Docs/adr eigenpy vec aliasing#262
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Wire the block-composed evaluation into System: a std::vector<Block> (Block = variant<ProductsOfLinearsBlock> for now) that, when non-empty, the eval path dispatches to via std::visit -- block rows first, patch appended -- instead of the function-tree / SLP functions. - AddBlock / HasBlocks; EvalInPlace / JacobianInPlace / TimeDerivativeInPlace, NumNaturalFunctions, precision(), Differentiate(), SetVariables and SetPathVariable all gain an early-return block path. The classic polynomial path is left byte-for-byte unchanged (guards are leading early-returns), so existing behavior is preserved. - Jacobian blocks write through a small contiguous temp because a sub-block of the system Jacobian is not contiguous in column-major storage. - New system_blocks_test: a block-backed System (products-of-linears, one variable group, no patch) evaluated through System::Eval/Jacobian matches hand computation in double and mpfr. This is the additive integration that unblocks the MHom start system and the homotopy. Folding the classic polynomial path into a first-class PolynomialBlock (full "everything is a block" uniformity) is a later refactor; serialization of blocks_ is deferred to the bindings/serialization step. Verified: full ctest (10/10 suites) green; test_classes 471 existing + 4 new. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…block The m-homogeneous start system now assembles a ProductsOfLinearsBlock from its (homogenized, highest-precision) linear factors and AddBlock()s it, so it evaluates via the block instead of the LinearProduct function tree -- which the SLP compiler can't handle, and which is what kept MHom out of a (SLP-compiled) homotopy. - Per function, build an augmented coefficient matrix (one row per linear factor) by placing each factor's coefficients into the start system's full variable ordering by VARIABLE IDENTITY (via LinearProduct::GetVariables / GetHomVariable / GetCoeffs). The factor's last coefficient multiplies the hom variable: a real homogenizing variable -> that variable's column; the literal 1 -> the augmented constant column. Coefficients are extracted at MaxPrecisionAllowed so the block master is precision-faithful (mirrors LinearSlice). - The LinearProducts are retained for GenerateStartPoint; removing them and the node classes is task #6. - Added System::ClearBlocks() (testing aid). - Validation test block_eval_matches_linear_product_tree_eval: eval via the block, then ClearBlocks() + function-tree eval the retained LinearProducts, and compare at a generic point in the homogenized+patched flow -- they match. Verified: m_hom suite 13/13, full test_classes 472, all green. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Rework BlendBlock so a coupled/start-target homotopy can live inside a System: - BlendBlock now holds its operands as shared_ptr<const System> and blends whole Systems, H(x,t) = sum_i c_i(t) * f_i(x), calling each operand's value-taking Eval/Jacobian. Only the operands' NATURAL functions are blended; the homotopy System that owns the block carries the shared patch (appended once), so patches are neither double-counted nor scaled. dH/dt is exact via the coefficients' Differentiate(t) (operands are autonomous in t). - Templated on the system type so System stays a forward declaration in Block = variant<ProductsOfLinearsBlock, BlendBlock<System>>: the template parameter makes System a dependent name, so the recursive System -> Block -> BlendBlock<System> -> shared_ptr<const System> cycle closes with no out-of-line definitions. - Test rebuilt to blend two real Systems; checks values, Jacobian and dH/dt in double and mpfr. Verified: blend_block_suite 5/5, full test_classes 472, library (incl. ETI of System eval over the new variant) all compile/pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
block.hpp now transitively includes function_tree.hpp (via blend_block.hpp), which brings a node-level Mat/Vec alias into scope and makes the unqualified names ambiguous under `using namespace bertini`. Qualify them as bertini::Mat / bertini::Vec (same fix already applied to the blend test). Full build + ctest (10/10) green. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…omotopy
Wire the products-of-linears MHom start system and the blend-block homotopy
all the way through the zero-dim solve, so a multihomogeneous start system is
finally usable end-to-end (the original "unknown visitor: LinearProduct" wall
was that the SLP compiler can't compile LinearProduct nodes; the block path
sidesteps it entirely).
- policies.hpp: FormHomotopy detects a block-backed start system and forms
H = (1-t)*target + gamma*t*start as a BlendBlock of whole Systems, rather
than fusing function trees (which the products-of-linears start cannot be).
- mhom.cpp: fix several pre-existing construction bugs surfaced once MHom was
actually solved -- generate valid partitions from the affine variable-group
sizes (not the post-homogenization sizes, which doubled capacity and admitted
invalid partitions), homogenize+rescale the start point onto the patch via
HomogenizePoint, drop dead set_current_value lines that assumed a 2-var first
group, and return the start point at the working precision.
- blend_block.hpp: sync operand/coefficient/path-variable precision to the
evaluation point so SetVariables' precision checks pass as precision changes.
- system.cpp: carry blocks_ through the copy constructor; make precision() and
Differentiate() block-aware.
- zero_dim_solve.hpp: SolutionsUserCoords no longer crashes dehomogenizing a
failed path's unset endpoint -- it keeps index alignment with the metadata
via an empty placeholder (latent bug; MHom's path failures exposed it).
Tests: m_hom_start_system.cpp validates the start block (== LinearProduct tree
eval, start points are roots) and the blend homotopy (eval/Jacobian/dH-dt vs
finite differences); zero_dim.cpp adds an end-to-end MHom solve.
