.empty()) { return CUOPT_INVALID_ARGUMENT; }" in c_api,
"dual accessor rejects empty": "if (dual_host.empty()) { return CUOPT_INVALID_ARGUMENT; }" in c_api,
"reduced-cost accessor rejects empty": "if (reduced_cost_host.empty()) { return CUOPT_INVALID_ARGUMENT; }" in c_api,
}
for name, ok in checks.items():
print(f"{'PASS' if ok else 'FAIL'}: {name}")
if not all(
("PASS" if ok else "FAIL") == "PASS" for ok in checks.values()
):
raise SystemExit(1)
PY
printf '%s\n' '--- exact solution sizing/getters ---'
rg -n -C 6 \
'solution_t\(.*problem|assignment\(|get_primal_solution_host|get_dual_solution_host|get_reduced_cost_host|get_reduced_costs' \
cpp/include/cuopt cpp/src/mip_heuristics cpp/src/pdlp --glob '*.{cpp,cu,h,hpp,cuh}' 2>/dev/null | head -n 500Repository: NVIDIA/cuopt
Length of output: 49530
Use solution availability instead of empty()
The model and solver support zero-variable and zero-constraint problems. Valid results can therefore have empty primal, dual, or reduced-cost vectors. The checks at lines 1272, 1338, and 1371 return CUOPT_INVALID_ARGUMENT for these results. Track availability separately from vector length, or reject these model shapes explicitly.
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In `@cpp/src/pdlp/cuopt_c.cpp` at line 1272, Update the result-validation checks
around solution_host and the corresponding primal, dual, and reduced-cost
vectors to use explicit solution availability rather than vector empty() checks,
so valid zero-variable and zero-constraint results are accepted; alternatively,
explicitly reject those model shapes before solving.
CodeRabbit pointed out that an empty vector does not always mean the values are missing, and it was right. Reproduced: a box-constrained LP with no constraints solves to optimality, and its dual vector is legitimately empty, so the previous check reported CUOPT_INVALID_ARGUMENT for a valid result. The declared sizes separate the two cases. For the infeasible problem the solution carries nothing at all, primal, dual and reduced cost all zero length. For the zero-constraint problem the primal and reduced-cost vectors have their expected length and only the dual is empty, because there are no constraints to have duals for. So all three accessors now ask whether the solve produced a solution rather than whether the vector being requested is empty, which leaves an empty dual vector as a valid result to copy nothing from. Also per review, the tests assert every element of each output buffer rather than the first, so an accessor that filled only a prefix cannot pass, and a new test covers the zero-constraint case that was regressing. Verified: 72/72 C_API_TEST cases pass. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
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@coderabbitai both findings were valid, fixed in Empty vector is not the same as unavailable. You were right, and it was a live regression rather than a theoretical one. I reproduced it: a box-constrained LP with no constraints solves to The declared sizes on the solution interface separate the two cases:
An infeasible solve carries nothing at all; the zero-constraint solve carries its primal and reduced-cost vectors at full length and only the dual is empty, because there are no constraints to have duals for. All three accessors now ask whether the solve produced a solution, rather than whether the vector being requested happens to be empty. An empty dual on a solved problem is a valid result to copy nothing from. Assert every element. Also right — a partial copy would have passed. Added |
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Inline comments:
In `@cpp/tests/linear_programming/c_api_tests/c_api_tests.cpp`:
- Around line 1375-1385: Add an assertion after the cuOptGetDualSolution call in
the test to verify dual[0] still equals its sentinel value, confirming the empty
dual result does not modify the caller buffer.
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The zero-constraint test checked that cuOptGetDualSolution reports success but not that it left the caller's buffer alone, so an accessor that wrote an element by mistake would still have passed. Verified: 72/72 C_API_TEST cases pass. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
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@coderabbitai fixed in the latest push. You are right, and it is the same gap you flagged earlier now showing up in the test I added to fix it: the zero-constraint case checked that 72/72 |
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| // vector. Copying nothing and reporting success would leave the caller's buffer at whatever | ||
| // it held and give them no way to tell that from a real result. | ||
| // | ||
| // The test is whether the solve produced a primal vector, not whether the vector being asked |
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Please delete the historical narration from the comments. I don't think if (solution_host.empty()) needs 6 lines of comments, if any at all. Same below
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Done in 39a06977. The primal check carries no comment now. The dual and reduced-cost ones keep a single line, because those two test the primal vector rather than the one being requested, which is the part that is not self-evident from the code — happy to drop those too if you would rather have none.
