Latest Threat Research:SANDWORM_MODE: Shai-Hulud-Style npm Worm Hijacks CI Workflows and Poisons AI Toolchains.Details →
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lapx - npm Package Compare versions

Comparing version
0.9.0
 to
0.9.1
+431
-426
benchmark.md
[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg)](https://github.com/rathaROG/lapx/releases)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v=0.8.1)](https://badge.fury.io/py/lapx)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v=0.9.1)](https://badge.fury.io/py/lapx)
[![Benchmark (Single)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml/badge.svg)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml)

@@ -30,7 +30,12 @@ [![Benchmark (Batch)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_batch.yaml/badge.svg)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_batch.yaml)

📊 Some benchmark results using `lapx` [v0.9.0](https://github.com/rathaROG/lapx/releases/tag/v0.8.0) (2025/10/27):
📊 Some benchmark results using `lapx` [v0.9.1](https://github.com/rathaROG/lapx/releases/tag/v0.9.1) (2025/10/31):
<details><summary>🗂️ Batch on my local Windows 11 i9-13900KS (8 p-core + 8 e-core) + python 3.11.9:</summary>
<details><summary>🗂️ Batch on my local Windows 11 i9-13900KS (8 p-core + 8 e-core) + python 3.11.9:</summary><br>
```
numpy==2.2.6
lapx @ git+https://github.com/rathaROG/lapx.git@8e1a5c5cbe1a813d5ee80570b285e316fcc99f7a # 0.9.1
```
```
Microsoft Windows [Version 10.0.26200.7019]

@@ -43,49 +48,49 @@ (c) Microsoft Corporation. All rights reserved.

CPU lapx-batch-jvx : cost=16.48859572, time=0.67588449s
CPU lapx-batch-jvs : cost=16.48859572, time=0.46411657s
CPU lapx-batch-jvxa : cost=16.48859572, time=0.71385884s
CPU lapx-batch-jvsa : cost=16.48859572, time=0.45670390s
CPU lapx-batch-jvsa64 : cost=16.48859572, time=0.70847058s
CPU lapx-loop-jvx : cost=16.48859572, time=3.95986462s
CPU lapx-loop-jvs : cost=16.48859572, time=2.66866994s
CPU lapx-batch-jvx : cost=16.48732425, time=0.70944118s
CPU lapx-batch-jvs : cost=16.48732425, time=0.43542552s
CPU lapx-batch-jvxa : cost=16.48732425, time=0.69259763s
CPU lapx-batch-jvsa : cost=16.48732425, time=0.44047427s
CPU lapx-batch-jvsa64 : cost=16.48732425, time=0.78757620s
CPU lapx-loop-jvx : cost=16.48732425, time=4.28805971s
CPU lapx-loop-jvs : cost=16.48732425, time=2.85956860s
# 20 x (3000x2000) | n_threads = 24
CPU lapx-batch-jvx : cost=16.65042067, time=0.18923616s
CPU lapx-batch-jvs : cost=16.65042067, time=0.17624354s
CPU lapx-batch-jvxa : cost=16.65042067, time=0.18447852s
CPU lapx-batch-jvsa : cost=16.65042067, time=0.18925667s
CPU lapx-batch-jvsa64 : cost=16.65042067, time=0.18949389s
CPU lapx-loop-jvx : cost=16.65042067, time=0.85662770s
CPU lapx-loop-jvs : cost=16.65042067, time=1.05569839s
CPU lapx-batch-jvx : cost=16.69374066, time=0.19042516s
CPU lapx-batch-jvs : cost=16.69374066, time=0.19088888s
CPU lapx-batch-jvxa : cost=16.69374066, time=0.17689967s
CPU lapx-batch-jvsa : cost=16.69374066, time=0.18332553s
CPU lapx-batch-jvsa64 : cost=16.69374066, time=0.18913651s
CPU lapx-loop-jvx : cost=16.69374066, time=0.85293603s
CPU lapx-loop-jvs : cost=16.69374066, time=1.09705830s
# 50 x (2000x2000) | n_threads = 24
CPU lapx-batch-jvx : cost=82.12386385, time=0.56725645s
CPU lapx-batch-jvs : cost=82.12386385, time=0.37664533s
CPU lapx-batch-jvxa : cost=82.12386385, time=0.57265162s
CPU lapx-batch-jvsa : cost=82.12386385, time=0.37772393s
CPU lapx-batch-jvsa64 : cost=82.12386385, time=0.61493921s
CPU lapx-loop-jvx : cost=82.12386385, time=4.46092606s
CPU lapx-loop-jvs : cost=82.12386385, time=3.49988031s
CPU lapx-batch-jvx : cost=81.88714629, time=0.40948844s
CPU lapx-batch-jvs : cost=81.88714629, time=0.34669971s
CPU lapx-batch-jvxa : cost=81.88714629, time=0.39700556s
CPU lapx-batch-jvsa : cost=81.88714629, time=0.33096647s
CPU lapx-batch-jvsa64 : cost=81.88714629, time=0.44180655s
CPU lapx-loop-jvx : cost=81.88714629, time=3.34968209s
CPU lapx-loop-jvs : cost=81.88714629, time=3.22587180s
# 100 x (1000x2000) | n_threads = 24
CPU lapx-batch-jvx : cost=58.19636934, time=0.18971944s
CPU lapx-batch-jvs : cost=58.19636934, time=0.16700149s
CPU lapx-batch-jvxa : cost=58.19636934, time=0.18943620s
CPU lapx-batch-jvsa : cost=58.19636934, time=0.16706610s
CPU lapx-batch-jvsa64 : cost=58.19636934, time=0.25204611s
CPU lapx-loop-jvx : cost=58.19636934, time=1.02838278s
CPU lapx-loop-jvs : cost=58.19636934, time=1.21967244s
CPU lapx-batch-jvx : cost=57.81402817, time=0.18936110s
CPU lapx-batch-jvs : cost=57.81402817, time=0.16963148s
CPU lapx-batch-jvxa : cost=57.81402817, time=0.18951726s
CPU lapx-batch-jvsa : cost=57.81402817, time=0.16521573s
CPU lapx-batch-jvsa64 : cost=57.81402817, time=0.25324345s
CPU lapx-loop-jvx : cost=57.81402817, time=0.96780610s
CPU lapx-loop-jvs : cost=57.81402817, time=1.20634890s
# 500 x (1000x1000) | n_threads = 24
CPU lapx-batch-jvx : cost=821.97407482, time=0.59273267s
CPU lapx-batch-jvs : cost=821.97407482, time=0.58274126s
CPU lapx-batch-jvxa : cost=821.97407482, time=0.58346224s
CPU lapx-batch-jvsa : cost=821.97407482, time=0.58098578s
CPU lapx-batch-jvsa64 : cost=821.97407482, time=0.61520362s
CPU lapx-loop-jvx : cost=821.97407482, time=6.64442897s
CPU lapx-loop-jvs : cost=821.97407482, time=7.03527546s
CPU lapx-batch-jvx : cost=820.77561875, time=0.55759573s
CPU lapx-batch-jvs : cost=820.77561875, time=0.56053782s
CPU lapx-batch-jvxa : cost=820.77561875, time=0.55279994s
CPU lapx-batch-jvsa : cost=820.77561875, time=0.56725907s
CPU lapx-batch-jvsa64 : cost=820.77561875, time=0.58956695s
CPU lapx-loop-jvx : cost=820.77561875, time=6.52994561s
CPU lapx-loop-jvs : cost=820.77561875, time=7.08902001s
```

@@ -97,3 +102,3 @@

https://github.com/rathaROG/lapx/actions/runs/18961613065/job/54149890164
https://github.com/rathaROG/lapx/actions/runs/18984608559/job/54225293380

@@ -104,17 +109,17 @@ ```

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.64 x slower
* lapjv : ✅ Passed 🐌 5.53 x slower
* lapjvx : ✅ Passed 🐌 2.68 x slower
* lapjvxa : ✅ Passed 🐌 1.81 x slower
* lapjvs : ✅ Passed 🐌 3.56 x slower
* lapjvc : ✅ Passed 🐌 1.53 x slower
* lapjv : ✅ Passed 🐌 5.17 x slower
* lapjvx : ✅ Passed 🐌 2.58 x slower
* lapjvxa : ✅ Passed 🐌 1.69 x slower
* lapjvs : ✅ Passed 🐌 3.41 x slower
* lapjvsa : ✅ Passed 🐌 3.36 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. scipy ⭐ : 0.00001024s
2. lapjvc : 0.00001677s
3. lapjvxa : 0.00001853s
4. lapjvx : 0.00002740s
5. lapjvsa : 0.00003439s
6. lapjvs : 0.00003648s
7. lapjv : 0.00005660s
1. scipy ⭐ : 0.00001055s
2. lapjvc : 0.00001615s
3. lapjvxa : 0.00001785s
4. lapjvx : 0.00002719s
5. lapjvsa : 0.00003547s
6. lapjvs : 0.00003601s
7. lapjv : 0.00005454s
-------------------------------

@@ -125,17 +130,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 2.03 x slower
* lapjv : ✅ Passed 🐌 5.72 x slower
* lapjvx : ✅ Passed 🐌 2.28 x slower
* lapjvxa : ✅ Passed 🐌 1.75 x slower
* lapjvs : ✅ Passed 🐌 2.7 x slower
* lapjvsa : ✅ Passed 🐌 1.04 x slower
* lapjvc : ✅ Passed 🐌 1.65 x slower
* lapjv : ✅ Passed 🐌 4.75 x slower
* lapjvx : ✅ Passed 🐌 2.0 x slower
* lapjvxa : ✅ Passed 🐌 1.98 x slower
* lapjvs : ✅ Passed 🐌 2.43 x slower
* lapjvsa : ✅ Passed 🏆 1.11 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. scipy ⭐ : 0.00000664s
2. lapjvsa : 0.00000690s
3. lapjvxa : 0.00001165s
4. lapjvc : 0.00001346s
5. lapjvx : 0.00001517s
6. lapjvs : 0.00001796s
7. lapjv : 0.00003800s
1. lapjvsa : 0.00000672s
2. scipy ⭐ : 0.00000748s
3. lapjvc : 0.00001232s
4. lapjvxa : 0.00001484s
5. lapjvx : 0.00001497s
6. lapjvs : 0.00001819s
7. lapjv : 0.00003557s
-------------------------------