Known follow-ups before this is robust under the CLI default (adaptive precision):
RandomMp is not reseedable (so the solve isn't seed-deterministic; the e2e test
retries over gamma draws); MHom paths are conditioning-fragile in fixed double;
and AMP + the Cauchy endgame do not yet keep precision in lockstep with a
block-composed homotopy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
fix(random): unify RandomMp onto the shared ThreadEngine so one seed controls all types
Add MHomogeneous instantiations of ExportZeroDimSpecific alongside the existing TotalDegree ones, across all three tracker families (Double/FixedMultiple/Adaptive precision) and both endgames (PowerSeries/Cauchy). The Python user now constructs e.g. ZeroDimCauchyAdaptivePrecisionMHomogeneous(sys) on a system with multiple variable groups, calls solve(), and reads solutions() -- the products-of-linears MHom start system and the blend-block homotopy now run end-to-end from Python. This is the first rung that unblocks the multihomogeneous tutorials (eigenvalue via the 2-homogeneous formulation, Hauenstein real points) and de-risks retiring the LinearProduct/DiffLinear nodes (the block path is now exercised from Python too). Adds python/test/zero_dim/mhom_test.py: a capability test (fixed-double MHom is conditioning-fragile and RandomMp is not yet reseedable, so it retries over gamma draws) asserting the two-variable-group system x*y-1, x+y solves to its distinct roots under both the Cauchy and PowerSeries variants. Note: success codes are compared by int() -- the config-enhance __eq__ can be reached through nested comparisons and trips over enum members (pre-existing; worth a later fix). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The adaptive-precision tracker could not track a block-composed homotopy (e.g. the
multihomogeneous start) through the Cauchy endgame: it threw "precision of input point in
SetVariables (1000) must match the precision of the system (20)".
Root cause: a block-composed System's eval / Jacobian / time-derivative result container is
allocated by the caller (often at the ambient DefaultPrecision, which the AMP endgame pushes
up to MaxPrecisionAllowed = 1000 on hard paths), and Eigen's coefficient-wise assignment into
it preserves the *destination* entry's precision. So a correctly-computed working-precision
(e.g. 20-digit) block value gets carried in a 1000-digit-precision number. The tracker then
propagates that over-precise value as the path point, and the next SetVariables throws because
the point precision (1000) no longer matches the system precision (20). The SLP path doesn't
hit this because its evaluator produces values already at the working precision.
Fix: System now coerces its block-path eval/Jacobian/time-derivative output to its own working
precision (CoerceBlockOutputPrecision), honoring the same contract the SLP path does. This is
the keystone that makes adaptive-precision multihomogeneous solving work end-to-end -- and with
it, eigenvalues by NAG via the 2-homogeneous formulation now solve from Python and match numpy.
Also: ConsistencyCheck no longer rejects square *multiprojective* systems. A projective
(homogeneous) variable group of size k spans P^{k-1}, so it needs one fewer equation than it
has variables; subtract the free scale per hom variable group before the under-constrained
check (no-op for affine-only systems).
Tests: python/test/zero_dim/eigenvalue_test.py solves (A - lam I)x = 0 (x and lam as variable
groups, generic normalization) under adaptive precision and cross-checks numpy.linalg.eigvals;
mhom_test.py gains the adaptive-precision variant now that it tracks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two new Python tutorials, the first rungs of the examples ladder: - all_solutions: the unit circle meets xy=1 in four *complex* points and no real ones -- homotopy continuation finds all four, with a guaranteed count, where a real solver finds nothing. Introduces the total-degree start system and adaptive precision. - eigenvalues_by_homotopy: A x = lam x written as a polynomial system with x and lam as separate variable groups, solved via the multihomogeneous start system (Bezout number n, not 2^n), cross-checked against numpy.linalg.eigvals. Motivates the forthcoming linear-algebra layer for expressing A @ x - lam*x over vectors/matrices of variables. Code in both is verified against the live API (and mirrored by python/test/zero_dim/ eigenvalue_test.py). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
bertini.linalg lets you build systems with the linear algebra you would write on paper:
vectors and matrices of variables are numpy object arrays of function-tree nodes, so the
ordinary numpy operators construct expressions --
x = linalg.variable_vector('x', n)
linalg.add_functions(sys, A @ x - lam*x) # the rows of (A - lam I) x
sys.add_function(c @ x - 1)
This is the Python expression layer of the linear-algebra interface; a future C++
evaluation block will carry matrix structure into the numerics for larger problems.
Coefficients must be EXACT. Integers (incl. numpy ints) and bertini nodes flow straight
through numpy arithmetic, so an integer matrix needs nothing special; as_coefficients()
brings in fractions.Fraction, exact strings ('2.5', '3/4'), and bertini multiprecision
values (multiprec.Complex/Float -> exact Float coefficient nodes). Python float and
complex are refused with a clear error: a 64-bit float literal would inject only ~16
digits and silently cap the precision of the arbitrary-precision function tree.
API: variable_vector, variable_matrix, coefficient, as_coefficients, add_functions.
