y a l mi p o p t i m i z a t i o n ma d e e a s y
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Y A L MI P : O p t i m i z a t i o n Ma d e E a - PowerPoint PPT Presentation

Y A L MI P : O p t i m i z a t i o n Ma d e E a s y ! a n d M o d e l i n g L a n g u a g e s / L a y e r s f o r O p t i m i z a t i o n i n g e n e r a l P i e r r e


  1. Y A L MI P : O p t i m i z a t i o n Ma d e E a s y ! a n d M o d e l i n g L a n g u a g e s / L a y e r s f o r O p t i m i z a t i o n i n g e n e r a l P i e r r e H a e s s i g t h C e n t r a l e S u p é l e c R e n n e s , A p r i l 6 , 2 0 1 7

  2. Wh e r e i s h u m a n t i m e s p e n t ? 1 ) F o r m a l i z e t h e p r o b l e m i n t o a Ma t h e m a t i c a l O p t i m i z a t i o n p r o b l e m → o f t h e r e s e a r c h e r c o r e s k i l l 2 ) T r a n s f o r m t o a c a n o n i c a l f o r m ( s o l v e r s p e c i f i c A P I ) [ n o w s o m e c o m p u t i n g t i m e ../ ] 3 ) R e t r i e v e r e s u l t s o u t o f t h e c a n o n i c a l f o r m a t ( a g a i n s o l v e r s p e c i f i c ) Y A L MI P c a n h e l p t h e r e s e a r c h e r f o c u s o n i t s c o r e s k i l l 4 / 1 4

  3. Mo d e l i n g L a n g u a g e s / L a y e r s f o r O p t i m i z a t i o n E n v i r o n m e n t S o fu w a r e / T o o b o x / P a c k a g e S t a n d a l o n e A MP L , G A MS ( ~ 1 9 9 0 ) Ma t l a b , C V X ( ~ 2 0 0 0 ) Y A L MI P P y t h o n P u L P , C V X P y J u l i a J u MP , C o n v e x . j l 6 / 1 4

  4. R e l a t i o n s h i p t o t h e o p t i m i z a t i o n s o l v e r ● Wh e n u s i n g a m o d e l i n g e n v i r o n m e n t , U s e r t h e s o l v e r i s f r o m t h e u s e r . m o s t l y h i d d e n ● T h e c h o i c e o f t h e m o d e l i n g l a y e r i s ( m o s t l y ) i n d e p e n d e n t o f t h e c h o i c e o f s o l v e r . Mo d e l i n g e n v . Y A L M I P , A M P L F o r e x . Y A L MI P p r o v i d e s i n t e r f a c e s f o r m o s t c o m m o n s o l v e r s : – G u r o b i , C P L E X , MO S E K ( C o m m e r c i a l ) S o l v e r – G L P K , I p o p t , S E D U MI ( F r e e ) G u r o b i , C P L E X – l i n p r o g f r o m Ma t l a b O p t i m i z a t i o n T o o l b o x 7 / 1 4

  5. Ma t l a b T o o b o x e s ● Y : 2 0 0 1 – p r e s e n t , A L MI P J . L ö f b e r g f r o m L i n k ö p i n g U n i v e r s i t y , ( p o s t - d o c a t E T H Z ü r i c h ) P r o j e c t : h tu p s : / / y a l m i p . g i t h u b . i o / . A u t h o r : h tu p : / / u s e r s . i s y . l i u . s e / e n / r t / j o h a n l / ● C : 2 0 0 5 – p r e s e n t , a n d . V X M. G r a n t S . B o y d f r o m S t a n f o r d U n i v e r s i t y ( c o m p a t i b i l i t y p r o b l e m w i t h M a t l a b 2 0 1 7 ) – C V X R e s e a r c h , i n c . 2 0 1 2 : h tu p : / / c v x r . c o m / – M. G r a n t j o i n e d C o n t i n u u m A n a l y t i c s i n 2 0 1 5 ( p l a t f o r m s f o r D a t a S c i e n c e , m o s t l y P y t h o n ) . 8 / 1 4

  6. Y A L MI P q u i c k s t a r t Z I P a r c h i v e a n d u n z i p t h e a r c h i v e i n s o m e f o l d e r . 1 ) D o w n l o a d ( h tu p s : / / y a l m i p . g i t h u b . i o / d o w n l o a d / ) 2 ) A d d Y A L MI P f o l d e r s ( w i t h s u b f o l d e r s ) t o t h e . MA T L A B p a t h ( c f . h tu p s : / / y a l m i p . g i t h u b . i o / t u t o r i a l / i n s t a l l a t i o n / ) 3 ) S t a r t u s i n g i t ! Y o u c a n l o o k a t t h e “ G e tu i n g S t a r t e d ” t u t o r i a l . ( h tu p s : / / y a l m i p . g i t h u b . i o / t u t o r i a l / b a s i c s / ) 9 / 1 4