@@ -146,17 +151,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 2.3 x slower
* lapjv : ✅ Passed 🐌 9.53 x slower
* lapjvx : ✅ Passed 🐌 3.62 x slower
* lapjvxa : ✅ Passed 🐌 3.04 x slower
* lapjvs : ✅ Passed 🐌 4.63 x slower
* lapjvsa : ✅ Passed 🐌 5.32 x slower
* lapjvc : ✅ Passed 🐌 2.16 x slower
* lapjv : ✅ Passed 🐌 9.32 x slower
* lapjvx : ✅ Passed 🐌 4.22 x slower
* lapjvxa : ✅ Passed 🐌 2.93 x slower
* lapjvs : ✅ Passed 🐌 4.38 x slower
* lapjvsa : ✅ Passed 🐌 4.89 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. scipy ⭐ : 0.00000537s
2. lapjvc : 0.00001233s
3. lapjvxa : 0.00001631s
4. lapjvx : 0.00001947s
5. lapjvs : 0.00002489s
6. lapjvsa : 0.00002857s
7. lapjv : 0.00005116s
1. scipy ⭐ : 0.00000558s
2. lapjvc : 0.00001205s
3. lapjvxa : 0.00001633s
4. lapjvx : 0.00002352s
5. lapjvs : 0.00002447s
6. lapjvsa : 0.00002730s
7. lapjv : 0.00005201s
-------------------------------

@@ -167,17 +172,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.94 x slower
* lapjv : ✅ Passed 🐌 1.34 x slower
* lapjvx : ✅ Passed 🏆 1.15 x faster
* lapjvxa : ✅ Passed 🏆 1.41 x faster
* lapjvs : ✅ Passed 🐌 1.98 x slower
* lapjvsa : ✅ Passed 🏆 1.13 x faster
* lapjvc : ✅ Passed 🐌 1.41 x slower
* lapjv : ✅ Passed 🐌 1.04 x slower
* lapjvx : ✅ Passed 🏆 1.42 x faster
* lapjvxa : ✅ Passed 🏆 1.77 x faster
* lapjvs : ✅ Passed 🏆 1.37 x faster
* lapjvsa : ✅ Passed 🏆 1.5 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.00003351s
2. lapjvx : 0.00004110s
3. lapjvsa : 0.00004164s
4. scipy ⭐ : 0.00004721s
5. lapjv : 0.00006310s
6. lapjvc : 0.00009150s
7. lapjvs : 0.00009357s
1. lapjvxa : 0.00003649s
2. lapjvsa : 0.00004314s
3. lapjvx : 0.00004534s
4. lapjvs : 0.00004701s
5. scipy ⭐ : 0.00006450s
6. lapjv : 0.00006738s
7. lapjvc : 0.00009109s
-------------------------------

@@ -188,17 +193,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🏆 1.43 x faster
* lapjv : ✅ Passed 🏆 1.44 x faster
* lapjvx : ✅ Passed 🏆 2.25 x faster
* lapjvxa : ✅ Passed 🏆 2.94 x faster
* lapjvs : ✅ Passed 🏆 2.27 x faster
* lapjvsa : ✅ Passed 🏆 3.99 x faster
* lapjvc : ✅ Passed 🏆 1.21 x faster
* lapjv : ✅ Passed 🏆 1.03 x faster
* lapjvx : ✅ Passed 🏆 1.54 x faster
* lapjvxa : ✅ Passed 🏆 2.0 x faster
* lapjvs : ✅ Passed 🏆 1.46 x faster
* lapjvsa : ✅ Passed 🏆 2.49 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvsa : 0.00002166s
2. lapjvxa : 0.00002932s
3. lapjvs : 0.00003803s
4. lapjvx : 0.00003831s
5. lapjv : 0.00005988s
6. lapjvc : 0.00006028s
7. scipy ⭐ : 0.00008634s
1. lapjvsa : 0.00002653s
2. lapjvxa : 0.00003293s
3. lapjvx : 0.00004293s
4. lapjvs : 0.00004526s
5. lapjvc : 0.00005453s
6. lapjv : 0.00006407s
7. scipy ⭐ : 0.00006601s
-------------------------------

@@ -209,17 +214,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.22 x slower
* lapjv : ✅ Passed 🏆 1.07 x faster
* lapjvx : ✅ Passed 🏆 1.54 x faster
* lapjvxa : ✅ Passed 🏆 1.88 x faster
* lapjvs : ✅ Passed 🏆 1.63 x faster
* lapjvsa : ✅ Passed 🏆 1.47 x faster
* lapjvc : ✅ Passed 🐌 1.45 x slower
* lapjv : ✅ Passed 🏆 1.11 x faster
* lapjvx : ✅ Passed 🏆 1.79 x faster
* lapjvxa : ✅ Passed 🏆 2.13 x faster
* lapjvs : ✅ Passed 🏆 1.73 x faster
* lapjvsa : ✅ Passed 🏆 1.41 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.00004026s
2. lapjvs : 0.00004654s
3. lapjvx : 0.00004924s
4. lapjvsa : 0.00005152s
5. lapjv : 0.00007051s
6. scipy ⭐ : 0.00007566s
7. lapjvc : 0.00009201s
1. lapjvxa : 0.00003505s
2. lapjvx : 0.00004173s
3. lapjvs : 0.00004336s
4. lapjvsa : 0.00005296s
5. lapjv : 0.00006746s
6. scipy ⭐ : 0.00007480s
7. lapjvc : 0.00010855s
-------------------------------

@@ -230,17 +235,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 4.97 x slower
* lapjv : ✅ Passed 🏆 2.02 x faster
* lapjvx : ✅ Passed 🏆 2.34 x faster
* lapjvxa : ✅ Passed 🏆 3.09 x faster
* lapjvs : ✅ Passed 🏆 3.95 x faster
* lapjvsa : ✅ Passed 🏆 3.89 x faster
* lapjvc : ✅ Passed 🐌 3.8 x slower
* lapjv : ✅ Passed 🏆 2.17 x faster
* lapjvx : ✅ Passed 🏆 3.0 x faster
* lapjvxa : ✅ Passed 🏆 4.33 x faster
* lapjvs : ✅ Passed 🏆 4.67 x faster
* lapjvsa : ✅ Passed 🏆 4.63 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvs : 0.00116294s
2. lapjvsa : 0.00117967s
3. lapjvxa : 0.00148459s
4. lapjvx : 0.00196006s
5. lapjv : 0.00227485s
6. scipy ⭐ : 0.00458916s
7. lapjvc : 0.02279758s
1. lapjvs : 0.00102049s
2. lapjvsa : 0.00102816s
3. lapjvxa : 0.00110050s
4. lapjvx : 0.00158621s
5. lapjv : 0.00219654s
6. scipy ⭐ : 0.00476269s
7. lapjvc : 0.01809850s
-------------------------------

@@ -251,17 +256,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🏆 1.46 x faster
* lapjv : ✅ Passed 🏆 1.19 x faster
* lapjvx : ✅ Passed 🏆 1.19 x faster
* lapjvxa : ✅ Passed 🏆 1.21 x faster
* lapjvs : ✅ Passed 🏆 1.58 x faster
* lapjvsa : ✅ Passed 🏆 1.6 x faster
* lapjvc : ✅ Passed 🏆 1.24 x faster
* lapjv : ✅ Passed 🏆 1.07 x faster
* lapjvx : ✅ Passed 🏆 1.4 x faster
* lapjvxa : ✅ Passed 🏆 1.41 x faster
* lapjvs : ✅ Passed 🏆 1.43 x faster
* lapjvsa : ✅ Passed 🏆 1.44 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvsa : 0.00610949s
2. lapjvs : 0.00617076s
3. lapjvc : 0.00669765s
4. lapjvxa : 0.00807378s
5. lapjvx : 0.00817340s
6. lapjv : 0.00822582s
7. scipy ⭐ : 0.00976096s
1. lapjvsa : 0.00626876s
2. lapjvs : 0.00630340s
3. lapjvxa : 0.00640438s
4. lapjvx : 0.00642594s
5. lapjvc : 0.00729600s
6. lapjv : 0.00845927s
7. scipy ⭐ : 0.00901426s
-------------------------------

@@ -272,17 +277,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 4.66 x slower
* lapjv : ✅ Passed 🏆 2.07 x faster
* lapjvx : ✅ Passed 🏆 3.44 x faster
* lapjvxa : ✅ Passed 🏆 3.42 x faster
* lapjvs : ✅ Passed 🏆 4.27 x faster
* lapjvsa : ✅ Passed 🏆 4.37 x faster
* lapjvc : ✅ Passed 🐌 4.58 x slower
* lapjv : ✅ Passed 🏆 2.04 x faster
* lapjvx : ✅ Passed 🏆 4.01 x faster
* lapjvxa : ✅ Passed 🏆 4.14 x faster
* lapjvs : ✅ Passed 🏆 4.42 x faster
* lapjvsa : ✅ Passed 🏆 4.49 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvsa : 0.00128856s
2. lapjvs : 0.00131904s
3. lapjvx : 0.00163978s
4. lapjvxa : 0.00164817s
5. lapjv : 0.00272247s
6. scipy ⭐ : 0.00563737s
7. lapjvc : 0.02625532s
1. lapjvsa : 0.00114634s
2. lapjvs : 0.00116270s
3. lapjvxa : 0.00124253s
4. lapjvx : 0.00128147s
5. lapjv : 0.00252214s
6. scipy ⭐ : 0.00514199s
7. lapjvc : 0.02353958s
-------------------------------