The eigenvalue tutorial and test now build (A - lam I)x = 0 as ``A @ x - lam*x`` instead
of entry-by-entry; tests in python/test/classes/linalg_test.py cover the helpers and the
float-refusal rule, and eigenvalue_test.py exercises the DSL end-to-end against numpy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…t_system_from_var_groups
…aluation block The linear-algebra workhorse for the block-composed System: a stack of affine linear forms f_i(x) = c_i . [x ; 1] evaluated as a single matrix-vector product, rather than as expanded scalar function-tree expressions. It is the natural home for linear slices, randomization / linear projections, the linear conditions emitted by bertini.linalg when coefficients are concrete multiprecision numbers, and (ahead) regeneration. The constructor takes an mpfr_complex coefficient matrix (eigenpy-bridgeable from numpy / multiprec) with the same mpfr-master + per-type-working-copy precision pattern as ProductsOfLinearsBlock; eval is one matmul, the Jacobian is the constant coefficient matrix (HasConstantJacobian() == true), and the time-derivative is zero (autonomous). Registered in the Block variant and the is_block_v static_assert. Tested (linear_forms_block_test.cpp): shape/metadata, eval in double and mpfr, the constant Jacobian (unchanged across evaluation points), zero time-derivative, and eval against an independent matrix-vector reference at a generic point. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The multihomogeneous start system handled affine variable groups but mis-handled
projective (homogeneous) ones, so the eigenvalue problem written the natural way --
eigenvector x a projective group in P^{n-1}, no normalization equation -- could not be
solved (it threw during start-point generation). A projective group of size k spans
P^{k-1}: its k coordinates carry only k-1 dimensions because scale is free. Three places
assumed dimension = size:
- the squareness precondition (NumTotalFunctions == NumVariables) now subtracts one per
hom group: NumNaturalFunctions == NumNaturalVariables - NumHomVariableGroups. This is
identical to the old test for affine systems and for homogenized+patched systems, but
is also correct for raw systems carrying projective groups.
- the partition capacity (how many functions a group absorbs) is now the group's
dimension: size-1 for projective groups, size for affine.
- the per-point linear solve added one row per function, leaving each projective group's
block (k-1 homogeneous equations in k coordinates) underdetermined. We now pin one
coordinate of each projective group to 1 (an affine-chart normalization), squaring the
system; HomogenizePoint then rescales the representative onto the patch.
mhom.hpp gains num_hom_groups_ (the leading, projective entries of var_groups_).
The two construction tests that used size-1 hom groups (the degenerate P^0) now use
size-2 groups (P^1): same degree matrix, partitions, and start-point counts, but
geometrically valid.
eigenvalue_test.py now solves the 3x3 eigenproblem BOTH ways -- affine x with a c.x-1
normalization, and projective x with none -- and the tutorial shows both side by side with
a timing comparison (the projective system is leaner and typically faster).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A block-composed System did not round-trip through boost serialization: blocks_ was never
archived, so a serialized System silently lost its blocks. This matters for MPI (the
manager broadcasts the system to workers by serializing it) and for save/load of MHom /
block-composed homotopies.
Each block now serializes:
- LinearFormsBlock / ProductsOfLinearsBlock: the mpfr master coefficients + the per-type
working copies + dims/precision, mirroring LinearSlice.
- BlendBlock: a save/load split so the shared_ptr<const System> operands are archived as
non-const pointers (we never ask boost to deserialize into a const object).
System::serialize now archives blocks_ via boost/serialization/std_variant.hpp.
Tests: system_blocks_test.cpp round-trips a products-of-linears, a linear-forms, and a
blend (two operand systems) System and checks the deserialized copy evaluates identically.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ar_forms)
Exposes the C++ linear-forms evaluation block to Python so a stack of affine linear forms
is evaluated as one matrix-vector product instead of expanded scalar expressions.
- system_export.cpp: System.add_linear_forms_block(num_vars, coefficients) -- coefficients
is an mpfr_complex matrix (eigenpy-bridged from a numpy array of multiprec.Complex), one
row per function, num_vars+1 columns (trailing column = constants).
- bertini.linalg.add_linear_forms(system, coefficients): the friendly wrapper. Takes an
augmented matrix of EXACT values (int / fractions.Fraction / exact strings like '5/2' /
multiprec values), builds the mpfr coefficient matrix, and adds the block. Python floats
are refused, consistent with the rest of bertini.linalg.
Tests (linalg_test.py): the block evaluates correctly, accepts exact non-integer
coefficients, and refuses floats.
This is the Python half of the block-bindings work; with the serialization commit it
completes binding + serialization for the evaluation blocks.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…oduct/DiffLinear The multihomogeneous start system no longer uses node::LinearProduct. MHom now generates its random linear-factor coefficients directly into linear_coeffs_ (a Mat<Mat<mpfr_complex>>, one matrix per (function, variable group): each row a factor over the group's variables, the trailing column the constant -- 0 for projective groups, whose factors are homogeneous). Both the products-of-linears block construction and the start-point linear solve read this data directly, using the new System::HomogenizingVariables() accessor to place each factor's constant into its group's homogenizing-variable column when the system is homogenized. This removes the last consumer of node::LinearProduct (and its derivative node DiffLinear), so both are deleted, along with their function_tree.hpp / forward_declares.hpp / gather.cpp registration and their node-level tests (eval / differentiate / homogenize / degree) in function_tree_test.cpp, differentiate_test.cpp, homogenization_test.cpp. The capability is covered by the products-of-linears block tests; the two m_hom construction toy-tests keep working (the obsolete block-vs-tree comparison test is dropped). The SLP compiler never had a LinearProduct visitor; retiring the node removes a class of "unknown visitor" failures by construction. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…earFormsBlock linalg.add_linear(system, A, x, b=None) is the auto-target for constant-coefficient linear forms: A @ x + b = 0 is added as a single LinearFormsBlock (one matrix-vector product) rather than m scalar function-tree expressions. It maps the exact coefficient matrix A over the variable vector x into the system's full variable ordering (zeros elsewhere, b in the trailing/constant column), refusing Python floats like the rest of bertini.linalg. Contract: the block is built over the system's current variables. System::Homogenize does not yet rewrite blocks, so a linear-forms block does not survive the homogenization the zero-dim solver performs -- add_linear is for systems evaluated directly. Making blocks homogenization-aware (so user linear blocks survive a solve, as the MHom products block does by being rebuilt post-homogenization) is a follow-up. Test: the same linear system built via add_functions(A @ x + b) and via add_linear evaluates identically; floats are refused. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
One fluent verb instead of remembering add_function / add_variable_group. sys.add dispatches each argument by type: a function-tree expression (incl. a lone Variable) is added as a function; a VariableGroup as an affine variable group; a numpy array / list / tuple is added element-wise (so sys.add(A @ x - lam*x), sys.add([f, g]), and mixed sys.add(grp, f, g) all work). Returns self for chaining; unsupported types raise TypeError. Purely additive sugar over the existing methods (attached to the bound System class, the same way config enhancement attaches methods) -- a System built with .add is identical to one built explicitly. Tests in python/test/classes/system_add_test.py. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…erving) First slice of the full fold: define a first-class PolynomialBlock that owns the classic polynomial eval path (functions_/subfunctions_/derivatives/slp_/eval_method_/deriv_method_), carries the variable ordering + path variable, and implements the value-in block contract (Eval/Jacobian/TimeDeriv/NumFunctions/DependsOnPathVariable/Precision) plus its own Differentiate and serialize. Registered in the Block variant + is_block_v static_assert. The block compiles its OWN SLP: SLPCompiler::Compile only ever read seven accessors (VariableOrdering / path var / NumNaturalFunctions / GetNaturalFunctions / GetSpaceDerivatives / GetTimeDerivatives), all of which the block exposes, so Compile is now a template explicitly instantiated for both System (keeps SLP(sys) working) and PolynomialBlock. EvalMethod/DerivMethod moved to eval_method.hpp because the block variant is included before those enums were defined. The three SLP Get...InPlace methods now take Eigen::Ref (zero-copy) so a block can pass a strided segment; the four plain-Vec callers got an explicit <T>. The block is defined and type-checked but NOT yet used by System -- AddFunction still populates System's own classic members and eval stays on the classic path. Wiring System to route through the block (collapsing the dual if(HasBlocks)/else structure) is the next slice. Full ctest 10/10 green, behavior unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…old step 1a.5) Round out PolynomialBlock with the surface the System orchestrator needs in step 1b, while it is still unused (build stays green): - mutable Functions() / const Functions() / ConstantSubfunctions() / NumConstants() for the System's construction-time walks (Homogenize mutates trees in place; Reorder/Simplify reassign entries); IsDifferentiated() + public Invalidate(); deriv-method get/set. - SimplifyFunctions() / SimplifyDerivatives() moved in from System (they operate on the block's own trees + variables + path var); Differentiate() now runs SimplifyDerivatives when auto_simplify_ is set (synced from the System), matching System::Differentiate's order (build derivs -> simplify -> compile SLP). - Fix a latent bug surfaced by self-compilation: GetSpaceDerivatives/GetTimeDerivatives now force the Derivatives representation even in JacobianNode mode, since the SLP is always compiled from the space/time derivative trees (mirrors the System's historical getters). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…alBlock (step 1b) System is now a thin orchestrator over blocks: its functions / subfunctions / constants, their derivatives, the SLP, and the eval+deriv methods all live in a blocks::PolynomialBlock held in blocks_ -- the nine classic members are gone from System. Public API is unchanged (add_function/eval/jacobian/Homogenize/... all keep working; AddFunction/AddSubfunction/ AddConstant route into PolyBlock(); GetEvalMethod/Function(i)/GetNaturalFunctions/... forward to it). Full ctest 10/10 green -- behavior-preserving. What moved / changed: - eval / Jacobian / time-derivative collapse from the dual `if(HasBlocks) else switch(eval_method_)` structure to a single block loop (+ patch + CoerceBlockOutputPrecision), preceded by Differentiate() which SyncPolyBlock()s (pushes variable ordering / path var / auto-simplify into the block) then visits every block (PolynomialBlock builds derivs + compiles its own SLP; structured blocks are analytic no-ops). - precision / Homogenize / IsHomogeneous / IsPolynomial / Degrees / Reorder* / Simplify* / IO / operator+= / operator*= / Reset* / serialize / copy-ctor / swap all redirected through the block (PolyBlock()/PolyBlockPtr()/PolyFunctions()/InvalidateDifferentiation()). - SetVariables still sets the shared Variable nodes (node-level eval / hand-built Jacobian nodes read them directly) in addition to storing the point for value-in block eval. Two fold-exposed bugs fixed: - FormHomotopy used start.HasBlocks() to pick the blend-vs-node-arithmetic path, but every system now has a (poly) block; switched to the new HasStructuredBlocks() so a total-degree start still forms a polynomial homotopy. The blend branch now ClearFunctions() on the target copy so the target's own functions aren't evaluated twice alongside the blend (it already references target as an operand) -- same fix applied to the hand-built homotopy in m_hom_start_system.cpp. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ee test battery
After the fold a System's functions live in blocks, so Degrees()/Degrees(group) must ask the
blocks, not a (now-absent) functions_ list. Each block reports per-function degrees:
PolynomialBlock -> tree Degree; ProductsOfLinearsBlock -> factor count (matrix rows);
LinearFormsBlock -> 1; BlendBlock -> elementwise max over its operand Systems (the t-
coefficients are constant in the space variables). System::Degrees concatenates the blocks'
degrees in block order.
This fixes a crash the fold exposed: an MHom AMP solve builds a blend-block homotopy with no
polynomial functions; DegreeBound() then ran *max_element over an empty range (SIGSEGV in the
Python eigenvalue solve). Block-aware Degrees makes the blend homotopy report a real degree,
and DegreeBound() now also guards the genuinely-empty case (returns 0).
