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Currently, we directly backpropagate through the tangent vector field calculation, which involves a Newton solve to find the minimum of a convex quadratic objective. It may be more efficient to define…
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# 11-1 Lagrangian · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter11/2021/03/24/11_01_Lagrangian/](https://convex-optimization-for-all.github.io/contents/chapter11/2…
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# 10-03 Max flow and min cut · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter10/2021/03/22/10_03_Max_flow_and_min_cut/](https://convex-optimization-for-all.github.io…
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# 06-04 Gradient boosting · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter06/2021/03/20/06_04_gradient_boosting/](https://convex-optimization-for-all.github.io/conte…
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Thanks a lot for this fantastic package!
I wonder if there is a way to start the optimization with an initial guess on variables? I see there is a `warm_starts` argument for `diffcp` used by this p…
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### Idea
We currently do not have optimization algorithms in stdlib. Having support for Linear Programming, Convex Optimization, Quadratic Programming, and/or Non-Linear Optimization algorithms wou…
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# 11-2 Lagrange dual function · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter11/2021/03/24/11_02_Lagrange_dual_function/](https://convex-optimization-for-all.github…
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# 09-05-02 Convergence analysis · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter09/2020/01/08/09_05_02_convergence_analysis/](https://convex-optimization-for-all.git…
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# 09-01 Proximal gradient descent · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter09/2020/01/08/09_01_proximal_gradient_descent/](https://convex-optimization-for-all…
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# 24-03 Solving integer programs · 모두를 위한 컨벡스 최적화
[https://convex-optimization-for-all.github.io/contents/chapter24/2021/03/28/24_03_Solving_integer_programs/](https://convex-optimization-for-all.g…