@@ -293,17 +298,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 257.47 x slower
* lapjvc : ✅ Passed 🐌 228.4 x slower
* lapjv : ✅ Passed 🏆 1.09 x faster
* lapjvx : ✅ Passed 🏆 1.09 x faster
* lapjvxa : ✅ Passed 🏆 1.08 x faster
* lapjvs : ✅ Passed 🐌 1.11 x slower
* lapjvsa : ✅ Passed 🐌 1.1 x slower
* lapjvx : ✅ Passed 🏆 1.24 x faster
* lapjvxa : ✅ Passed 🏆 1.25 x faster
* lapjvs : ✅ Passed 🐌 1.1 x slower
* lapjvsa : ✅ Passed 🐌 1.12 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvx : 0.09400308s
2. lapjv : 0.09424586s
3. lapjvxa : 0.09509772s
4. scipy ⭐ : 0.10258777s
5. lapjvsa : 0.11241747s
6. lapjvs : 0.11372150s
7. lapjvc : 26.41295845s
1. lapjvxa : 0.08090518s
2. lapjvx : 0.08157910s
3. lapjv : 0.09252072s
4. scipy ⭐ : 0.10097560s
5. lapjvs : 0.11067445s
6. lapjvsa : 0.11269509s
7. lapjvc : 23.06289135s
-------------------------------

@@ -314,17 +319,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.02 x slower
* lapjv : ✅ Passed 🐌 1.62 x slower
* lapjvx : ✅ Passed 🐌 1.62 x slower
* lapjvxa : ✅ Passed 🐌 1.62 x slower
* lapjvs : ✅ Passed 🏆 1.76 x faster
* lapjvsa : ✅ Passed 🏆 1.76 x faster
* lapjvc : ✅ Passed 🏆 1.33 x faster
* lapjv : ✅ Passed 🐌 1.02 x slower
* lapjvx : ✅ Passed 🏆 1.42 x faster
* lapjvxa : ✅ Passed 🏆 1.42 x faster
* lapjvs : ✅ Passed 🏆 2.3 x faster
* lapjvsa : ✅ Passed 🏆 2.3 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvs : 1.34793133s
2. lapjvsa : 1.34966543s
3. scipy ⭐ : 2.37237136s
4. lapjvc : 2.41397720s
5. lapjvxa : 3.84284193s
6. lapjvx : 3.84922083s
7. lapjv : 3.85101395s
1. lapjvsa : 0.97763377s
2. lapjvs : 0.97772870s
3. lapjvx : 1.58527767s
4. lapjvxa : 1.58615075s
5. lapjvc : 1.69588961s
6. scipy ⭐ : 2.24908994s
7. lapjv : 2.28853597s
-------------------------------

@@ -335,17 +340,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 273.6 x slower
* lapjv : ✅ Passed 🏆 2.03 x faster
* lapjvx : ✅ Passed 🏆 2.04 x faster
* lapjvxa : ✅ Passed 🏆 2.05 x faster
* lapjvs : ✅ Passed 🏆 1.59 x faster
* lapjvsa : ✅ Passed 🏆 1.63 x faster
* lapjvc : ✅ Passed 🐌 217.35 x slower
* lapjv : ✅ Passed 🏆 2.07 x faster
* lapjvx : ✅ Passed 🏆 2.4 x faster
* lapjvxa : ✅ Passed 🏆 2.42 x faster
* lapjvs : ✅ Passed 🏆 1.65 x faster
* lapjvsa : ✅ Passed 🏆 1.64 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.19721307s
2. lapjvx : 0.19786040s
3. lapjv : 0.19958704s
4. lapjvsa : 0.24835583s
5. lapjvs : 0.25347052s
6. scipy ⭐ : 0.40418303s
7. lapjvc : 110.58635478s
1. lapjvxa : 0.16547692s
2. lapjvx : 0.16660061s
3. lapjv : 0.19367327s
4. lapjvs : 0.24263112s
5. lapjvsa : 0.24431215s
6. scipy ⭐ : 0.40064618s
7. lapjvc : 87.08241680s
-------------------------------

@@ -358,3 +363,3 @@ ```

https://github.com/rathaROG/lapx/actions/runs/18961613065/job/54149890234
https://github.com/rathaROG/lapx/actions/runs/18984608559/job/54225293427

@@ -365,17 +370,17 @@ ```

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.71 x slower
* lapjv : ✅ Passed 🐌 5.14 x slower
* lapjvx : ✅ Passed 🐌 2.32 x slower
* lapjvxa : ✅ Passed 🐌 1.57 x slower
* lapjvs : ✅ Passed 🐌 3.57 x slower
* lapjvsa : ✅ Passed 🐌 3.25 x slower
* lapjvc : ✅ Passed 🐌 2.43 x slower
* lapjv : ✅ Passed 🐌 5.19 x slower
* lapjvx : ✅ Passed 🐌 2.7 x slower
* lapjvxa : ✅ Passed 🐌 1.48 x slower
* lapjvs : ✅ Passed 🐌 3.41 x slower
* lapjvsa : ✅ Passed 🐌 3.07 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. scipy ⭐ : 0.00000567s
2. lapjvxa : 0.00000888s
3. lapjvc : 0.00000971s
4. lapjvx : 0.00001317s
5. lapjvsa : 0.00001842s
6. lapjvs : 0.00002025s
7. lapjv : 0.00002913s
1. scipy ⭐ : 0.00000625s
2. lapjvxa : 0.00000925s
3. lapjvc : 0.00001521s
4. lapjvx : 0.00001688s
5. lapjvsa : 0.00001917s
6. lapjvs : 0.00002129s
7. lapjv : 0.00003242s
-------------------------------

@@ -386,17 +391,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.83 x slower
* lapjv : ✅ Passed 🐌 4.14 x slower
* lapjvx : ✅ Passed 🐌 1.83 x slower
* lapjvxa : ✅ Passed 🐌 1.59 x slower
* lapjvs : ✅ Passed 🐌 2.01 x slower
* lapjvsa : ✅ Passed 🏆 1.32 x faster
* lapjvc : ✅ Passed 🐌 3.64 x slower
* lapjv : ✅ Passed 🐌 4.33 x slower
* lapjvx : ✅ Passed 🐌 2.05 x slower
* lapjvxa : ✅ Passed 🐌 1.41 x slower
* lapjvs : ✅ Passed 🐌 2.8 x slower
* lapjvsa : ✅ Passed 🏆 1.17 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvsa : 0.00000283s
2. scipy ⭐ : 0.00000375s
3. lapjvxa : 0.00000596s
4. lapjvc : 0.00000687s
5. lapjvx : 0.00000687s
6. lapjvs : 0.00000754s
7. lapjv : 0.00001554s
1. lapjvsa : 0.00000346s
2. scipy ⭐ : 0.00000404s
3. lapjvxa : 0.00000571s
4. lapjvx : 0.00000829s
5. lapjvs : 0.00001133s
6. lapjvc : 0.00001471s
7. lapjv : 0.00001750s
-------------------------------

@@ -407,17 +412,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 2.66 x slower
* lapjv : ✅ Passed 🐌 5.96 x slower
* lapjvx : ✅ Passed 🐌 3.03 x slower
* lapjvxa : ✅ Passed 🐌 2.6 x slower
* lapjvs : ✅ Passed 🐌 3.77 x slower
* lapjvsa : ✅ Passed 🐌 4.37 x slower
* lapjvc : ✅ Passed 🐌 2.76 x slower
* lapjv : ✅ Passed 🐌 5.61 x slower
* lapjvx : ✅ Passed 🐌 2.84 x slower
* lapjvxa : ✅ Passed 🐌 2.87 x slower
* lapjvs : ✅ Passed 🐌 3.57 x slower
* lapjvsa : ✅ Passed 🐌 4.02 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. scipy ⭐ : 0.00000292s
2. lapjvxa : 0.00000758s
3. lapjvc : 0.00000775s
4. lapjvx : 0.00000883s
5. lapjvs : 0.00001100s
6. lapjvsa : 0.00001275s
7. lapjv : 0.00001738s
1. scipy ⭐ : 0.00000333s
2. lapjvc : 0.00000921s
3. lapjvx : 0.00000946s
4. lapjvxa : 0.00000958s
5. lapjvs : 0.00001192s
6. lapjvsa : 0.00001342s
7. lapjv : 0.00001871s
-------------------------------

@@ -428,17 +433,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 2.19 x slower
* lapjv : ✅ Passed 🏆 1.34 x faster
* lapjvx : ✅ Passed 🏆 1.83 x faster
* lapjvxa : ✅ Passed 🏆 2.24 x faster
* lapjvs : ✅ Passed 🏆 1.74 x faster
* lapjvsa : ✅ Passed 🏆 1.22 x faster
* lapjvc : ✅ Passed 🐌 1.9 x slower
* lapjv : ✅ Passed 🏆 1.31 x faster
* lapjvx : ✅ Passed 🏆 1.86 x faster
* lapjvxa : ✅ Passed 🏆 2.32 x faster
* lapjvs : ✅ Passed 🏆 1.63 x faster
* lapjvsa : ✅ Passed 🏆 1.91 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.00001796s
2. lapjvx : 0.00002200s
3. lapjvs : 0.00002308s
4. lapjv : 0.00002992s
5. lapjvsa : 0.00003283s
6. scipy ⭐ : 0.00004017s
7. lapjvc : 0.00008783s
1. lapjvxa : 0.00002146s
2. lapjvsa : 0.00002608s
3. lapjvx : 0.00002679s
4. lapjvs : 0.00003046s
5. lapjv : 0.00003796s
6. scipy ⭐ : 0.00004979s
7. lapjvc : 0.00009475s
-------------------------------

@@ -449,17 +454,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🏆 1.16 x faster
* lapjv : ✅ Passed 🏆 2.13 x faster
* lapjvx : ✅ Passed 🏆 2.69 x faster
* lapjvxa : ✅ Passed 🏆 3.29 x faster
* lapjvs : ✅ Passed 🏆 2.35 x faster
* lapjvsa : ✅ Passed 🏆 3.41 x faster
* lapjvc : ✅ Passed 🐌 1.61 x slower
* lapjv : ✅ Passed 🏆 1.41 x faster
* lapjvx : ✅ Passed 🏆 2.05 x faster
* lapjvxa : ✅ Passed 🏆 2.66 x faster
* lapjvs : ✅ Passed 🏆 1.69 x faster
* lapjvsa : ✅ Passed 🏆 2.87 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvsa : 0.00002146s
2. lapjvxa : 0.00002221s
3. lapjvx : 0.00002721s
4. lapjvs : 0.00003108s
5. lapjv : 0.00003437s
6. lapjvc : 0.00006300s
7. scipy ⭐ : 0.00007317s
1. lapjvsa : 0.00001892s
2. lapjvxa : 0.00002046s
3. lapjvx : 0.00002650s
4. lapjvs : 0.00003208s
5. lapjv : 0.00003850s
6. scipy ⭐ : 0.00005437s
7. lapjvc : 0.00008771s
-------------------------------