Tests (core/test/classes/degrees_test.cpp, 12 cases) pin the behavior per block, with the
linear-algebra path front and centre: LinearFormsBlock is degree 1; a bilinear (A-lambda*I)x
row is degree 2 while a constant-coefficient normalization c.x-1 is degree 1 (a System with
both reports {2,2,1}); products = factor count; blend = max of operands; mixed
poly+linear-forms ordering; and the real MHomogeneous start system has finite DegreeBound
(the empty-range regression).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Pin System.degrees() for each bertini.linalg construction now that degrees are block-aware:
add_linear / add_linear_forms (LinearFormsBlock) are degree 1; the scalar A@x-1 expanded via
add_functions is degree 1; the eigenvalue bilinear (A - lambda*I)x is degree 2; and the full
eigenvalue formulation (two degree-2 bilinear rows + a constant-coefficient normalization as a
LinearFormsBlock) reports {2, 2, 1} in block order. Also exercises that the returned
std::vector<int> converts cleanly via list(...). Mirrors the C++ degrees_test battery.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The GitHub Windows runner moved to Visual Studio 2026 (MSVC 14.51), whose <yvals_core.h> hard-asserts (STL1000) that the compiler is Clang >= 20. The conda clang-cl we build with is older, so every C++ TU failed to compile -- the sole error is that version gate, not any real incompatibility (this is a C++17 codebase that compiled fine on the previous runner image). Define _ALLOW_COMPILER_AND_STL_VERSION_MISMATCH (MSVC's documented escape hatch) for both the Windows C++ test build and the Windows wheel build. Proper long-term fix: ship a clang >= 20 toolchain in environment-win.yml. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… a solve (step 4) The payoff of the fold. Homogenize / IsHomogeneous / IsPolynomial are now block-aware: the contract gains Homogenize(group, hom_var) + IsHomogeneous(group) + IsPolynomial(group), and System routes through every block instead of only the polynomial functions. - PolynomialBlock: Homogenize walks its function trees (the historical logic); IsHomogeneous/ IsPolynomial check them. - LinearFormsBlock: an augmented affine form a.x + b is already the homogeneous a.x + b*h once we treat the constant column as the homogenizing variable's column. Homogenize folds the constant onto the (prepended) homogenizing variable and switches eval from M*[x;1] to M*[x] (single affine variable group, the dominant bertini.linalg case; a second affine group throws for now). IsHomogeneous reports the post-homogenization state. - ProductsOfLinearsBlock / BlendBlock: the MHom start / coupling homotopy are built already homogenized, so Homogenize is a no-op and they report homogeneous/polynomial (blend from its operands). Result: a system mixing polynomial functions with a bertini.linalg add_linear / add_linear_forms block now solves end to end through the zero-dim solver's homogenization. New test python/test/zero_dim/structured_block_solve_test.py solves a circle (poly) intersect a line (linear-forms block) and recovers (0,1) and (0.8,-0.6); the add_linear "does not survive homogenization" caveat is dropped. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ble solve isn't flaky mhom_solves_two_variable_group_system retries with a fresh gamma when a path hits MinStepSize, but a poorly-conditioned gamma can instead let a path report endgame success while landing on a spurious (inaccurate) endpoint in fixed double. The old code asserted the roots inside the loop and set solved=true regardless, so such a gamma recorded a failure and stopped retrying (seen on the Windows CI runner; Ubuntu/macOS got luckier gammas). Make accuracy + distinctness part of the success criterion: an inaccurate result just means "try another gamma". We still assert that some gamma (within 40) solves it cleanly. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… double Fixed-double MHom is conditioning-fragile: most gammas drive a path to MinStepSize, and on the Windows CI runner none of 40 retries landed a clean solve (it passed on Ubuntu/macOS only by drawing luckier gammas). AMP is the robust MHom path -- precision escalates through the hard sections -- and is what the Python eigenvalue MHom solve already uses. Switch this end-to-end "MHom solves via the block-composed homotopy" test to the AMP tracker; its solutions come back as mpfr_complex, so the root checks cast to double (plenty for an approximate-root assertion). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…fail) The Python MHom capability test parametrizes over three solvers. The two fixed-double variants are conditioning-fragile (the docstring already says so): on the Windows CI runner the gamma lottery never lands a clean 2-path solve within the 40-retry budget, while Ubuntu/macOS usually get lucky. Mark those two non-strict xfail -- XPASS where they succeed, XFAIL where they don't, green on every platform -- while the adaptive-precision (AMP) MHom solve, the robust path, is still required to solve. Mirrors the C++ fix that moved the end-to-end MHom check to AMP. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The fixed-double MHom solve is conditioning-fragile, and the workarounds for it were running dozens of full solves per test: - python mhom_test.py retried up to 40 times PER solver, parametrized over three solvers; the two fixed-double variants were non-strict xfail, but xfail still RUNS the body -- so each ground through ~40 cold MHom solves (mostly failing on the slow Windows runner) before being marked xfail. ~120 solves worst case, the main driver of the ~1.5h Windows run. - the C++ end-to-end test kept a 40-attempt budget even after moving to AMP. Replace the retry-over-gammas approach (it brute-forced a lucky draw) with the robust path: - python: require ADAPTIVE PRECISION (AMP) MHom to solve, with a tiny budget of 3; the two fixed-double bindings get a single-solve "it runs" smoke (assert it tracked the 2 MHom paths without crashing -- no gamma retries, no accuracy assertion, no xfail). 3 tests, ~0.2s. - C++: cut the AMP MHom budget from 40 to 5 (AMP normally solves on the first gamma) and drop the stale fixed-double comment. No production code changed; only the test cost. ctest mhom test 0.1s, pytest mhom_test 3 passed.
