mhtirogla
Home
Systems
API
Pricing
About
Log in
← Back
Home
›
Systems
›
Numerical and mathematical computing
constraint-solver
constraint programming solver
OR-Tools CP-SAT, Gecode
System
Techniques · 12
Primitives · 23
constraint-solver
Work-stealing deque
concurrency · medium: Per-worker deque: the owner pushes and pops at the bottom without contention, idle workers steal from the top; the core of Rayon, Tokio and Go.
›
Bipartite matching
graphs · medium: Maximum/weighted bipartite matching (Hopcroft-Karp, Hungarian) for assignment problems
CDCL SAT solving
optimization · medium: Search for a satisfying assignment, learn a new constraint from every dead end, and jump back past irrelevant choices; handles industrial formulas with millions of clauses.
›
Constraint propagation (constraint…
optimization · medium: Variables with finite domains, propagators that prune impossible values to a fixpoint, and backtracking search with trail undo between decisions.
›
Cutting planes
optimization · medium: Valid inequalities added to an LP relaxation to cut off its fractional optimum without removing any integer point; the engine of branch-and-cut MIP solvers.
›
Guided local search
optimization · medium: Penalize frequent solution features to escape local optima in routing and scheduling
Job-shop scheduling
optimization · medium: Model jobs as chains of operations, resource order as a choice of arc directions, and minimise the longest path; build with dispatching rules, improve by swapping critical neighbours or solve with CP.
›
Max flow (Dinic)
graphs · low: Maximum s-t flow by BFS level graphs and blocking flows; also gives the min cut, bipartite matching and assignment with capacities.
›
Drat proof certificates
optimization · low: DRAT and LRAT proofs emitted by SAT solvers and checked independently
Two watched literals
optimization · low: Unit propagation for SAT that watches just two literals per clause, so backtracking costs nothing and long clauses are rarely touched.
›
Simplex method
optimization · low: Solves linear programs by walking along the edges of the feasible polytope from vertex to vertex, one basis swap (pivot) at a time; the workhorse behind MIP solvers.
›
Strongly connected components
graphs · low: Tarjan/Kosaraju SCCs for cycle detection in dependency graphs
Binary decision diagrams
optimization · low: A reduced ordered BDD stores a Boolean function as a shared DAG that is unique per variable order, so equivalence is one integer compare and counting is one pass.
›
Tseitin transformation
optimization · low: Linear-size CNF encoding of formulas with auxiliary variables
Epoch-based reclamation
concurrency · low: Threads announce a global epoch while touching shared lock-free data; garbage retired in epoch e is freed once the epoch reaches e + 2.
›
Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
›
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
›
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
Arena allocator
systems · low: Allocate by bumping a pointer through big chunks and free everything at once: a few instructions per allocation for objects sharing a lifetime.
›
Interval arithmetic
numerics · low: Rigorous bounds with outward rounding; root isolation and robust geometric tests
Journaled state revert
storage · low: Undo journal of state writes enabling nested checkpoints and cheap revert
Floating-point error
numerics · low: How IEEE 754 rounding error accumulates and how to contain it: compensated (Kahan/Neumaier) and pairwise summation, error-free transformations with FMA, ULP comparisons.
›
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
›
+ 5 more techniques
+ 7 more primitives
System
Techniques · 12
Primitives · 23
constraint-solver
Work-stealing deque
concurrency · medium: Per-worker deque: the owner pushes and pops at the bottom without contention, idle workers steal from the top; the core of Rayon, Tokio and Go.
›
Bipartite matching
graphs · medium: Maximum/weighted bipartite matching (Hopcroft-Karp, Hungarian) for assignment problems
CDCL SAT solving
optimization · medium: Search for a satisfying assignment, learn a new constraint from every dead end, and jump back past irrelevant choices; handles industrial formulas with millions of clauses.
›
Constraint propagation (constraint…
optimization · medium: Variables with finite domains, propagators that prune impossible values to a fixpoint, and backtracking search with trail undo between decisions.
›
Cutting planes
optimization · medium: Valid inequalities added to an LP relaxation to cut off its fractional optimum without removing any integer point; the engine of branch-and-cut MIP solvers.
›
Guided local search
optimization · medium: Penalize frequent solution features to escape local optima in routing and scheduling
Job-shop scheduling
optimization · medium: Model jobs as chains of operations, resource order as a choice of arc directions, and minimise the longest path; build with dispatching rules, improve by swapping critical neighbours or solve with CP.
›
Large neighbourhood search (ruin an…
optimization · medium: Repeatedly remove a chunk of a solution and rebuild it with a heuristic or exact solver; learn which operator pairs pay off.
›
Presolve reductions
optimization · medium: Cheap, provably safe reductions run before the solver (fix variables, drop rows, tighten bounds and coefficients) so LP and MIP solves start from a smaller, tighter model.
›
Critical path analysis
graphs · medium: Longest dependency chain in a DAG of timed spans or build actions
Local search 2opt or opt
optimization · medium: 2-opt, Or-opt, relocate and swap neighborhoods with fast delta evaluation for routes
Simulated annealing
optimization · medium: Stochastic local search with a cooling schedule for combinatorial optimization
Max flow (Dinic)
graphs · low: Maximum s-t flow by BFS level graphs and blocking flows; also gives the min cut, bipartite matching and assignment with capacities.
›
Drat proof certificates
optimization · low: DRAT and LRAT proofs emitted by SAT solvers and checked independently
Two watched literals
optimization · low: Unit propagation for SAT that watches just two literals per clause, so backtracking costs nothing and long clauses are rarely touched.
›
Simplex method
optimization · low: Solves linear programs by walking along the edges of the feasible polytope from vertex to vertex, one basis swap (pivot) at a time; the workhorse behind MIP solvers.
›
Strongly connected components
graphs · low: Tarjan/Kosaraju SCCs for cycle detection in dependency graphs
Binary decision diagrams
optimization · low: A reduced ordered BDD stores a Boolean function as a shared DAG that is unique per variable order, so equivalence is one integer compare and counting is one pass.
›
Tseitin transformation
optimization · low: Linear-size CNF encoding of formulas with auxiliary variables
Epoch-based reclamation
concurrency · low: Threads announce a global epoch while touching shared lock-free data; garbage retired in epoch e is freed once the epoch reaches e + 2.
›
Memory ordering
concurrency · low: What Relaxed, Acquire, Release and SeqCst atomics actually guarantee, what they cost on x86 and ARM, and which one each pattern needs.
›
BFS and DFS
graphs · low: The two basic graph traversals; BFS gives hop distances and levels, DFS gives discovery/finish order for cycles, components and topological order.
›
d-ary heap
data-structures · low: Array heap with d children per node (d = 4 typical): half the binary heap's depth, siblings in one cache line, faster priority queues.
›
Arena allocator
systems · low: Allocate by bumping a pointer through big chunks and free everything at once: a few instructions per allocation for objects sharing a lifetime.
›
Interval arithmetic
numerics · low: Rigorous bounds with outward rounding; root isolation and robust geometric tests
Journaled state revert
storage · low: Undo journal of state writes enabling nested checkpoints and cheap revert
Floating-point error
numerics · low: How IEEE 754 rounding error accumulates and how to contain it: compensated (Kahan/Neumaier) and pairwise summation, error-free transformations with FMA, ULP comparisons.
›
Swiss table
data-structures · low: Open-addressing hash table that filters 8-16 slots at once with 1-byte hash tags and SIMD, giving fast lookups at 87.5 % load.
›
Interval tree
data-structures · low: Index of ranges that answers "which intervals overlap this point or range?" in O(log n + k); static sorted-array form is the compact, cache-friendly choice.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
Fast hash functions
data-structures · low: Non-cryptographic hashes built from 64×64→128-bit multiplies and xor-shifts; pick by key size, trust level and whether the hash is persisted.
›
Sparse matrices (CSR)
numerics · low: Compressed sparse row stores only nonzeros as row pointers, column indices and values; SpMV streams them once, so it is memory-bandwidth bound.
›
Topological sort
graphs · low: Order the vertices of a DAG so every edge points forward; the core of build systems, schedulers and dependency resolution, with cycle detection for free.
›
k-d tree
geometry · low: A binary tree that splits k-dimensional points by one coordinate per level, giving fast nearest-neighbour and box queries on static low-dimensional data.
›
LU and Cholesky factorization
numerics · low: Factor A = PLU (any square matrix, partial pivoting) or A = LLᵀ (SPD) once in O(n³), then solve each right-hand side in O(n²).
›
Copy for AI
Similar systems
Share the most building blocks with this one.
sat-smt-solver
21 %
lp-mip-solver
16 %
vehicle-routing
15 %