@@ -470,17 +475,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 2.31 x slower
* lapjv : ✅ Passed 🏆 1.15 x faster
* lapjvx : ✅ Passed 🏆 1.54 x faster
* lapjvxa : ✅ Passed 🏆 1.89 x faster
* lapjvs : ✅ Passed 🏆 1.47 x faster
* lapjvsa : ✅ Passed 🏆 1.53 x faster
* lapjvc : ✅ Passed 🐌 2.02 x slower
* lapjv : ✅ Passed 🏆 1.27 x faster
* lapjvx : ✅ Passed 🏆 1.86 x faster
* lapjvxa : ✅ Passed 🏆 2.27 x faster
* lapjvs : ✅ Passed 🏆 1.63 x faster
* lapjvsa : ✅ Passed 🏆 1.83 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.00001904s
2. lapjvx : 0.00002342s
3. lapjvsa : 0.00002350s
4. lapjvs : 0.00002458s
5. lapjv : 0.00003133s
6. scipy ⭐ : 0.00003604s
7. lapjvc : 0.00008329s
1. lapjvxa : 0.00002217s
2. lapjvx : 0.00002708s
3. lapjvsa : 0.00002746s
4. lapjvs : 0.00003079s
5. lapjv : 0.00003954s
6. scipy ⭐ : 0.00005025s
7. lapjvc : 0.00010162s
-------------------------------

@@ -491,17 +496,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 6.57 x slower
* lapjv : ✅ Passed 🏆 3.31 x faster
* lapjvx : ✅ Passed 🏆 3.69 x faster
* lapjvxa : ✅ Passed 🏆 3.81 x faster
* lapjvs : ✅ Passed 🏆 2.86 x faster
* lapjvsa : ✅ Passed 🏆 3.05 x faster
* lapjvc : ✅ Passed 🐌 7.42 x slower
* lapjv : ✅ Passed 🏆 2.99 x faster
* lapjvx : ✅ Passed 🏆 3.78 x faster
* lapjvxa : ✅ Passed 🏆 3.7 x faster
* lapjvs : ✅ Passed 🏆 2.95 x faster
* lapjvsa : ✅ Passed 🏆 3.03 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.00073746s
2. lapjvx : 0.00076150s
3. lapjv : 0.00085017s
4. lapjvsa : 0.00092200s
5. lapjvs : 0.00098329s
6. scipy ⭐ : 0.00281312s
7. lapjvc : 0.01849563s
1. lapjvx : 0.00077642s
2. lapjvxa : 0.00079467s
3. lapjvsa : 0.00096896s
4. lapjv : 0.00098104s
5. lapjvs : 0.00099629s
6. scipy ⭐ : 0.00293721s
7. lapjvc : 0.02179187s
-------------------------------

@@ -512,17 +517,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.37 x slower
* lapjv : ✅ Passed 🏆 1.09 x faster
* lapjvx : ✅ Passed 🏆 1.11 x faster
* lapjvxa : ✅ Passed 🏆 1.18 x faster
* lapjvs : ✅ Passed 🏆 1.06 x faster
* lapjvsa : ✅ Passed 🏆 1.04 x faster
* lapjvc : ✅ Passed 🐌 1.22 x slower
* lapjv : ✅ Passed 🏆 2.3 x faster
* lapjvx : ✅ Passed 🏆 2.42 x faster
* lapjvxa : ✅ Passed 🏆 2.39 x faster
* lapjvs : ✅ Passed 🏆 2.04 x faster
* lapjvsa : ✅ Passed 🏆 2.17 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.00629000s
2. lapjvx : 0.00669658s
3. lapjv : 0.00680446s
4. lapjvs : 0.00702367s
5. lapjvsa : 0.00716958s
6. scipy ⭐ : 0.00744075s
7. lapjvc : 0.01022246s
1. lapjvx : 0.00280804s
2. lapjvxa : 0.00284217s
3. lapjv : 0.00295687s
4. lapjvsa : 0.00313346s
5. lapjvs : 0.00333604s
6. scipy ⭐ : 0.00679725s
7. lapjvc : 0.00829000s
-------------------------------

@@ -533,17 +538,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 5.87 x slower
* lapjv : ✅ Passed 🏆 2.05 x faster
* lapjvx : ✅ Passed 🏆 3.86 x faster
* lapjvxa : ✅ Passed 🏆 2.83 x faster
* lapjvs : ✅ Passed 🏆 3.29 x faster
* lapjvsa : ✅ Passed 🏆 3.35 x faster
* lapjvc : ✅ Passed 🐌 6.83 x slower
* lapjv : ✅ Passed 🏆 2.53 x faster
* lapjvx : ✅ Passed 🏆 3.11 x faster
* lapjvxa : ✅ Passed 🏆 2.9 x faster
* lapjvs : ✅ Passed 🏆 2.57 x faster
* lapjvsa : ✅ Passed 🏆 2.52 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvx : 0.00102792s
2. lapjvsa : 0.00118358s
3. lapjvs : 0.00120550s
4. lapjvxa : 0.00140042s
5. lapjv : 0.00192958s
6. scipy ⭐ : 0.00396425s
7. lapjvc : 0.02326779s
1. lapjvx : 0.00112583s
2. lapjvxa : 0.00120517s
3. lapjvs : 0.00136033s
4. lapjv : 0.00138454s
5. lapjvsa : 0.00138996s
6. scipy ⭐ : 0.00349771s
7. lapjvc : 0.02389029s
-------------------------------

@@ -554,17 +559,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 349.67 x slower
* lapjv : ✅ Passed 🐌 3.52 x slower
* lapjvx : ✅ Passed 🐌 1.43 x slower
* lapjvxa : ✅ Passed 🐌 1.45 x slower
* lapjvs : ✅ Passed 🐌 3.18 x slower
* lapjvsa : ✅ Passed 🐌 2.84 x slower
* lapjvc : ✅ Passed 🐌 303.21 x slower
* lapjv : ✅ Passed 🐌 4.3 x slower
* lapjvx : ✅ Passed 🐌 1.65 x slower
* lapjvxa : ✅ Passed 🐌 1.37 x slower
* lapjvs : ✅ Passed 🐌 2.67 x slower
* lapjvsa : ✅ Passed 🐌 3.06 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. scipy ⭐ : 0.07873675s
2. lapjvx : 0.11293429s
3. lapjvxa : 0.11401713s
4. lapjvsa : 0.22370100s
5. lapjvs : 0.25039458s
6. lapjv : 0.27737229s
7. lapjvc : 27.53160242s
1. scipy ⭐ : 0.08946058s
2. lapjvxa : 0.12264183s
3. lapjvx : 0.14727325s
4. lapjvs : 0.23845862s
5. lapjvsa : 0.27356104s
6. lapjv : 0.38505029s
7. lapjvc : 27.12492925s
-------------------------------

@@ -575,17 +580,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 1.35 x slower
* lapjv : ✅ Passed 🏆 2.36 x faster
* lapjvx : ✅ Passed 🏆 2.15 x faster
* lapjvxa : ✅ Passed 🏆 2.23 x faster
* lapjvs : ✅ Passed 🏆 2.52 x faster
* lapjvsa : ✅ Passed 🏆 2.89 x faster
* lapjvc : ✅ Passed 🐌 1.61 x slower
* lapjv : ✅ Passed 🏆 1.6 x faster
* lapjvx : ✅ Passed 🏆 1.86 x faster
* lapjvxa : ✅ Passed 🏆 1.89 x faster
* lapjvs : ✅ Passed 🏆 2.06 x faster
* lapjvsa : ✅ Passed 🏆 2.6 x faster
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvsa : 0.90473763s
2. lapjvs : 1.03581571s
3. lapjv : 1.10758192s
4. lapjvxa : 1.17104621s
5. lapjvx : 1.21566396s
6. scipy ⭐ : 2.61083117s
7. lapjvc : 3.53453567s
1. lapjvsa : 0.94210742s
2. lapjvs : 1.19320912s
3. lapjvxa : 1.29491192s
4. lapjvx : 1.32094417s
5. lapjv : 1.53106525s
6. scipy ⭐ : 2.45269650s
7. lapjvc : 3.93988354s
-------------------------------

@@ -596,17 +601,17 @@

-----------------------------------------
* lapjvc : ✅ Passed 🐌 308.42 x slower
* lapjv : ✅ Passed 🐌 2.1 x slower
* lapjvx : ✅ Passed 🏆 1.32 x faster
* lapjvxa : ✅ Passed 🏆 1.87 x faster
* lapjvs : ✅ Passed 🐌 2.3 x slower
* lapjvsa : ✅ Passed 🐌 1.98 x slower
* lapjvc : ✅ Passed 🐌 313.47 x slower
* lapjv : ✅ Passed 🐌 1.34 x slower
* lapjvx : ✅ Passed 🏆 1.46 x faster
* lapjvxa : ✅ Passed 🏆 2.76 x faster
* lapjvs : ✅ Passed 🐌 1.47 x slower
* lapjvsa : ✅ Passed 🐌 1.38 x slower
----- 🎉 SPEED RANKING 🎉 -----
1. lapjvxa : 0.18422821s
2. lapjvx : 0.26109171s
3. scipy ⭐ : 0.34502992s
4. lapjvsa : 0.68365575s
5. lapjv : 0.72371579s
6. lapjvs : 0.79274062s
7. lapjvc : 106.41484879s
1. lapjvxa : 0.13445975s
2. lapjvx : 0.25395613s
3. scipy ⭐ : 0.37139379s
4. lapjv : 0.49666225s
5. lapjvsa : 0.51077446s
6. lapjvs : 0.54710338s
7. lapjvc : 116.42058821s
-------------------------------

@@ -647,3 +652,3 @@ ```

scipy==1.16.3
lapx @ git+https://github.com/rathaROG/lapx.git@ca0bbee8e319fe005c557d5a2bcce1148d89797c
lapx @ git+https://github.com/rathaROG/lapx.git@8e1a5c5cbe1a813d5ee80570b285e316fcc99f7a # 0.9.1
```

@@ -664,11 +669,11 @@

-----------------------------------------------------------------------------------------------------------------------
10x10 | 0.000063s 4th | 0.000057s ✓ 2nd | 0.000063s ✓ 5th | 0.000069s ✓ 6th | 0.000061s ✓ 3rd | 0.000057s ✓ 1st
25x20 | 0.000058s 4th | 0.000104s ✗ 6th | 0.000062s ✓ 5th | 0.000052s ✓ 2nd | 0.000058s ✓ 3rd | 0.000051s ✓ 1st
50x50 | 0.000083s 4th | 0.000086s ✗ 5th | 0.000068s ✓ 3rd | 0.000058s ✓ 1st | 0.000103s ✓ 6th | 0.000063s ✓ 2nd
100x150 | 0.000131s 2nd | 0.000828s ✗ 6th | 0.000132s ✓ 3rd | 0.000144s ✓ 4th | 0.000680s ✓ 5th | 0.000123s ✓ 1st
250x250 | 0.001126s 4th | 0.001218s ✓ 5th | 0.000557s ✓ 2nd | 0.000537s ✓ 1st | 0.001516s ✓ 6th | 0.000605s ✓ 3rd
550x500 | 0.003531s 4th | 0.011714s ✓ 5th | 0.001424s ✓ 2nd | 0.001358s ✓ 1st | 0.017545s ✓ 6th | 0.001511s ✓ 3rd
1000x1000 | 0.022934s 4th | 0.026359s ✓ 5th | 0.010415s ✓ 2nd | 0.010320s ✓ 1st | 0.031669s ✓ 6th | 0.012068s ✓ 3rd
2000x2500 | 0.034198s 4th | 1.627013s ✓ 6th | 0.013647s ✓ 1st | 0.015660s ✓ 2nd | 1.531048s ✓ 5th | 0.022275s ✓ 3rd
5000x5000 | 1.095034s 3rd | 2.335637s ✓ 6th | 1.082954s ✓ 2nd | 1.103870s ✓ 4th | 1.140890s ✓ 5th | 0.496765s ✓ 1st
10x10 | 0.000056s 3rd | 0.000056s ✗ 4th | 0.000061s ✓ 6th | 0.000050s ✓ 1st | 0.000060s ✓ 5th | 0.000052s ✓ 2nd
25x20 | 0.000052s 3rd | 0.000061s ✗ 5th | 0.000061s ✓ 6th | 0.000049s ✓ 1st | 0.000056s ✓ 4th | 0.000051s ✓ 2nd
50x50 | 0.000084s 4th | 0.000085s ✗ 5th | 0.000072s ✓ 2nd | 0.000063s ✓ 1st | 0.000105s ✓ 6th | 0.000073s ✓ 3rd
100x150 | 0.000148s 4th | 0.000564s ✓ 5th | 0.000135s ✓ 3rd | 0.000110s ✓ 1st | 0.000671s ✓ 6th | 0.000120s ✓ 2nd
250x250 | 0.001327s 4th | 0.001399s ✓ 5th | 0.000527s ✓ 2nd | 0.000510s ✓ 1st | 0.001417s ✓ 6th | 0.000579s ✓ 3rd
550x500 | 0.003237s 4th | 0.011715s ✓ 5th | 0.001379s ✓ 2nd | 0.001344s ✓ 1st | 0.014237s ✓ 6th | 0.001463s ✓ 3rd
1000x1000 | 0.023160s 5th | 0.020795s ✓ 4th | 0.006882s ✓ 2nd | 0.006875s ✓ 1st | 0.027876s ✓ 6th | 0.008634s ✓ 3rd
2000x2500 | 0.036176s 4th | 1.683198s ✓ 5th | 0.014039s ✓ 1st | 0.015730s ✓ 2nd | 1.683977s ✓ 6th | 0.023320s ✓ 3rd
5000x5000 | 1.116971s 4th | 1.807397s ✓ 6th | 0.811022s ✓ 2nd | 0.844260s ✓ 3rd | 1.125310s ✓ 5th | 0.421959s ✓ 1st
-----------------------------------------------------------------------------------------------------------------------

@@ -679,8 +684,8 @@

🎉 --------------------------- OVERALL RANKING --------------------------- 🎉
1. LAPX LAPJVS : 533.5186 ms | ✅ | 🥇x4 🥈x1 🥉x4
2. LAPX LAPJV : 1109.3228 ms | ✅ | 🥇x1 🥈x4 🥉x2 🏳️x2
3. LAPX LAPJVX : 1132.0674 ms | ✅ | 🥇x4 🥈x2 🚩x2 🥴x1
4. BASELINE SciPy : 1157.1577 ms | ⭐ | 🥈x1 🥉x1 🚩x7
5. LAPX LAPJVC : 2723.5708 ms | ✅ | 🥉x2 🏳️x3 🥴x4
6. LAPX LAPJV-IFT : 4003.0145 ms | ⚠️ | 🥈x1 🏳️x4 🥴x4
1. LAPX LAPJVS : 456.2515 ms | ✅ | 🥇x1 🥈x3 🥉x5
2. LAPX LAPJV : 834.1779 ms | ✅ | 🥇x1 🥈x5 🥉x1 🥴x2
3. LAPX LAPJVX : 868.9913 ms | ✅ | 🥇x7 🥈x1 🥉x1
4. BASELINE SciPy : 1181.2127 ms | ⭐ | 🥉x2 🚩x6 🏳️x1
5. LAPX LAPJVC : 2853.7103 ms | ✅ | 🚩x1 🏳️x2 🥴x6
6. LAPX LAPJV-IFT : 3525.2702 ms | ⚠️ | 🚩x2 🏳️x6 🥴x1
🎉 ------------------------------------------------------------------------- 🎉

@@ -696,11 +701,11 @@

-----------------------------------------------------------------------------------------------------------------------
10x10 | 0.000048s 2nd | 0.000055s ✗ 5th | 0.000048s ✓ 3rd | 0.000039s ✓ 1st | 0.000054s ✓ 4th | 0.000055s ✓ 6th
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50x50 | 0.000077s 4th | 0.000080s ✗ 6th | 0.000057s ✓ 3rd | 0.000048s ✓ 1st | 0.000078s ✓ 5th | 0.000051s ✓ 2nd
100x150 | 0.000112s 3rd | 0.000635s ✓ 6th | 0.000123s ✓ 4th | 0.000092s ✓ 1st | 0.000588s ✓ 5th | 0.000093s ✓ 2nd
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550x500 | 0.003480s 4th | 0.010844s ✓ 5th | 0.001426s ✓ 2nd | 0.001311s ✓ 1st | 0.016003s ✓ 6th | 0.001447s ✓ 3rd
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5000x5000 | 1.070328s 2nd | 3.315799s ✓ 6th | 1.622128s ✓ 4th | 1.628149s ✓ 5th | 1.100956s ✓ 3rd | 0.537018s ✓ 1st
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25x20 | 0.000045s 1st | 0.000064s ✗ 6th | 0.000056s ✓ 5th | 0.000048s ✓ 3rd | 0.000051s ✓ 4th | 0.000048s ✓ 2nd
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550x500 | 0.003814s 4th | 0.011264s ✓ 5th | 0.001395s ✓ 2nd | 0.001389s ✓ 1st | 0.016663s ✓ 6th | 0.001569s ✓ 3rd
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-----------------------------------------------------------------------------------------------------------------------

@@ -711,8 +716,8 @@

🎉 --------------------------- OVERALL RANKING --------------------------- 🎉
1. LAPX LAPJVS : 573.3374 ms | ✅ | 🥇x2 🥈x2 🥉x4 🥴x1
2. BASELINE SciPy : 1132.9018 ms | ⭐ | 🥈x2 🥉x2 🚩x5
3. LAPX LAPJV : 1648.4320 ms | ✅ | 🥇x1 🥈x3 🥉x2 🚩x3
4. LAPX LAPJVX : 1654.0251 ms | ✅ | 🥇x6 🥈x2 🏳️x1
5. LAPX LAPJVC : 2742.8296 ms | ✅ | 🥉x1 🚩x1 🏳️x4 🥴x3
6. LAPX LAPJV-IFT : 4919.4888 ms | ⚠️ | 🏳️x4 🥴x5
1. LAPX LAPJVS : 423.9891 ms | ✅ | 🥇x1 🥈x3 🥉x5
2. LAPX LAPJVX : 520.0521 ms | ✅ | 🥇x7 🥈x1 🥉x1
3. LAPX LAPJV : 538.5159 ms | ✅ | 🥈x5 🥉x2 🚩x1 🏳️x1
4. BASELINE SciPy : 1154.1538 ms | ⭐ | 🥇x1 🚩x5 🏳️x2 🥴x1
5. LAPX LAPJVC : 2803.8866 ms | ✅ | 🥉x1 🚩x2 🏳️x3 🥴x3
6. LAPX LAPJV-IFT : 3034.8488 ms | ⚠️ | 🚩x1 🏳️x3 🥴x5
🎉 ------------------------------------------------------------------------- 🎉

@@ -728,11 +733,11 @@

-----------------------------------------------------------------------------------------------------------------------
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5000x5000 | 0.969325s 5th | 1.507703s ✓ 6th | 0.668259s ✓ 3rd | 0.656468s ✓ 2nd | 0.954102s ✓ 4th | 0.475278s ✓ 1st
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550x500 | 0.003384s 4th | 0.011987s ✓ 5th | 0.001601s ✓ 3rd | 0.001488s ✓ 1st | 0.016294s ✓ 6th | 0.001589s ✓ 2nd
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-----------------------------------------------------------------------------------------------------------------------

@@ -743,8 +748,8 @@

🎉 --------------------------- OVERALL RANKING --------------------------- 🎉
1. LAPX LAPJVS : 511.1457 ms | ✅ | 🥇x3 🥈x2 🥉x4
2. LAPX LAPJVX : 684.5318 ms | ✅ | 🥇x4 🥈x4 🥉x1
3. LAPX LAPJV : 696.4601 ms | ✅ | 🥇x1 🥈x3 🥉x3 🚩x2
4. BASELINE SciPy : 1032.4388 ms | ⭐ | 🥇x1 🚩x6 🏳️x1 🥴x1
5. LAPX LAPJVC : 2557.4136 ms | ✅ | 🥉x1 🚩x1 🏳️x5 🥴x2
6. LAPX LAPJV-IFT : 3202.4579 ms | ✅ | 🏳️x3 🥴x6
1. LAPX LAPJVS : 572.1697 ms | ✅ | 🥇x1 🥈x4 🥉x4
2. LAPX LAPJVX : 716.9497 ms | ✅ | 🥇x5 🥈x4
3. LAPX LAPJV : 734.7961 ms | ✅ | 🥇x2 🥈x1 🥉x3 🚩x1 🏳️x2
4. BASELINE SciPy : 1153.8943 ms | ⭐ | 🥇x1 🥉x1 🚩x4 🏳️x2 🥴x1
5. LAPX LAPJVC : 2669.6871 ms | ✅ | 🥉x1 🚩x2 🏳️x3 🥴x3
6. LAPX LAPJV-IFT : 3372.3898 ms | ✅ | 🚩x2 🏳️x2 🥴x5
🎉 ------------------------------------------------------------------------- 🎉

@@ -760,11 +765,11 @@

-----------------------------------------------------------------------------------------------------------------------
10x10 | 0.000049s 6th | 0.000046s ✓ 4th | 0.000046s ✓ 5th | 0.000038s ✓ 1st | 0.000044s ✓ 3rd | 0.000041s ✓ 2nd
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50x50 | 0.000067s 4th | 0.000074s ✓ 5th | 0.000058s ✓ 3rd | 0.000053s ✓ 2nd | 0.000081s ✓ 6th | 0.000053s ✓ 1st