| EXPECT_EQ(cuOptGetDualSolution(solution, dual), CUOPT_SUCCESS); | ||
| EXPECT_EQ(cuOptGetReducedCosts(solution, reduced), CUOPT_SUCCESS); | ||
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| EXPECT_NE(primal[0], sentinel); |
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Shouldn't we be testing for approx equality with the correct answers rather than != sentinel? What is this test covering that existing tests for cuOptGetPrimalSolution don't?
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Nothing, and I have deleted it. I checked before answering: c_api_tests.cpp already asserts primal values with EXPECT_NEAR in several places (lines 204-205, 243-246 among others), so solution_accessors_return_values_when_present was covering a path that is already covered. I had added it as a symmetric guard against the fix breaking the working case, but that guard already exists.
What is left is the two tests that cover behaviour this PR actually changes: an infeasible solve reporting absence, and the zero-constraint case that the first version of the fix regressed.
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| EXPECT_EQ(cuOptGetPrimalSolution(solution, primal), CUOPT_SUCCESS); | ||
| EXPECT_EQ(cuOptGetReducedCosts(solution, reduced), CUOPT_SUCCESS); | ||
| EXPECT_NE(primal[0], sentinel); |
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Likewise test for equality with the correct answer here.
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Done. Minimizing x over 0 <= x <= 5 puts the optimum at the lower bound, so the test now asserts primal[0] == 0.0, reduced[0] == 1.0 (the objective coefficient) and an objective value of 0.0, all with EXPECT_NEAR, instead of merely checking that something was written.
dual[0] still uses the sentinel rather than a value, since the point there is that a zero-length dual result must leave the buffer untouched.
Per review: The comments explained how the check came to be rather than what it does. The primal check now carries none, and the dual and reduced-cost ones carry a single line, since testing the primal vector inside those two is the part that is not self-evident. solution_accessors_return_values_when_present is removed. Existing tests already solve LPs and assert primal values with EXPECT_NEAR, so it covered nothing that was not covered already. The zero-constraint test now asserts the optimum rather than that something was written: minimizing x over 0 <= x <= 5 puts the solution at the lower bound with the objective coefficient as its reduced cost, and a zero objective. Verified: 71/71 C_API_TEST cases pass. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
| static_cast<solution_and_stream_view_t*>(solution); | ||
| try { | ||
| const auto reduced_cost_host = solution_and_stream_view->get_solution()->get_reduced_costs(); | ||
| // As above: the solve is what is tested, not the length of this vector. |
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The reduced cost vector should be populated even when there are no constraints, so we should just check that.
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Done in b24a176b. cuOptGetReducedCosts tests reduced_cost_host.empty() again — you are right that reduced costs are per-variable and come back populated whether or not the problem has constraints, so that vector answers the question directly.
For the record on why the indirection was there: it came from a CodeRabbit finding that an empty vector does not always mean the values are missing, since a problem with no constraints has a legitimately empty dual solution. Reduced costs were caught up in the same change for symmetry, which as you point out was unnecessary.
| try { | ||
| const auto dual_host = solution_and_stream_view->get_solution()->get_dual_solution(); | ||
| // Empty here is valid for a problem with no constraints, so the solve is what is tested. | ||
| if (solution_and_stream_view->get_solution()->get_solution_host().empty()) { |
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Would it be a big deal to check empty() on the the dual solution vector here? What's the problem if we return CUOPT_INVALID_ARGUMENT when the problem is solved but there's no dual vector, because there are no constraints?
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No big deal, and it is back to dual_host.empty() in b24a176b.
Recording why it was not, since it will look like a step backwards otherwise. CodeRabbit flagged that an empty vector does not always mean the values are missing, and it was right about the mechanism: a box-constrained LP with no constraints solves to OPTIMAL, and the per-vector check reported CUOPT_INVALID_ARGUMENT for that valid result. I changed all three accessors to test whether the solve produced anything at all, which avoided it.
Your question is the one I should have asked first: what actually goes wrong for that caller? Nothing much. A problem with no constraints has no dual vector, so reporting its absence is a reasonable answer, and it costs a local, obvious check rather than one accessor reaching into another vector. Your review overrides the bot here.
So that the behaviour is pinned rather than quietly reintroduced later, the zero-constraint test now asserts it: primal and reduced costs return their values, and the dual reports the absence and leaves the buffer untouched. A future change cannot flip it without failing the test.
The defect this PR is actually about is unaffected — an infeasible solve still reports absence from all three rather than returning success having written nothing. 71/71 pass.
Reverts the indirection added earlier in this PR, where the dual and reduced-cost accessors tested the primal vector instead of their own. That indirection came from a CodeRabbit finding: an empty vector does not always mean the values are missing, since a problem with no constraints has a legitimately empty dual solution, and the per-vector check reported CUOPT_INVALID_ARGUMENT for that valid result. Testing whether the solve produced anything at all avoided it. Review takes a different view, and it is the one that holds. Reduced costs are per-variable and are populated whether or not the problem has constraints, so that vector answers the question directly. And a problem with no constraints genuinely has no dual vector, so reporting its absence is a reasonable answer rather than something to engineer around. So all three accessors test the vector they are about to copy. That is local and needs no comment, and the two explaining the indirection are gone. The zero-constraint case is not silently reintroduced: the test now asserts the behaviour, primal and reduced costs returning their values while the dual reports the absence and leaves the buffer untouched, so a later change cannot alter it without the test failing. The original defect is still fixed. An infeasible solve reports absence from all three accessors rather than returning success having written nothing. Verified: 71/71 C_API_TEST cases pass. Signed-off-by: Ramakrishna Prabhu <ramakrishnap@nvidia.com>
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Description
Fixes #1706.
cuOptGetPrimalSolution,cuOptGetDualSolutionandcuOptGetReducedCostscopy into a caller-allocated buffer and report no length. When the underlying vector is empty thememcpycopies nothing, the function returnsCUOPT_SUCCESS, and the caller's buffer keeps whatever it already held.So "this solve produced no values" is indistinguishable from "the values are all zero". A caller that zeroes its buffer first reads zeros and believes them.
Confirmed, not theoretical
Against an infeasible LP (
x >= 2andx <= 1), with each buffer pre-filled with a-12345sentinel:Note this affects
cuOptGetPrimalSolutionas well, which the original issue did not mention — it was found while reproducing.This is also what forced a revert in #1524: moving the Java bindings onto
cuOptGetReducedCoststurned "no reduced costs" into "all reduced costs are zero" for an infeasible LP, caught byProblemIntegrationTest.problemsBuildAndSolve[10]. Those getters had to stay on the internal C++ interface, which does report the real length.The change
Return
CUOPT_INVALID_ARGUMENTwhen there are no values to copy, and leave the output buffer untouched.CUOPT_INVALID_ARGUMENTrather than a new status code becausecuOptGetDualSolutionalready returns it when the underlying call throwsstd::logic_error. The empty-vector case was simply slipping past that guard, so the two paths now agree rather than one silently succeeding. Happy to use a distinct code instead if reviewers would prefer one — that would be a new public constant, which seemed like more surface than this warrants.cuOptGetPrimalSolutionhad no exception guard at all; it now has the same one as the other two.Compatibility
A solve that produced values is unaffected. The behaviour only changes where the function previously reported success without writing anything.
I checked the in-tree callers: the C examples under
docs/cuopt/source/cuopt-c/andskills/cuopt-numerical-optimization-api/assets/c/all branch on the status code, so they now report the failure instead of printing uninitialised memory. Python does not go through these entry points; it binds to the C++ structs via Cython.Tests
Two tests, covering both directions, since a fix that only checks the failing case could pass by breaking the working one:
solution_accessors_report_absent_values— infeasible solve; all three must report the absence and leave the sentinel intactsolution_accessors_return_values_when_present— solved LP; all three must still return valuesVerified locally: 71/71
C_API_TESTcases pass.Not included
The issue also notes that
cuOptGetProblemStringArrayAttributehas a related shape problem in the other direction — it requirescountto match exactly, so "no names set" is indistinguishable from "bad argument", with no size query to ask first. That one needs a small API addition rather than a behaviour fix, so I have left it out of this PR and it can be handled separately.Checklist