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Transport, logistics and mobility
supply-chain-planning
supply chain planning
Kinaxis
System
Techniques · 13
Primitives · 39
supply-chain-planning
Min cost flow
graphs · medium: Minimum-cost flow (successive shortest paths, network simplex) for distribution and rebalancing
Bill of materials explosion
optimization · medium: MRP netting: explode BOMs level by level with lead-time offsetting into planned orders
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.
›
Inventory policies safety stock
optimization · medium: (s,S), base-stock, EOQ and newsvendor policies with safety stock from demand uncertainty
Mixed-integer programming (branch a…
optimization · medium: Solves LPs with integer variables by solving the LP relaxation, branching on a fractional variable, and pruning subtrees whose LP bound cannot beat the incumbent.
›
Time-series forecasting (baselines,…
statistics · medium: The end-to-end recipe: fix horizon and uncertainty, beat naive baselines, choose a model family, and validate with rolling-origin backtests and scale-free metrics.
›
Columnar storage
storage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.
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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.
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Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
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.
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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.
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Bellman ford negative cycles
graphs · low: Bellman-Ford shortest paths with negative edges and negative-cycle (arbitrage) detection
Dijkstra's algorithm
graphs · low: Single-source shortest paths with non-negative edge weights by settling vertices in order of distance with a priority queue.
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Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
Calendar date arithmetic
systems · low: Civil dates, time zones (tzdb), DST gaps, month-end anchoring and duration math
Interval arithmetic
numerics · low: Rigorous bounds with outward rounding; root isolation and robust geometric tests
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.
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Journaled state revert
storage · low: Undo journal of state writes enabling nested checkpoints and cheap revert
Croston method
statistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)
Exponential smoothing (ETS, Holt-Wi…
statistics · low: Forecast by keeping a few running states (level, trend, seasonal) updated by a fraction of each forecast error; O(1) memory per series and a strong baseline.
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STL decomposition (seasonal-trend w…
statistics · low: Split a series into trend, seasonal and remainder with iterated loess smoothing; the seasonal shape may drift and robust weights keep outliers out of it.
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Time-series cross-validation (rolli…
statistics · low: Estimate forecast error by repeatedly training on the past and testing on the next h steps, moving the origin forward; never trains on the future.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
+ 6 more techniques
+ 23 more primitives
System
Techniques · 13
Primitives · 39
supply-chain-planning
Min cost flow
graphs · medium: Minimum-cost flow (successive shortest paths, network simplex) for distribution and rebalancing
Bill of materials explosion
optimization · medium: MRP netting: explode BOMs level by level with lead-time offsetting into planned orders
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.
›
Inventory policies safety stock
optimization · medium: (s,S), base-stock, EOQ and newsvendor policies with safety stock from demand uncertainty
Mixed-integer programming (branch a…
optimization · medium: Solves LPs with integer variables by solving the LP relaxation, branching on a fractional variable, and pruning subtrees whose LP bound cannot beat the incumbent.
›
Time-series forecasting (baselines,…
statistics · medium: The end-to-end recipe: fix horizon and uncertainty, beat naive baselines, choose a model family, and validate with rolling-origin backtests and scale-free metrics.
›
Columnar storage
storage · medium: Store each column contiguously so analytic scans read only the columns they need, compress well and run as tight SIMD loops.
›
MVCC
storage · medium: Keep timestamped versions of each row so readers see a consistent snapshot without blocking writers; the default concurrency control in modern databases.
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Vectorized execution
storage · medium: Query operators pass batches of about 1-2K values per column instead of single tuples, amortizing interpretation overhead and enabling tight SIMD loops.
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Incremental computation
systems · medium: Dependency graph of derived values recomputed only when inputs change (reactive cells, spreadsheets)
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.
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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.
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Arima
statistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fit
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.
›
Monte Carlo integration
numerics · low: Estimate an expectation by averaging random samples; error falls as σ/√N in any dimension, and variance reduction (antithetic, control variates, importance sampling) shrinks σ.
›
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.
›
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.
›
Bellman ford negative cycles
graphs · low: Bellman-Ford shortest paths with negative edges and negative-cycle (arbitrage) detection
Dijkstra's algorithm
graphs · low: Single-source shortest paths with non-negative edge weights by settling vertices in order of distance with a priority queue.
›
Hash aggregation
storage · low: GROUP BY via hash tables with partitioning and spill to disk
Calendar date arithmetic
systems · low: Civil dates, time zones (tzdb), DST gaps, month-end anchoring and duration math
Interval arithmetic
numerics · low: Rigorous bounds with outward rounding; root isolation and robust geometric tests
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.
›
Journaled state revert
storage · low: Undo journal of state writes enabling nested checkpoints and cheap revert
Croston method
statistics · low: Forecasting intermittent demand by separately smoothing demand sizes and intervals (Croston, SBA, TSB)
Exponential smoothing (ETS, Holt-Wi…
statistics · low: Forecast by keeping a few running states (level, trend, seasonal) updated by a fraction of each forecast error; O(1) memory per series and a strong baseline.
›
STL decomposition (seasonal-trend w…
statistics · low: Split a series into trend, seasonal and remainder with iterated loess smoothing; the seasonal shape may drift and robust weights keep outliers out of it.
›
Time-series cross-validation (rolli…
statistics · low: Estimate forecast error by repeatedly training on the past and testing on the next h steps, moving the origin forward; never trains on the future.
›
Bit packing and PFOR
compression · low: Store blocks of integers with exactly b bits each and patch rare outliers separately (PFOR); the fastest integer codec family, billions of values per second.
›
Delta encoding
compression · low: Store differences between consecutive values instead of the values, turning sorted IDs, timestamps and counters into small numbers that pack into few bits.
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Dictionary encoding
compression · low: Map values to small integer codes via a dictionary for compact, comparable columns
Zone maps
storage · low: Per-block min/max (and sparse primary) indexes to skip data during scans
Hash consing
data-structures · low: Intern structurally equal immutable terms so equality is pointer comparison and sharing is maximal
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.
›
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.
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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.
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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.
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Finite differences
numerics · low: Replace derivatives with weighted differences of grid values; simple stencils plus a stability condition (CFL) turn PDEs like heat, advection and waves into array loops.
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L-BFGS
numerics · low: Quasi-Newton minimizer that approximates the inverse Hessian from the last m gradient differences via the two-loop recursion; O(m·n) memory and work per step.
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Kalman filter
robotics · low: Optimal recursive estimator for a linear system with Gaussian noise: fuses a motion model with noisy measurements into a state estimate plus its covariance.
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Varint (LEB128)
compression · low: Variable-length integers in 7-bit groups with a continuation bit; small numbers take 1 byte, the wire format of Protobuf, WebAssembly and DWARF.
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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.
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SIMD programming
hardware-os · low: Process 4-16 lanes per instruction with AVX2, AVX-512 or NEON via Rust std::arch, with runtime dispatch and a portable fallback.
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Evaluation metrics
machine-learning · low: Precision, recall, ROC AUC, average precision, log loss, NDCG and pass@k computed correctly, plus paired confidence intervals for comparing models.
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Loss functions
machine-learning · low: The scalar a model minimizes: cross-entropy for classes and tokens, MSE or Huber for values, each with a stable form and simple gradient.
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Rejection sampling
ml-training · low: Best-of-n and filtered sampling: generate many candidates, keep those passing a verifier or reward threshold
Explicit Euler
numerics · low: Step an ODE forward by its current slope, y += h·f(t, y); first-order, one evaluation per step, the baseline every other integrator is measured against.
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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).
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PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
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Thomas tridiagonal solver
numerics · low: O(n) tridiagonal linear solve used in implicit finite-difference and spline schemes
Bootstrap confidence intervals
statistics · low: Resampling-based confidence intervals for arbitrary statistics
Robust statistics
statistics · low: Location and scale estimates (median, MAD, trimmed mean, Huber M-estimator) that stay correct when a minority of the data is garbage.
›
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