BranchesTags

Repository files navigation

Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal distributed algorithms has increased. The benefit from distributed computations is clear, as we multiply the computing powers, thus reducing the computation time, and allow processing of data containing an extreme number of features. But there is a significant overhead caused by the network communication. New optimization methods and algorithms are to solve the problem introduced by the communication expenses.

The goal

This work aims to implement and compare some of the most popular distributed convex optimization algorithms:

  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc

in solving the problem of multi-label classification on fashion MNIST.

The results

You can read the detailed report in docs/report.pdf

Running benchmark

To run the benchmark:

  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py

Contributors

Sources

About

No description, website, or topics provided.

Resources

Readme
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Communication-efficient distributed optimization

With the growing number of distributed computations, the need for optimal
distributed algorithms has increased. The benefit from distributed computations
is clear, as we multiply the computing powers, thus reducing the computation
time, and allow processing of data containing an extreme number of features.
But there is a significant overhead caused by the network communication.
New optimization methods and algorithms are to solve the problem introduced by
the communication expenses.
The goal
This work aims to implement and compare some of the most popular distributed
convex optimization algorithms:
  • ADMM (centralized),
  • DANE (centralized),
  • Network-DANE (decentralized),
  • Network-SARAH (decentralized),
  • etc
in solving the problem of multi-label classification on
fashion MNIST.
The results
You can read the detailed report in docs/report.pdf
Running benchmark
To run the benchmark:
  1. Create a virtual environment:
virtualenv .venv
source .venv/bin/activate
  1. Install requirements using pip
pip3 install -r requirements.txt
  1. Run the main script
python3 main.py
Contributors
Sources
About
No description, website, or topics provided.
Resources
Readme
Activity
Stars
2 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages