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Candidate solution
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1	CS168: The Modern Algorithmic Toolbox Lecture #18: Linear and Convex Programming, with Applications to Sparse Recovery Tim Roughgarden & Gregory Valiant∗ May 25, 2016 Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-06-04 09:49:43 Mathematical optimization Numerical analysis Mathematical analysis Operations research Linear programming Convex optimization Convex analysis Ellipsoid method Feasible region Convex function Linear inequality Candidate solution
2	Neuroevolutionary Constrained Optimization for Content Creation Antonios Liapis, Georgios N. Yannakakis, Member, IEEE and Julian Togelius, Member, IEEE Abstract— This paper presents a constraint-based procedural conten Add to Reading List Source URL: antoniosliapis.com Language: English - Date: 2014-12-01 05:18:40 Mathematical optimization Operations research Cybernetics Applied mathematics Mathematical analysis Systems science Candidate solution Constraint Genetic algorithm Spaceship Feasible region Interplanetary Transport System
3	at e d Luis Ceze * Easy to ed Add to Reading List Source URL: synapse.uwplse.org Language: English - Date: 2016-01-19 16:03:46 Mathematical optimization Operations research Symbol Candidate solution Model theory Algorithm Logic Mathematics Abstraction
4	CS261: A Second Course in Algorithms Lecture #7: Linear Programming: Introduction and Applications∗ Tim Roughgarden† January 26, 2016 Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-02-06 11:11:11 Mathematical optimization Operations research Numerical analysis Mathematical analysis Linear programming Convex optimization Combinatorial optimization Feasible region Duality Linear regression Candidate solution Linear-fractional programming
5	COSMOS public description clean Add to Reading List Source URL: www.belpex.be Language: English - Date: 2016-04-22 04:57:20 Economy Finance Mathematical optimization Money Financial markets Stock market Operations research EPEX SPOT Societates Europaeae Electricity market Order Candidate solution
6	AN LP RELAXATION PROCEDURE FOR ANNOTATING POINT FEATURES USING INTERACTIVE GRAPHICS Robert G. Cromley University of Connecticut Storrs, CTABSTRACT Add to Reading List Source URL: mapcontext.com Language: English - Date: 2008-08-29 23:58:50 Mathematical optimization Mathematics Mathematical analysis Operations research Convex optimization Linear programming Combinatorial optimization Automatic label placement Constraint Linear programming relaxation Relaxation Candidate solution
7	Sentient Sketchbook: Computer-Aided Game Level Authoring Antonios Liapis Georgios N. Yannakakis Add to Reading List Source URL: antoniosliapis.com Language: English - Date: 2014-12-01 05:18:59 Software Mathematical optimization Information science Information retrieval Candidate solution Genetic algorithm Personalization Google Maps Constraint Evolutionary computation Neuroevolution
8	CS264: Beyond Worst-Case Analysis Lecture #8: Exact Recovery in Stable Cut Instances∗ Tim Roughgarden† October 15, Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2014-11-05 01:13:23 Combinatorial optimization Linear programming relaxation Maximum flow problem Maximum cut Linear programming Cut Candidate solution Randomized algorithm Max-flow min-cut theorem Randomized rounding
9	CS261: A Second Course in Algorithms Lecture #10: The Minimax Theorem and Algorithms for Linear Programming∗ Tim Roughgarden† February 4, 2016 Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-02-14 14:14:35 Operations research Linear programming Convex optimization Combinatorial optimization Mathematical optimization Simplex algorithm Ellipsoid method Simplex Candidate solution Nonlinear programming Minimax Optimization problem
10	CS261: Problem Set #3 Due by 11:59 PM on Tuesday, February 23, 2016 Instructions: (1) Form a group of 1-3 students. You should turn in only one write-up for your entire group. (2) Submission instructions: We are using Gr Add to Reading List Source URL: theory.stanford.edu Language: English - Date: 2016-02-24 10:23:04 Operations research Linear programming Mathematical optimization Convex optimization Algorithm Candidate solution Simplex algorithm Linear programming relaxation