100x150 | 0.000117s 2nd | 0.000752s ✓ 6th | 0.000123s ✓ 3rd | 0.000126s ✓ 4th | 0.000721s ✓ 5th | 0.000098s ✓ 1st
250x250 | 0.001063s 4th | 0.001545s ✓ 6th | 0.000447s ✓ 2nd | 0.000445s ✓ 1st | 0.001303s ✓ 5th | 0.000477s ✓ 3rd
550x500 | 0.003711s 4th | 0.011309s ✓ 5th | 0.001524s ✓ 2nd | 0.001460s ✓ 1st | 0.016480s ✓ 6th | 0.001558s ✓ 3rd
1000x1000 | 0.019167s 1st | 0.053561s ✓ 6th | 0.025616s ✓ 3rd | 0.025778s ✓ 4th | 0.023447s ✓ 2nd | 0.027353s ✓ 5th
2000x2500 | 0.035676s 4th | 1.579856s ✓ 5th | 0.014502s ✓ 2nd | 0.014438s ✓ 1st | 1.699035s ✓ 6th | 0.023144s ✓ 3rd
5000x5000 | 1.214213s 5th | 1.230595s ✓ 6th | 0.511229s ✓ 2nd | 0.514490s ✓ 3rd | 1.144982s ✓ 4th | 0.452692s ✓ 1st
10x10 | 0.000040s 3rd | 0.000046s ✓ 6th | 0.000045s ✓ 5th | 0.000037s ✓ 1st | 0.000042s ✓ 4th | 0.000040s ✓ 2nd
25x20 | 0.000042s 1st | 0.000117s ✓ 6th | 0.000058s ✓ 4th | 0.000062s ✓ 5th | 0.000056s ✓ 3rd | 0.000051s ✓ 2nd
50x50 | 0.000080s 4th | 0.000084s ✓ 5th | 0.000062s ✓ 3rd | 0.000055s ✓ 1st | 0.000096s ✓ 6th | 0.000060s ✓ 2nd
100x150 | 0.000142s 4th | 0.000650s ✓ 6th | 0.000131s ✓ 3rd | 0.000104s ✓ 1st | 0.000586s ✓ 5th | 0.000106s ✓ 2nd
250x250 | 0.001134s 5th | 0.001073s ✓ 4th | 0.000330s ✓ 2nd | 0.000317s ✓ 1st | 0.001307s ✓ 6th | 0.000389s ✓ 3rd
550x500 | 0.003360s 4th | 0.010832s ✓ 5th | 0.001444s ✓ 2nd | 0.001410s ✓ 1st | 0.015605s ✓ 6th | 0.001537s ✓ 3rd
1000x1000 | 0.020469s 4th | 0.023088s ✓ 5th | 0.008001s ✓ 1st | 0.008134s ✓ 2nd | 0.024824s ✓ 6th | 0.010342s ✓ 3rd
2000x2500 | 0.038874s 4th | 1.661149s ✓ 6th | 0.014831s ✓ 1st | 0.016389s ✓ 2nd | 1.640910s ✓ 5th | 0.022910s ✓ 3rd
5000x5000 | 0.984126s 5th | 1.032316s ✓ 6th | 0.393411s ✓ 2nd | 0.380302s ✓ 1st | 0.949218s ✓ 4th | 0.401329s ✓ 3rd
-----------------------------------------------------------------------------------------------------------------------

@@ -775,8 +780,8 @@

🎉 --------------------------- OVERALL RANKING --------------------------- 🎉
1. LAPX LAPJVS : 505.4603 ms | ✅ | 🥇x3 🥈x1 🥉x4 🏳️x1
2. LAPX LAPJV : 553.5970 ms | ✅ | 🥈x4 🥉x3 🏳️x2
3. LAPX LAPJVX : 556.8710 ms | ✅ | 🥇x4 🥈x2 🥉x1 🚩x2
4. BASELINE SciPy : 1274.1026 ms | ⭐ | 🥇x2 🥈x1 🚩x4 🏳️x1 🥴x1
5. LAPX LAPJV-IFT : 2877.7913 ms | ✅ | 🚩x1 🏳️x3 🥴x5
6. LAPX LAPJVC : 2886.1434 ms | ✅ | 🥈x1 🥉x1 🚩x2 🏳️x2 🥴x3
1. LAPX LAPJVX : 406.8110 ms | ✅ | 🥇x6 🥈x2 🏳️x1
2. LAPX LAPJV : 418.3143 ms | ✅ | 🥇x2 🥈x3 🥉x2 🚩x1 🏳️x1
3. LAPX LAPJVS : 436.7637 ms | ✅ | 🥈x4 🥉x5
4. BASELINE SciPy : 1048.2659 ms | ⭐ | 🥇x1 🥉x1 🚩x5 🏳️x2
5. LAPX LAPJVC : 2632.6435 ms | ✅ | 🥉x1 🚩x2 🏳️x2 🥴x4
6. LAPX LAPJV-IFT : 2729.3534 ms | ✅ | 🚩x1 🏳️x3 🥴x5
🎉 ------------------------------------------------------------------------- 🎉

@@ -792,11 +797,11 @@

-----------------------------------------------------------------------------------------------------------------------
10x10 | 0.000055s 6th | 0.000048s ✓ 5th | 0.000046s ✓ 4th | 0.000037s ✓ 1st | 0.000043s ✓ 3rd | 0.000040s ✓ 2nd
25x20 | 0.000043s 1st | 0.000059s ✓ 6th | 0.000053s ✓ 4th | 0.000045s ✓ 2nd | 0.000055s ✓ 5th | 0.000046s ✓ 3rd
50x50 | 0.000074s 4th | 0.000080s ✓ 5th | 0.000063s ✓ 3rd | 0.000054s ✓ 1st | 0.000088s ✓ 6th | 0.000058s ✓ 2nd
100x150 | 0.000146s 4th | 0.000647s ✓ 5th | 0.000107s ✓ 3rd | 0.000095s ✓ 1st | 0.000714s ✓ 6th | 0.000103s ✓ 2nd
250x250 | 0.000964s 4th | 0.001495s ✓ 6th | 0.000565s ✓ 1st | 0.000603s ✓ 2nd | 0.001220s ✓ 5th | 0.000636s ✓ 3rd
550x500 | 0.003138s 4th | 0.010879s ✓ 5th | 0.001294s ✓ 1st | 0.001329s ✓ 2nd | 0.016092s ✓ 6th | 0.001405s ✓ 3rd
1000x1000 | 0.020857s 3rd | 0.042133s ✓ 6th | 0.019502s ✓ 2nd | 0.019448s ✓ 1st | 0.023370s ✓ 5th | 0.021119s ✓ 4th
2000x2500 | 0.032293s 4th | 1.575432s ✓ 6th | 0.014037s ✓ 1st | 0.014037s ✓ 2nd | 1.482075s ✓ 5th | 0.022823s ✓ 3rd
5000x5000 | 0.974974s 4th | 1.340142s ✓ 6th | 0.564158s ✓ 2nd | 0.570803s ✓ 3rd | 1.116583s ✓ 5th | 0.442339s ✓ 1st
10x10 | 0.000051s 6th | 0.000045s ✓ 4th | 0.000049s ✓ 5th | 0.000037s ✓ 1st | 0.000042s ✓ 3rd | 0.000038s ✓ 2nd
25x20 | 0.000044s 1st | 0.000057s ✓ 6th | 0.000055s ✓ 4th | 0.000045s ✓ 2nd | 0.000056s ✓ 5th | 0.000046s ✓ 3rd
50x50 | 0.000068s 4th | 0.000076s ✓ 5th | 0.000064s ✓ 3rd | 0.000055s ✓ 1st | 0.000089s ✓ 6th | 0.000059s ✓ 2nd
100x150 | 0.000152s 4th | 0.000662s ✓ 5th | 0.000140s ✓ 3rd | 0.000107s ✓ 1st | 0.000692s ✓ 6th | 0.000112s ✓ 2nd
250x250 | 0.001222s 4th | 0.001813s ✓ 6th | 0.000859s ✓ 2nd | 0.000820s ✓ 1st | 0.001422s ✓ 5th | 0.000873s ✓ 3rd
550x500 | 0.003388s 4th | 0.010608s ✓ 5th | 0.001394s ✓ 2nd | 0.001381s ✓ 1st | 0.014955s ✓ 6th | 0.001531s ✓ 3rd
1000x1000 | 0.023853s 4th | 0.036422s ✓ 6th | 0.016408s ✓ 2nd | 0.015504s ✓ 1st | 0.029056s ✓ 5th | 0.017412s ✓ 3rd
2000x2500 | 0.033767s 4th | 1.643829s ✓ 6th | 0.014560s ✓ 2nd | 0.014478s ✓ 1st | 1.421325s ✓ 5th | 0.022940s ✓ 3rd
5000x5000 | 1.026469s 4th | 2.122379s ✓ 6th | 0.946314s ✓ 2nd | 0.947657s ✓ 3rd | 1.053784s ✓ 5th | 0.458383s ✓ 1st
-----------------------------------------------------------------------------------------------------------------------

@@ -807,8 +812,8 @@

🎉 --------------------------- OVERALL RANKING --------------------------- 🎉
1. LAPX LAPJVS : 488.5671 ms | ✅ | 🥇x1 🥈x3 🥉x4 🚩x1
2. LAPX LAPJV : 599.8239 ms | ✅ | 🥇x3 🥈x2 🥉x2 🚩x2
3. LAPX LAPJVX : 606.4511 ms | ✅ | 🥇x4 🥈x4 🥉x1
4. BASELINE SciPy : 1032.5424 ms | ⭐ | 🥇x1 🥉x1 🚩x6 🥴x1
5. LAPX LAPJVC : 2640.2397 ms | ✅ | 🥉x1 🏳️x5 🥴x3
6. LAPX LAPJV-IFT : 2970.9133 ms | ✅ | 🏳️x4 🥴x5
1. LAPX LAPJVS : 501.3927 ms | ✅ | 🥇x1 🥈x3 🥉x5
2. LAPX LAPJV : 979.8427 ms | ✅ | 🥈x5 🥉x2 🚩x1 🏳️x1
3. LAPX LAPJVX : 980.0842 ms | ✅ | 🥇x7 🥈x1 🥉x1
4. BASELINE SciPy : 1089.0140 ms | ⭐ | 🥇x1 🚩x7 🥴x1
5. LAPX LAPJVC : 2521.4207 ms | ✅ | 🥉x1 🏳️x5 🥴x3
6. LAPX LAPJV-IFT : 3815.8903 ms | ✅ | 🚩x1 🏳️x3 🥴x5
🎉 ------------------------------------------------------------------------- 🎉

@@ -815,0 +820,0 @@ ```

+2
-1
lap/__init__.py

@@ -110,3 +110,3 @@ # Copyright (c) 2025 Ratha SIV | MIT License

__version__ = '0.9.0'
__version__ = '0.9.1'
__author__ = 'Ratha SIV'

@@ -123,1 +123,2 @@ __description__ = 'Linear assignment problem solvers, including single and batch solvers.'

]
+15
-5
lapx.egg-info/PKG-INFO
Metadata-Version: 2.4
Name: lapx
Version: 0.9.0
Version: 0.9.1
Summary: Linear assignment problem solvers, including single and batch solvers.

@@ -67,5 +67,5 @@ Home-page: https://github.com/rathaROG/lapx

[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg)](https://github.com/rathaROG/lapx/releases)
[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg?v0.9.1)](https://github.com/rathaROG/lapx/releases)
[![Platforms](https://img.shields.io/badge/platform-windows%20%7C%20linux%20%7C%20macos-gold)](https://pypi.org/project/lapx/#files)
[![Python Versions](https://img.shields.io/pypi/pyversions/lapx.svg)](https://pypi.org/project/lapx/)
[![Python Versions](https://img.shields.io/pypi/pyversions/lapx.svg?v0.9.1)](https://pypi.org/project/lapx/)

@@ -99,3 +99,3 @@ [![Benchmark (Single)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml/badge.svg)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml)

[![Wheels](https://img.shields.io/pypi/wheel/lapx)](https://pypi.org/project/lapx/)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v0.8.1)](https://badge.fury.io/py/lapx)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v0.9.1)](https://badge.fury.io/py/lapx)
