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Optimization Course Information

These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.

The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.

Course Outline

  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers

Audience

  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.

Technical Requirements

The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.

To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.

Credits

These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.

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(function(){
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var __m = "github.com";
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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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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");
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})();
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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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, '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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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, '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" + '

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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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, '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");
}
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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, '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('^' + ".*" + '

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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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, '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); }
})();
})();

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Optimization Course Information
These Mathematical Optimization Notebooks complement the presentations in Stanford Intelligent Systems Laboratory's Mathematical Optimization Curriuculum. There is a notebook for every lesson in which the Julia language is used. If you would like to make answers unavailable to your students, you can remove answers.jl from the set of files available to them.
The first three units are non-Calculus, requiring only a knowledge of Algebra; the last two units require completion of Calculus AB. All of the units make use of the Julia programming language to teach students how to apply basic coding techniques to solve complex and relevant mathematical problems.
Course Outline
  1. Introductions and Skills
  • Optimization, vectors, iteration and recursion, foundational programming skills
  1. Non-calculus methods without constraints
  • Methods in two dimensions using computers; extension to methods in three or more dimensions
  1. Non-calculus methods with constraints
  • Linear programming
  1. Calculus methods without constraints
  • Newton's method and review of derivative meaning; derivatives in 3D and above with implications for optimization
  1. Calculus methods with constraints
  • Penalty functions; overview of other methods; Lagrange multipliers
Audience
  • First three units: math content around Algebra 1 level, analytical skills approaching Calculus. Students at the Pre-Calculus level should feel comfortable. Skilled, motivated students in Algebra 1 can certainly give it a shot.
  • Last two units: Calculus required – know how to take derivatives and be familiar with their implications for finding maxima and minima.
  • Computer programming skills will be taught from the ground up. Previous experience is not necessary.
Technical Requirements
The notebooks run in the IJulia environment. You will need several Julia packages: Revealables, Interact, Reactive, Gadfly, and Calculus.
To use the notebooks, clone them (and their associated files) from this repository and open them in IJulia.
Credits
These notebooks began as curriculum developed by Julia Roberts, a mathematics teacher at Cupertino High School in San Jose. The curriculum was modified and adapted into notebooks by Renee Trochet, a mathematics teacher at Eastside College Prep in East Palo Alto. This series of lessons was created with support from Professor Mykel Kochenderfer at Stanford University, under a grant from the National Science Foundation through the IISME (Industry Initiatives for Science and Math Education) program.
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