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Healthcare
hospital-scheduling
hospital and OR scheduling
Epic Cadence
System
Techniques · 15
Primitives · 42
hospital-scheduling
Inventory reservation
finance · medium: Reserving stock with TTL holds, release on timeout and no overselling under contention
Gradient-boosted decision trees (GB…
machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.
›
Discrete event simulation
numerics · medium: Simulation driven by a time-ordered event queue with deterministic, reproducible replay
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.
›
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.
›
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.
›
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.
›
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.
›
Fhir resource model
networking · low: FHIR resources, references, search parameters and REST interactions for health data exchange
Hl7v2 message parsing
networking · low: HL7 v2 pipe-delimited segments, escaping, ACKs and MLLP framing for clinical interfaces
Optimistic concurrency control
storage · low: Run transactions without locks, record what they read, and validate at commit that nothing changed; fastest when conflicts are rare.
›
Conditional writes
distributed · low: Put-if-absent / if-match compare-and-swap on objects or keys as a commit primitive
Idempotency keys
finance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.
›
Hierarchical timing wheel
systems · low: Timers bucketed by expiry into wheels of slots, coarser wheels for far deadlines: O(1) insert and cancel for millions of timeouts.
›
Quantile sketches
data-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histograms
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.
›
Regularization
machine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
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
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.
›
+ 8 more techniques
+ 26 more primitives
System
Techniques · 15
Primitives · 42
hospital-scheduling
Inventory reservation
finance · medium: Reserving stock with TTL holds, release on timeout and no overselling under contention
Gradient-boosted decision trees (GB…
machine-learning · medium: Add small regression trees one at a time, each fitted to the loss gradient; histogram splits over 256 bins make it the strongest tabular default.
›
Discrete event simulation
numerics · medium: Simulation driven by a time-ordered event queue with deterministic, reproducible replay
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.
›
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.
›
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.
›
Recurrence rule expansion
systems · medium: Expand an RFC 5545 RRULE lazily, in local wall-clock time, into concrete instants; store the rule, materialise only a bounded horizon.
›
Critical path analysis
graphs · medium: Longest dependency chain in a DAG of timed spans or build actions
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.
›
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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Simulated annealing
optimization · medium: Stochastic local search with a cooling schedule for combinatorial optimization
Arima
statistics · medium: ARIMA/SARIMA forecasting: differencing, AR and MA terms, order selection, maximum-likelihood fit
Local search 2opt or opt
optimization · medium: 2-opt, Or-opt, relocate and swap neighborhoods with fast delta evaluation for routes
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.
›
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.
›
Fhir resource model
networking · low: FHIR resources, references, search parameters and REST interactions for health data exchange
Hl7v2 message parsing
networking · low: HL7 v2 pipe-delimited segments, escaping, ACKs and MLLP framing for clinical interfaces
Optimistic concurrency control
storage · low: Run transactions without locks, record what they read, and validate at commit that nothing changed; fastest when conflicts are rare.
›
Conditional writes
distributed · low: Put-if-absent / if-match compare-and-swap on objects or keys as a commit primitive
Idempotency keys
finance · low: Client-chosen unique key per logical operation, stored with a request fingerprint and the final response, so retries replay the first result instead of acting twice.
›
Hierarchical timing wheel
systems · low: Timers bucketed by expiry into wheels of slots, coarser wheels for far deadlines: O(1) insert and cancel for millions of timeouts.
›
Quantile sketches
data-structures · low: Mergeable quantile sketches: t-digest, DDSketch, HDR histograms
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.
›
Regularization
machine-learning · low: Techniques that trade training fit for generalization: weight decay, dropout, early stopping, label smoothing and data mixing, with the exact update rules and defaults.
›
PRNGs (PCG, xoshiro)
numerics · low: Small, fast, statistically strong non-cryptographic generators: PCG32 and xoshiro256**, with seeding, parallel streams and unbiased ranges.
›
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
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.
›
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.
›
Icalendar format
storage · low: ICalendar (RFC 5545) objects, VTIMEZONE and iTIP scheduling messages
Calendar date arithmetic
systems · low: Civil dates, time zones (tzdb), DST gaps, month-end anchoring and duration math
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.
›
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.
›
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.
›
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.
›
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.
›
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.
›
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.
›
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.
›
Fencing tokens
distributed · low: A number that grows with every lock grant and is checked by the protected resource, so a paused ex-holder's late write is rejected.
›
Leases
distributed · low: A lock that expires on its own: bounded-time ownership that survives crashes and partitions, given bounded clock drift and fencing tokens on writes.
›
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.
›
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.
›
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).
›
Thomas tridiagonal solver
numerics · low: O(n) tridiagonal linear solve used in implicit finite-difference and spline schemes
Cryptographic hashes (SHA-2, BLAKE3)
security · low: Collision-resistant fingerprints of data; SHA-256 for interoperability, BLAKE3 for speed, HMAC/HKDF for keyed uses.
›
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.
›
Exponential backoff with jitter
systems · low: Retry after delays that grow geometrically and are randomized, so failed clients spread out instead of hammering a recovering service in synchronized waves.
›
Slab allocator
systems · low: Pre-carve pages into equal-size slots per size class with an in-place free list: O(1) alloc and free, no external fragmentation, cache-warm reuse.
›
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