[![Downloads](https://static.pepy.tech/badge/lapx)](https://pepy.tech/project/lapx)

@@ -131,2 +131,12 @@ [![Downloads](https://static.pepy.tech/badge/lapx/month)](https://pepy.tech/project/lapx)

<details><summary>⚡ Extra performance</summary><br>
Since [v0.9.1](https://github.com/rathaROG/lapx/releases/tag/v0.9.1), `lapx` enables safe optimizations by default. For source build, you can opt into extra flags via environment variables which might boost the performance further:
- `LAPX_FASTMATH=1` — enable fast-math (may change floating‑point semantics)
- `LAPX_NATIVE=1` — GCC/Clang only; tune for the CPU of build machine (not suitable for sharing)
- `LAPX_LTO=0` — disable link-time optimization if link time/memory is an issue
See the [setup.py](https://github.com/rathaROG/lapx/blob/main/setup.py) for details.
</details>
## 🧪 Usage

@@ -232,3 +242,3 @@

`lapjvs()` is an enhanced version of Vadim Markovtsev's [`lapjv`](https://github.com/src-d/lapjv). While `lapjvs()` does not use CPU special instruction sets like the original implementation, it still delivers comparable performance. It natively supports both square and rectangular cost matrices and can produce output either in SciPy's [`linear_sum_assignment`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) style or `(x, y)` mappings. See the [docstring here](https://github.com/rathaROG/lapx/blob/main/lap/lapjvs.py) for more details.
`lapjvs()` is an enhanced version of Vadim Markovtsev's [`lapjv`](https://github.com/src-d/lapjv). While `lapjvs()` does not use CPU special instruction sets like the original implementation, it still delivers comparable performance. It natively supports both square and rectangular cost matrices and can produce output either in SciPy's [`linear_sum_assignment`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) style or `(x, y)` mappings. See the [docstring here](https://github.com/rathaROG/lapx/blob/main/lap/_lapjvs_wp.py) for more details.

@@ -235,0 +245,0 @@ ```python

+15
-5
PKG-INFO
Metadata-Version: 2.4
Name: lapx
Version: 0.9.0
Version: 0.9.1
Summary: Linear assignment problem solvers, including single and batch solvers.

@@ -67,5 +67,5 @@ Home-page: https://github.com/rathaROG/lapx

[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg)](https://github.com/rathaROG/lapx/releases)
[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg?v0.9.1)](https://github.com/rathaROG/lapx/releases)
[![Platforms](https://img.shields.io/badge/platform-windows%20%7C%20linux%20%7C%20macos-gold)](https://pypi.org/project/lapx/#files)
[![Python Versions](https://img.shields.io/pypi/pyversions/lapx.svg)](https://pypi.org/project/lapx/)
[![Python Versions](https://img.shields.io/pypi/pyversions/lapx.svg?v0.9.1)](https://pypi.org/project/lapx/)

@@ -99,3 +99,3 @@ [![Benchmark (Single)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml/badge.svg)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml)

[![Wheels](https://img.shields.io/pypi/wheel/lapx)](https://pypi.org/project/lapx/)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v0.8.1)](https://badge.fury.io/py/lapx)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v0.9.1)](https://badge.fury.io/py/lapx)
[![Downloads](https://static.pepy.tech/badge/lapx)](https://pepy.tech/project/lapx)

@@ -131,2 +131,12 @@ [![Downloads](https://static.pepy.tech/badge/lapx/month)](https://pepy.tech/project/lapx)

<details><summary>⚡ Extra performance</summary><br>
Since [v0.9.1](https://github.com/rathaROG/lapx/releases/tag/v0.9.1), `lapx` enables safe optimizations by default. For source build, you can opt into extra flags via environment variables which might boost the performance further:
- `LAPX_FASTMATH=1` — enable fast-math (may change floating‑point semantics)
- `LAPX_NATIVE=1` — GCC/Clang only; tune for the CPU of build machine (not suitable for sharing)
- `LAPX_LTO=0` — disable link-time optimization if link time/memory is an issue
See the [setup.py](https://github.com/rathaROG/lapx/blob/main/setup.py) for details.
</details>
## 🧪 Usage

@@ -232,3 +242,3 @@

`lapjvs()` is an enhanced version of Vadim Markovtsev's [`lapjv`](https://github.com/src-d/lapjv). While `lapjvs()` does not use CPU special instruction sets like the original implementation, it still delivers comparable performance. It natively supports both square and rectangular cost matrices and can produce output either in SciPy's [`linear_sum_assignment`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) style or `(x, y)` mappings. See the [docstring here](https://github.com/rathaROG/lapx/blob/main/lap/lapjvs.py) for more details.
`lapjvs()` is an enhanced version of Vadim Markovtsev's [`lapjv`](https://github.com/src-d/lapjv). While `lapjvs()` does not use CPU special instruction sets like the original implementation, it still delivers comparable performance. It natively supports both square and rectangular cost matrices and can produce output either in SciPy's [`linear_sum_assignment`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) style or `(x, y)` mappings. See the [docstring here](https://github.com/rathaROG/lapx/blob/main/lap/_lapjvs_wp.py) for more details.

@@ -235,0 +245,0 @@ ```python

+14
-4
README.md

@@ -16,5 +16,5 @@ <details><summary>🆕 What's new</summary><hr>

[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg)](https://github.com/rathaROG/lapx/releases)
[![GitHub release](https://img.shields.io/github/release/rathaROG/lapx.svg?v0.9.1)](https://github.com/rathaROG/lapx/releases)
[![Platforms](https://img.shields.io/badge/platform-windows%20%7C%20linux%20%7C%20macos-gold)](https://pypi.org/project/lapx/#files)
[![Python Versions](https://img.shields.io/pypi/pyversions/lapx.svg)](https://pypi.org/project/lapx/)
[![Python Versions](https://img.shields.io/pypi/pyversions/lapx.svg?v0.9.1)](https://pypi.org/project/lapx/)

@@ -48,3 +48,3 @@ [![Benchmark (Single)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml/badge.svg)](https://github.com/rathaROG/lapx/actions/workflows/benchmark_single.yaml)

[![Wheels](https://img.shields.io/pypi/wheel/lapx)](https://pypi.org/project/lapx/)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v0.8.1)](https://badge.fury.io/py/lapx)
[![PyPI version](https://badge.fury.io/py/lapx.svg?v0.9.1)](https://badge.fury.io/py/lapx)
[![Downloads](https://static.pepy.tech/badge/lapx)](https://pepy.tech/project/lapx)

@@ -80,2 +80,12 @@ [![Downloads](https://static.pepy.tech/badge/lapx/month)](https://pepy.tech/project/lapx)

<details><summary>⚡ Extra performance</summary><br>
Since [v0.9.1](https://github.com/rathaROG/lapx/releases/tag/v0.9.1), `lapx` enables safe optimizations by default. For source build, you can opt into extra flags via environment variables which might boost the performance further:
- `LAPX_FASTMATH=1` — enable fast-math (may change floating‑point semantics)
- `LAPX_NATIVE=1` — GCC/Clang only; tune for the CPU of build machine (not suitable for sharing)
- `LAPX_LTO=0` — disable link-time optimization if link time/memory is an issue
See the [setup.py](https://github.com/rathaROG/lapx/blob/main/setup.py) for details.
</details>
## 🧪 Usage

@@ -181,3 +191,3 @@

