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<title>EE617: Linear and Convex Optimization (Fall 2017)</title>
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<div class="menu-category">Yuanzhang Xiao</div>
<div class="menu-item"><a href="index.html">Home</a></div>
<div class="menu-category">Research</div>
<div class="menu-item"><a href="research.html">Research</a></div>
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<div class="menu-item"><a href="convex-optimization-fall2017.html">EE617</a></div>
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<h1>EE617: Linear and Convex Optimization (Fall 2017)</h1>
<div id="subtitle"><a href="http://yuanzhangxiao.com">Yuanzhang Xiao</a>, <a href="https://manoa.hawaii.edu/">University of Hawaii at Manoa</a></div>
</div>
<p><b>Instructor:</b> <a href="http://yuanzhangxiao.com">Yuanzhang Xiao</a>, <a href="mailto:[email protected]">[email protected]</a></p>
<p><b>Lectures:</b> Monday and Wednesday 3:00pm - 4:15pm, Holmes Hall 242</p>
<p><b>Office Hours:</b> Tuesday and Thursday 3:00pm - 4:15pm (or by appointment), POST Building 201G</p>
<p><b>Text Book:</b> <a href="https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf">Convex Optimization by Stephen Boyd and Lieven Vandenberghe</a></p>
<p><b>Software:</b> <a href="http://cvxr.com/cvx/">CVX in Matlab</a> or <a href="http://www.cvxpy.org/en/latest/">CVXPY in Python</a>
</p>
<p><b>Grading Policy:</b> </p>
<ul>
<li><p>5 homework assignments (50%)</p>
</li>
<li><p>mid-term exam (20%, open book)</p>
</li>
<li><p>final project (30%)</p>
</li>
</ul>
<h2>Schedule, Lecture Notes, and Reading</h2>
<p>The schedule is subject to adjustment.</p>
<p><b>Theory (Aug. 21 - Sep. 20, 5 weeks)</b></p>
<ol>
<li><p>Introduction and Motivation (<a href="./Convex-Optimization-Fall-2017/1-Introduction.pdf">Lecture Notes</a>, Read: Chapter 1)</p>
</li>
<li><p>Convex Sets (<a href="./Convex-Optimization-Fall-2017/2-Convex-Sets.pdf">Lecture Notes</a>, Read: Chapter 2.1-2.3, 2.5)</p>
</li>
<li><p>Convex Functions (<a href="./Convex-Optimization-Fall-2017/3-Convex-Functions.pdf">Lecture Notes</a>, Read: Chapter 3.1, 3.2, 3.4)</p>
</li>
<li><p>Convex Optimization Problems (<a href="./Convex-Optimization-Fall-2017/4-Convex-Optimization-Problems.pdf">Lecture Notes</a>, Read: Chapter 4.1-4.5)</p>
</li>
<li><p>Optimality Condition and Duality (<a href="./Convex-Optimization-Fall-2017/5-Duality.pdf">Lecture Notes</a>, Read: Chapter 5.1-5.8)</p>
</li>
</ol>
<p><b>Applications (Sep. 25 - Oct. 18, 4 weeks)</b></p>
<ol>
<li><p>Applications in Machine Learning (<a href="./Convex-Optimization-Fall-2017/6-Applications-in-Machine-Learning.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Applications in Signal Processing (<a href="./Convex-Optimization-Fall-2017/7-Applications-in-Signal-Processing.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Applications in Wireless Communications (<a href="./Convex-Optimization-Fall-2017/8-Applications-in-Wireless-Communications.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Applications in Smart Grids (<a href="./Convex-Optimization-Fall-2017/9-Applications-in-Smart-Grids.pdf">Lecture Notes</a>)</p>