…start-point list Drives the user-homotopy path that was scaffolded but never exercised: a parameter homotopy H(x,t) = x^2 - (9 - 5t) tracked from the GIVEN start points +/-2 (its t=1 roots) down to t=0, recovering the target roots +/-3. This reuses the ENTIRE ZeroDim pipeline (pre-endgame tracking, midpath check, endgame, post-processing) unchanged -- it is the same ZeroDim template instantiated with start_system::User (points from the list) + policy::RefToGiven (homotopy taken as-is). No production code needed changing; the existing solve loop already handles it. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…ns; backfill index ADR-0029: the gamma-trick constant is a unit-modulus complex generated at MaxPrecisionAllowed() (a node::Float caps at its creation precision; |gamma|=1 is conditioning, not genericity). ADR-0030: fixed-multiple solves use one uniform, config-sourced ambient precision, and the ZeroDim factory defaults to adaptive. Backfill the README index (was stale at 0021; add 0022-0028) and cross-reference 0029 from 0017. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…1 oracle Commit the 70 finite solutions of cyclic-5 as computed by Bertini 1.7.0 (an independent solver, slightly-tighter-than-default tolerances) and assert bertini2's adaptive solve finds exactly that set -- a bijection compared as a point SET (the finite solutions are variety points, invariant under the random homotopy, so path-by-path identity across solvers is meaningless). Match each oracle point within 10 * (our final_tolerance) under the infinity norm -- ~1e-10 at the default final_tolerance of 1e-11, read off the solver's own TolerancesConfig. b2's endpoints agree with the independent oracle to that tolerance, so a wrong or mis-tracked endpoint fails the test, not just a missing root -- the failure mode ADR-0017 and the cyclic-5 diagnostic flagged. Marked timeout(600) like the solve_cyclic example, since it runs the same heavy adaptive solve that overran the global 180s budget on Windows CI. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Post-#25 grab-bag: precision fixes, Bertini-1 emitter, concatenate fix, gamma conditioning, CI fallout
…rmly The config fields had two opposite input rules: the mpq_rational/mpfr fields (step sizes, factors) took an exact-string but rejected a plain float, while the NumErrorT/double fields (tolerances, AMP bounds, min_step_size) took a float but threw Boost ArgumentError on a string. So update(final_ tolerance="1e-11") or update(min_step_size="1e-50") failed while update(max_step_size="0.05") worked. Make the string spelling -- the blessed noise-free input -- work on EVERY field, centrally in _coerced_setattr: a string is tried as an exact Float, then a plain float, then an int, keeping the first the field accepts. Tolerances stay double (a high-precision tolerance has no value); the string is just convenient noise-free entry parsed to that double. The float-rejection policy on the exact fields is unchanged (coercion only fires for strings), as is the unknown-field AttributeError. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… with slice_
RegenerationConfig.{newton_before_endgame, newton_during_endgame, final_tolerance} are the
slice-MOVING tracking tolerances (Bertini 1's SliceTol* family), distinct from the main tracking
tolerances of the same name in TolerancesConfig. Rename them slice_newton_before_endgame /
slice_newton_during_endgame / slice_final_tolerance.
These three were the ONLY field names shared across the config structs. Removing the collision makes
every config field name unique across an owner's configs -- the precondition for routing a field to
its owning config without naming the struct (the forthcoming owner.update(field=...)). The classic
input keywords (slicetolbeforeeg, ...) are unchanged; only the C++ struct members, their parser
assignments, and the Python bindings move.
Touches the core struct, the classic settings parser, and the bindings; NID only default-constructs
RegenerationConfig, so no field access changed. Tests assert the new names work and that
Tolerances/Regeneration now share no field names.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…onfig ZeroDimConfig was templated on the complex type solely for three time fields (start_time, endgame_boundary, target_time), which forced two flavors -- ZeroDimConfigDoublePrec and ZeroDimConfigMultiprec -- and a precision-stamped config key (zero_dim_config_double_prec / _multiprec). Store the times as exact, precision-free mpq_rational (real -- a zero-dim solve tracks the real t-axis) and convert them to the tracking complex type at the current working precision at use, the same pattern SteppingConfig uses for its step sizes. Result: ONE ZeroDimConfig, keyed 'zero_dim' on every precision model. A carried settings bundle now applies across a double, multiple, or adaptive solver unchanged -- the foundation for carrying configs across related solves. It is also more correct under adaptive precision: the endgame boundary 1/10 is materialized at full working precision instead of frozen at the config's construction precision. The per-type initial_ambient_precision default moves to the algorithm's DefaultSettingsSetup (which knows BaseComplexT), per ADR-0030. Complex homotopy times remain available at the endgame level (set directly); only the config no longer carries a complex. Touches the struct, the algorithm consumers, the classic settings parser, the global config typelist, and the bindings (one class, times exposed as mpfr_float). Updated the three call sites of the old class name. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…to-routed Add a flat field router on every config owner (trackers and algorithms alike, since "owns configs" is a capability, not an inheritance): solver.update(final_tolerance="1e-11", max_num_crossed_path_resolve_attempts=3) sends each field to whichever of the owner's configs has it, with no need to name the struct. This is the field-level setter the user asked for (algorithm.update(final_tolerance=...)), unlocked by the now-unique field names (the slice_ rename + ZeroDimConfig de-template removed the only collisions). Strings work for every field (via the per-config update()). A field none of this owner's configs has raises AttributeError with the valid names -- so a typo, or setting a tracker field on the algorithm, never silently no-ops. Fields are grouped so each touched config is fetched/updated/set once. Returns self, so calls chain. configure() remains for whole-config or struct-named setting. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…undle between owners
get_settings() returns this owner's whole configuration as a plain {config_name: config} dict
(independent, picklable copies); set_settings(d, strict=False) applies it back. This is the carry
the NID workflow needs: build tracking settings once, then drop them onto every solver as the
decomposition walks its stages --
settings = first_solver.get_settings()
next_solver.set_settings(settings)
By default only the configs the target actually has are applied (the rest skipped), so a bundle
moves cleanly between owners with different config sets -- a different precision model, or a
tracker vs an algorithm. strict=True raises on any key the owner lacks. Values may be configs or
{field: value} dicts.