`lapjvs()` is an enhanced version of Vadim Markovtsev's [`lapjv`](https://github.com/src-d/lapjv). While `lapjvs()` does not use CPU special instruction sets like the original implementation, it still delivers comparable performance. It natively supports both square and rectangular cost matrices and can produce output either in SciPy's [`linear_sum_assignment`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) style or `(x, y)` mappings. See the [docstring here](https://github.com/rathaROG/lapx/blob/main/lap/lapjvs.py) for more details.
`lapjvs()` is an enhanced version of Vadim Markovtsev's [`lapjv`](https://github.com/src-d/lapjv). While `lapjvs()` does not use CPU special instruction sets like the original implementation, it still delivers comparable performance. It natively supports both square and rectangular cost matrices and can produce output either in SciPy's [`linear_sum_assignment`](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.linear_sum_assignment.html) style or `(x, y)` mappings. See the [docstring here](https://github.com/rathaROG/lapx/blob/main/lap/_lapjvs_wp.py) for more details.

@@ -184,0 +194,0 @@ ```python

+102
-1
setup.py
# Copyright (c) 2025 Ratha SIV | MIT License
from setuptools import Extension, setup, find_packages
from setuptools.command.build_ext import build_ext # custom build_ext for high-perf flags

@@ -28,2 +29,78 @@ LICENSE = "MIT"

class BuildExt(build_ext):
"""
Add portable, high-performance compiler/linker flags and allow
optional opt-ins via env vars:
- LAPX_FASTMATH=1 -> -ffast-math (or /fp:fast)
- LAPX_NATIVE=1 -> -march=native -mtune=native
- LAPX_LTO=0 -> disable LTO if needed
"""
def has_flag(self, flag):
import tempfile, os
with tempfile.NamedTemporaryFile('w', suffix='.cpp', delete=False) as f:
f.write("int main(){return 0;}")
fname = f.name
try:
self.compiler.compile([fname], extra_postargs=[flag])
except Exception:
try: os.remove(fname)
except OSError: pass
return False
try: os.remove(fname)
except OSError: pass
return True
def build_extensions(self):
import os, sys
ctype = self.compiler.compiler_type
is_msvc = (ctype == 'msvc')
compile_opts = []
link_opts = []
if is_msvc:
compile_opts += ['/O2', '/DNDEBUG']
# Link-time optimization (LTO)
if self.has_flag('/GL'):
compile_opts += ['/GL']
link_opts += ['/LTCG']
# Optional fast-math (opt-in)
if os.environ.get('LAPX_FASTMATH') == '1':
compile_opts += ['/fp:fast']
else:
compile_opts += ['-O3', '-DNDEBUG']
if sys.version_info >= (3, 9) and self.has_flag('-fvisibility=hidden'):
compile_opts += ['-fvisibility=hidden']
if self.has_flag('-fno-math-errno'):
compile_opts += ['-fno-math-errno']
# Link-time optimization (prefer ThinLTO when available)
if os.environ.get('LAPX_LTO', '1') == '1':
if self.has_flag('-flto=thin'):
compile_opts += ['-flto=thin']
link_opts += ['-flto=thin']
elif self.has_flag('-flto'):
compile_opts += ['-flto']
link_opts += ['-flto']
# Optional fast-math (opt-in)
if os.environ.get('LAPX_FASTMATH') == '1' and self.has_flag('-ffast-math'):
compile_opts += ['-ffast-math']
# Optional native tuning (opt-in; avoid for portable wheels)
if os.environ.get('LAPX_NATIVE') == '1':
if self.has_flag('-march=native'):
compile_opts += ['-march=native']
if self.has_flag('-mtune=native'):
compile_opts += ['-mtune=native']
# Minor call overhead reduction on Linux/glibc (if supported)
if sys.platform.startswith('linux') and self.has_flag('-fno-plt'):
compile_opts += ['-fno-plt']
# Apply to all extensions
for ext in self.extensions:
prev_cargs = list(getattr(ext, 'extra_compile_args', []) or [])
prev_largs = list(getattr(ext, 'extra_link_args', []) or [])
ext.extra_compile_args = prev_cargs + compile_opts
ext.extra_link_args = prev_largs + link_opts
super().build_extensions()
def main_setup():

@@ -96,4 +173,17 @@ import os

# Safe, high-performance Cython directives
cython_directives = dict(
language_level=3,
boundscheck=False,
wraparound=False,
nonecheck=False,
initializedcheck=False,
cdivision=True,
infer_types=True,
profile=False,
linetrace=False,
)
# Merge all extensions
ext_modules = cythonize([ext_jv, ext_jvx]) + [ext_jvc, ext_jvs]
ext_modules = cythonize([ext_jv, ext_jvx], compiler_directives=cython_directives) + [ext_jvc, ext_jvs]

@@ -144,2 +234,3 @@ setup(

ext_modules=ext_modules,
cmdclass={'build_ext': BuildExt},
)

@@ -155,3 +246,13 @@

>>> python -m build --wheel
Base optimizations are applied automatically (e.g., optimized build
[/O2 on MSVC or -O3 on GCC/Clang], -DNDEBUG, and LTO when supported).
Extra opt-ins can be enabled via environment variables:
- LAPX_FASTMATH=1 -> enables fast-math (/fp:fast on MSVC, -ffast-math on GCC/Clang)
- LAPX_NATIVE=1 -> enables -march=native -mtune=native (GCC/Clang only)
- LAPX_LTO=0 -> disables LTO if needed
Note: Cython compiler directives (boundscheck=False, wraparound=False, cdivision=True, etc.)
are enabled by default for Cython modules.
"""
main_setup()
+24
-10
src/_lapjvs/lapjvs.cpp

@@ -110,3 +110,3 @@ #include <functional>

if (typ != NPY_FLOAT32 && typ != NPY_FLOAT64) {
PyErr_SetString(PyExc_TypeError, "\"cost_matrix\" must be float32 or float64 for lapjvs_native()");
PyErr_SetString(PyExc_TypeError, "\"cost_matrix\" must be float32 or float64");
return NULL;

@@ -125,7 +125,14 @@ }

int dim = static_cast<int>(dims[0]);
if (dim <= 0) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is invalid or too large");
if (dim < 0) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is too large or invalid");
return NULL;
}
if (dim == 0) {
npy_intp ret_dims[] = {0};
pyarray row_ind_array(PyArray_SimpleNew(1, ret_dims, NPY_INT));
pyarray col_ind_array(PyArray_SimpleNew(1, ret_dims, NPY_INT));
return Py_BuildValue("(OO)", row_ind_array.get(), col_ind_array.get());
}
auto cost_matrix = PyArray_DATA(cost_matrix_array.get());

@@ -179,7 +186,14 @@

int dim = static_cast<int>(dims[0]);
if (dim <= 0) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is invalid or too large");
if (dim < 0) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is too large or invalid");
return NULL;
}
if (dim == 0) {
npy_intp ret_dims[] = {0};
pyarray row_ind_array(PyArray_SimpleNew(1, ret_dims, NPY_INT));
pyarray col_ind_array(PyArray_SimpleNew(1, ret_dims, NPY_INT));
return Py_BuildValue("(OO)", row_ind_array.get(), col_ind_array.get());
}
auto cost_matrix = PyArray_DATA(cost_matrix_array.get());

@@ -217,3 +231,3 @@

if (typ != NPY_FLOAT32 && typ != NPY_FLOAT64) {
PyErr_SetString(PyExc_TypeError, "\"cost_matrix\" must be float32 or float64 for lapjvsa()");
PyErr_SetString(PyExc_TypeError, "\"cost_matrix\" must be float32 or float64");
return NULL;

@@ -233,3 +247,3 @@ }

if (dim < 0) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is invalid");
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is too large or invalid");
return NULL;

@@ -296,3 +310,3 @@ }

if (!cost_matrix_array) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\" must be convertible to float32 for lapjvsa_float32()");
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\" must be convertible to float32");
return NULL;

@@ -312,3 +326,3 @@ }

if (dim < 0) {
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is invalid");
PyErr_SetString(PyExc_ValueError, "\"cost_matrix\"'s shape is too large or invalid");
return NULL;

@@ -353,2 +367,2 @@ }

return reinterpret_cast<PyObject*>(pairs.release());
}
}
src/_lapjv/_lapjv.cpp

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src/_lapjv/_lapjvx.cpp

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