</li>
</ol>
<p><b>Review Session For Mid-Term Exam (Oct. 23)</b></p>
<p><b>Mid-Term Exam (Oct. 25) (<a href="./Convex-Optimization-Fall-2017/Mid-Term.pdf">Exam</a> and <a href="./Convex-Optimization-Fall-2017/Solution-Mid-Term.pdf">Solution</a>)</b></p>
<p><b>Algorithm (Oct. 30 - Nov. 22, 4 weeks)</b></p>
<ol>
<li><p>CVX in Matlab and CVXPY in Python (<a href="./Convex-Optimization-Fall-2017/10-Disciplined-Convex-Programming-CVX.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Unconstrained Minimization (<a href="./Convex-Optimization-Fall-2017/11-Unconstrained-Optimization.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Equality Constrained Minimization (<a href="./Convex-Optimization-Fall-2017/12-Equality-Constrained-Optimization.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Interior-Point Methods (<a href="./Convex-Optimization-Fall-2017/13-Interior-Point-Method.pdf">Lecture Notes</a>)</p>
</li>
</ol>
<p><b>Advanced Topics (Nov. 27 - Dec. 6, 2 weeks)</b></p>
<ol>
<li><p>Convex Relaxation For Nonconvex Problems (<a href="./Convex-Optimization-Fall-2017/14-Convex-Relaxation-Nonconvex-Problems.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Gradient Descent in Machine Learning (<a href="./Convex-Optimization-Fall-2017/15-Gradient-Descent-in-Machine-Learning.pdf">Lecture Notes</a>)</p>
</li>
<li><p>Softmax Regression and Neural Networks (<a href="./Convex-Optimization-Fall-2017/16-Softmax-Regression-and-Neural-Networks.pdf">Lecture Notes</a>)</p>
</li>
</ol>
<p><b>Final Project (Dec. 15)</b></p>
<h2>Homework</h2>
<p><b>Homework 1</b> (due Oct. 23) (<a href="./Convex-Optimization-Fall-2017/Solution-Homework-1.pdf">Solution</a>)</p>
<ul>
<li><p>exercises 2.7, 2.11, 2.12 (a,b,c,d,e,g), 2.19, 2.24</p>
</li>
<li><p>exercises 3.2, 3.19, 3.20, 3.21, 3.23</p>
</li>
</ul>
<p><b>Homework 2</b> (due Oct. 23) (<a href="./Convex-Optimization-Fall-2017/Solution-Homework-2.pdf">Solution</a>)</p>
<ul>
<li><p>exercises 4.7, 4.11, 4.15, 4.23, 4.33</p>
</li>
<li><p>exercises 5.5, 5.11, 5.21 (a,b,c), 5.26, 5.27</p>
</li>
</ul>
<p><b><a href="./Convex-Optimization-Fall-2017/Homework-3.pdf">Homework 3</a></b> (due Nov. 15) (<a href="./Convex-Optimization-Fall-2017/homework_3_solution.m">Solution in Matlab</a>, <a href="./Convex-Optimization-Fall-2017/homework_3_solution.py">Solution in Python</a>)</p>
<p><b><a href="./Convex-Optimization-Fall-2017/Homework-4.pdf">Homework 4</a></b> (due Dec. 4)</p>
<ul>
<li><p>required files for Matlab: <a href="./Convex-Optimization-Fall-2017/load_mnist.m">load_mnist.m</a>, <a href="./Convex-Optimization-Fall-2017/load_mnist_5_6.m">load_mnist_5_6.m</a>, <a href="./Convex-Optimization-Fall-2017/logistic_regression.m">logistic_regression.m</a></p>
</li>
<li><p>required files for Python: <a href="./Convex-Optimization-Fall-2017/load_mnist.py">load_mnist.py</a>, <a href="./Convex-Optimization-Fall-2017/load_mnist_5_6.py">load_mnist_5_6.py</a>, <a href="./Convex-Optimization-Fall-2017/logistic_regression.py">logistic_regression.py</a></p>
</li>
</ul>
<p><b><a href="./Convex-Optimization-Fall-2017/Homework-5.pdf">Homework 5</a></b> (due Dec. 4)</p>
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