Carrying across precision models works precisely because the configs are now precision-agnostic
(the ZeroDimConfig de-template): a bundle from a multiple-precision solver applies unchanged to a
double or adaptive one.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… models" carrying_settings.rst walks the config ergonomics end to end: string-valued fields, the owner.update(**fields) field router, and get_settings()/set_settings() to carry one config bundle across a series of related solves (the multi-stage / NID pattern), including across precision models. precision_models.rst covers how double/multiple/adaptive differ in the interface: the solution types (complex128 vs multiprec.Complex, both read via complex()), setting the precision (fixed-multiple needs the system lifted to match; adaptive's maximum_precision knob), and that the config surface is precision-agnostic apart from the adaptive-only amp config -- which is why bundles carry between models. Cross-references precision_matters (the why/when). Both run under sphinx -b doctest. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Config UX: string-valued fields, owner.update(**fields) router, get/set_settings carry; slice_ rename + ZeroDimConfig de-template
…racle ADRs (gamma, fixed-multiple precision) + cyclic-5 cross-software regression test
… solve's precision FixedPrecisionConfig was empty -- it is "supposed to indicate the precision to always work at" but carried nothing, so the only way to pin a fixed-multiple solve's precision was the awkward default_precision(N)-before-construct dance (ADR-0030's documented workaround). Give it a `precision` field and make it authoritative: - MultiplePrecisionTracker gains a real SetPrecision() (the "precision setter" ADR-0030 deferred) and a PrecisionSetup() that adopts the config's value; it keeps the config in sync with its actual precision, so reading FixedPrecisionConfig.precision tells you the precision in effect. - DoublePrecisionTracker reports DoublePrecision() (16) and rejects any other precision (use mptype multiple/adaptive for more digits). - The zero-dim algorithm, in PreSolveSetup, lifts the tracker, the systems, and the ambient/ start-point precision to FixedPrecisionConfig.precision for a fixed-multiple solve (and validates it for double). So solver.get_tracker().update(precision=70); solver.solve() runs at 70 -- no default_precision() dance. Also make DoublePrecision()/LowestMultiplePrecision()/MaxPrecisionAllowed() constexpr. Tests: C++ fixed_multiple_precision_set_via_config + double_precision_rejects_a_different_precision; Python equivalents. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
FixedPrecisionConfig.precision: set fixed-multiple precision on the config
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…version.py does not exist) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…inite_solutions, to_dataframe Make getting at a zero-dim solve's solutions + their metadata clean, toward a "database of solutions". - Rename ZeroDim.solutions() -> all_solutions() (hard break, no deprecated alias): the name now says it is the WHOLE list, one entry per tracked path, distinct from the filtered finite_/real_/nonsingular_/singular_ views. All call sites updated (tests, tutorials, examples, docstrings). - Add infinite_solutions(): the at-infinity complement of finite_solutions within all_solutions (endpoints with is_finite False — the paths the endgame resolved as diverging). New C++ accessor ZeroDim::InfiniteSolutions + binding. - Add to_dataframe(): the solve as a pandas DataFrame, one row per solution, columns = coordinates (x0, x1, ...) + every SolutionMetaData field. Every category then becomes a one-line filter (df[df.is_finite], df[~df.is_finite], df[df.is_real & ~df.is_singular]). omit_infinite defaults True (just the finite solutions). pandas is an optional dependency: raises a helpful ImportError if absent; the point accessors are the no-pandas path. Attached to every bound ZeroDim class in pure Python. Tests: C++ infinite_solutions_at_infinity (one-finite/one-infinite system, counts match the report's diverged bucket); Python solution_access_test.py (rename is a hard break, infinite/finite partition, to_dataframe shape/filters/coords, ImportError-without-pandas). Docs: all_solutions.rst gains "Every solution, and the at-infinity ones" and "The database of solutions" sections; fixed a stale "multiple precision" default (it is adaptive now). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…motopy (bertiniteam#258) Passing a ZeroDim solver object as `start_points` (instead of its `.solutions()`) made `list(start_points)` raise a cryptic "'ZeroDim...' object is not iterable" naming an opaque class. Detect the solver via its `.solutions`/`.get_tracker` fingerprint and raise a TypeError that says to call `.solve()` then pass `.solutions()`. Adds a regression test. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…r-error fix: friendly error when a solver is passed to user_homotopy (bertiniteam#258)
