Main

related bits

0

processing priority

3

site type

0 (generic, awaiting analysis)

review version

11

html import

20 (imported)

Events

first seen date

2025-01-22 12:40:39

expired found date

-

created at

2025-01-22 12:40:37

updated at

2026-01-15 09:31:58

Domain name statistics

length

16

crc

64324

tld

86

nm parts

0

nm random digits

0

nm rare letters

0

Connections

is subdomain of id

87719371 (github.io)

previous id

0

replaced with id

0

related id

-

dns primary id

0

dns alternative id

0

lifecycle status

0 (unclassified, or currently active)

Subdomains and pages

deleted subdomains

0

page imported products

0

page imported random

0

page imported parking

0

Error counters

count skipped due to recent timeouts on the same server IP

0

count content received but rejected due to 11-799

0

count dns errors

0

count cert errors

0

count timeouts

0

count http 429

0

count http 404

0

count http 403

0

count http 5xx

0

next operation date

-

Server

server bits

server ip

-

Mainpage statistics

mp import status

20

mp rejected date

-

mp saved date

-

mp size orig

163994

mp size raw text

75804

mp inner links count

13

mp inner links status

10 (links queued, awaiting import)

Open Graph

title

Spaghetti Optimization

description

Two years ago, I started to study Computational Optimal Transport (OT), and, now, it is time to wrap up informally the main ideas by using an …

image

site name

author

Stefano Gualandi

updated

2026-03-09 00:11:35

raw text

Spaghetti Optimization Spaghetti Optimization My cookbook about Math, Algorithms, and Programming RSS Blog Archives Credits An Informal and Biased Tutorial on Kantorovich-Wasserstein Distances Dec 31 st , 2018 | Comments Two years ago, I started to study Computational Optimal Transport (OT) , and, now, it is time to wrap up informally the main ideas by using an Operations Research (OR) perspective with a Machine Learning (ML) motivation. Yes, an OR perspective. Why an OR perspective? Well, because most of the current theoretical works on Optimal Transport have a strong functional analysis bias, and, hence, the are pretty far to be an “easy reading” for anyone working on a different research area. Since I’m more comfortable with “summations” than with “integrations”, in this post I focus only on Discrete Optimal Transport and on Kantorovich-Wasserstein distances between a pair of discrete measures. Why an ML motivation? Because measuring ...

Text analysis

redirect type

0 (-)

block type

0 (no issues)

detected language

1 (English)

category id

Zastosowania AI (149)

index version

1

spam phrases

1

Text statistics

text nonlatin

0

text cyrillic

0

text characters

55855

text words

11901

text unique words

2191

text lines

2024

text sentences

567

text paragraphs

178

text words per sentence

20

text matched phrases

0

text matched dictionaries

0

RSS

rss path

rss status

1 (priority 1 already searched, no matches found)

rss found date

-

rss size orig

0

rss items

0

rss spam phrases

0

rss detected language

0 (awaiting analysis)

inbefore feed id

-

inbefore status

0 (new)

Sitemap

sitemap status

40 (completed successful import of reports.txt file to table in_pages)

sitemap review version

2

sitemap urls count

18

sitemap urls adult

0

sitemap filtered products

0

sitemap filtered videos

0

sitemap found date

2025-01-22 12:40:39

sitemap process date

2025-02-16 01:28:27

sitemap first import date

-

sitemap last import date

2026-01-15 09:31:58