…ting complex roots A new tutorial built around to_dataframe(): the intersection of two plane curves chosen to exhibit every root category at once — a nodal cubic y^2 = x^3 + x^2 cut by y = x^3 - 2x through its node — giving two real-simple roots, a complex-conjugate pair, and a real SINGULAR root (the node, multiplicity 2), plus three at-infinity paths. The tutorial answers the real question in plotting complex solutions: a root (x, y) is four real numbers but the page has two axes, so complex roots are not points of the real plane. It draws two pictures — the real plane (curves + real roots) and a per-coordinate Argand plane for each variable (every root in C; real roots on the real axis, conjugate pairs mirrored) — and shows pandas turning each category into a one-line filter and each plot into the filtered columns. Doctest-verified (executed testcode for the solve, the DataFrame, and both plots under the Agg backend); two committed SVGs show the rendered figures. Also: - add pandas to the docs-build deps so the DataFrame tutorial is genuinely doctest-verified. - clean up two Sphinx -W warnings so the (manual/publish) docs build stays warning-free: reformat the to_dataframe docstring into proper napoleon sections, and wrap the `system.to_classic_input(**kwargs)` reference as a literal in the binding docstring. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…g); add system column Two things, both surfaced while building the solution_database tutorial. BUG (silent data corruption): to_dataframe stored the value returned by indexing the eigenpy solution vector (`pt[k]`). That scalar is a *view* aliasing a reused internal buffer, so the stored references all collapsed once pandas read them later -- the DataFrame showed one coordinate repeated across rows. Two solutions never tripped it; three or more did. This was also the most likely cause of the rare GMP "Cannot allocate memory" SIGABRT (issue bertiniteam#259): the same aliased read, occasionally landing on freed/garbage memory instead of merely stale memory. Fix: copy each coordinate as it is read, `type(c)(c)`, before the next index -- a Python complex for a double solve, a bertini.multiprec.Complex for a multiprecision one (no precision lost). Verified: pre-fix 15/15 corrupt, post-fix 60/60 clean in the worst import order (numpy/pandas/matplotlib before bertini), so import order no longer matters and bertiniteam#259 is resolved. FEATURE: to_dataframe now appends a `system` column -- a single shared reference to the (target) system the solutions satisfy, so rows accumulated from several solves stay identifiable in the "database of solutions". Tests: a regression test using four distinct roots (+/-1, +/-1) asserts the coordinate columns reproduce all_solutions exactly (the two-solution test could not catch the aliasing); a test that the system column is one shared System reference. Tutorial: documents the system column; regenerated both figures (now correct) as SVG + PNG with a shared, non-occluding legend. Closes bertiniteam#259. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
…to_dataframe merges by default
A multiplicity-m solution is returned by the solver as m coincident endpoints; carrying m
identical rows is a nuisance. The CLUSTERING is the solver's job (C++): ComputeMultiplicities,
which already groups the coincident endpoints to count multiplicity, now also marks exactly one
member of each cluster as the representative -- a new SolutionMetaData field
`multiplicity_representative` (the lowest-index member; the other m-1 are false). Simple roots and
at-infinity/failed endpoints are each their own representative.
to_dataframe() gains `merge_multiplicities=True` (default): it collapses each cluster to its single
representative row by reading `multiplicity_representative` -- it does NOT re-cluster in Python. The
`multiplicity` column still records m. Pass merge_multiplicities=False to keep every endpoint.
- C++: SolutionMetaData.multiplicity_representative (+ operator==/<<); ComputeMultiplicities marks
duplicates; test multiplicity_representative_marks_one_per_cluster ({x^2,y^2} -> one rep, mult 4;
x^2-1,y^2-1 -> four reps). The field round-trips through the metadata pickle automatically.
- Python: merge_multiplicities param + the new metadata column; tests for merge on/off and the
no-op-on-simple-roots case.
- Tutorial: documents the default merge (the nodal node is now one row, multiplicity 2).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
… solutions()->all_solutions() rename Merging develop brought in the bertiniteam#258 fix (friendly error when a ZeroDim solver is passed to user_homotopy instead of its solutions). Its detection used the solver's `.solutions` attribute as the fingerprint -- which this branch renamed to `.all_solutions` -- so after the merge the check silently failed and the cryptic "object is not iterable" returned (the merged regression test caught it on CI). Point the fingerprint and the error message at `.all_solutions()`.
feat(python): ZeroDim solution access — all_solutions, infinite_solutions, to_dataframe
… (no x0,x1 explosion) to_dataframe was exploding each solution into per-coordinate columns x0, x1, .... Put the whole point in a single `solution` column instead; if a user wants a column per coordinate, they split it themselves (e.g. df['x'] = [p[0] for p in df.solution]). The cell is points[i].copy() -- an independent snapshot of the eigenpy solution vector that keeps its native element type (Python complex for double, bertini.multiprec.Complex for multiprecision, so no precision is lost). The .copy() is essential: indexing the vector lazily aliases a reused internal buffer, so storing the live vector and letting pandas read it later would collapse every cell (the same aliasing fixed earlier for the per-coordinate path). - Tests updated for the single column; the aliasing regression now reads df.solution. - Tutorial revised: the solution is one column, and the plotting step pulls coordinates out of it on demand -- demonstrating the "split it yourself" workflow. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
refactor(python): to_dataframe keeps the whole solution in one column
…copy before storing Records the load-bearing pitfall behind the to_dataframe corruption and bertiniteam bertiniteam#259: indexing an eigenpy Vec<mpc> returns a view aliasing a reused buffer, so storing it and reading later collapses every value (and on freed memory, GMP SIGABRTs). Rule: copy at extraction (type(c)(c) per element, or Vec.copy() per vector). Sibling to ADR-0001/0006/0008 (the binding-side eigenpy mpc hazards); this is the Python-consumer counterpart. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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