  7. G r i d - c o n n e c t e d P V - s t o r a g e s y s t e m D e mo : c f . P V g r i d . m s c r i p t 1 0 / 1 4

  8. Wr a p u p : a d v a n t a g e s / d r a w b a c k s K e y a d v a n t a g e s : ● S h o r t e r s t a r t i n g t i m e ( f o r s t u d e n t s ) , s h o r t e r d e v e l o p m e n t t i m e ● I n c r e a s e d a g i l i t y ( → b e tu e r r e s e a r c h ! ) – Qv i c k l y c o mp a r e s o l v e r s – Qv i c k l y c o mp a r e d i fg e r e n t p r o b l e m mo d e l s ( e . g . L P v s . Q P ) B u t m a y b e : ● C o m p u t a t i o n a l o v e r h e a d ? – e . g . l e s s e fg i c i e n t w h e n r e c y c l i n g t h e p r o b l e m ( l i k e f o r MP C ) ? 1 1 / 1 4

  9. A p p l i c a t i o n t o E m b e d d e d O p t i m i z a t i o n E x a m p l e o f a n “ B A R C P r o j e c t ” A u t o n o mo u s D r i v i n g R C C a r ● I m p l e m e n t a t i o n w i t h J u l i a + J u MP ( + R O S + ../ ) ● P r o j e c t p a g e s : h tu p : / / w w w . b a r c - p r o j e c t . c o m / , h tu p s : / / g i t h u b . c o m / MP C - B e r k e l e y / b a r c ● P r e s e n t a t i o n b y J o n G o n z a l e s ( B e r k e l e y MP C L a b ) a t J u l i a C o n 2 0 1 6 h tu p s : / / w w w . y o u t u b e . c o m / w a t c h ? v = b X 4 T X WO 7 d A 0 1 2 / 1 4

  10. S t a n d a l o n e c o m m e r c i a l m o d e l i n g l a n g u a g e s ● A ( A Ma t h e m a t i c a l P r o g r a m m i n g L a n g u a g e ) h tu p : / / a m p l . c o m / MP L – s t a r t e d ~ 1 9 8 5 a t B e l l l a b s – “ A MP L O p t i m i z a t i o n L L C ” s p u n - o fg i n 2 0 0 2 . ● G ( G e n e r a l A l g e b r a i c Mo d e l i n g S y s t e m ) h tu p s : / / w w w . g a m s . c o m A MS – s t a r t e d i n 1 9 7 0 s a t t h e Wo r l d B a n k ( a n e c o n o m i c m o d e l i n g g r o u p ) – c o m m e r c i a l p r o d u c t b y “ G A MS D e v e l o p e m e n t C o r p . ” s i n c e 1 9 8 7 1 3 / 1 4

  11. S o m e r e f e r e n c e s , i n c h r o n o l o g i c a l o r d e r ● G : J . B i s s c h o p a n d A . Me e r a u s , “ O n t h e d e v e l o p m e n t o f a g e n e r a l a l g e b r a i c A MS m o d e l i n g s y s t e m i n a s t r a t e g i c p l a n n i n g e n v i r o n m e n t , ” Ma t h e m a t i c a l P r o g r a m m i n g S t u d i e s , v o l . 2 0 , p . 1 – 2 9 , 1 9 8 2 . ● A : R . F o u r e r , D . M. G a y , a n d B . W. K e r n i g h a n , “ A Mo d e l i n g L a n g u a g e f o r MP L Ma t h e m a t i c a l P r o g r a m m i n g , ” Ma n a g e m e n t S c i e n c e , v o l . 3 6 , n o . 5 , p . 5 1 9 – 5 5 4 , 1 9 9 0 . ● Y : J . L ö f b e r g , “ Y A L MI P : a t o o l b o x f o r m o d e l i n g a n d o p t i m i z a t i o n i n A L MI P MA T L A B , ” i n 2 0 0 4 I E E E I n t e r n a t i o n a l C o n f e r e n c e o n R o b o t i c s a n d A u t o m a t i o n , 2 0 0 4 . ● C : M. G r a n t a n d S . B o y d , “ G r a p h I m p l e m e n t a t i o n s f o r N o n s m o o t h C o n v e x V X P r o g r a m s ” , i n R e c e n t A d v a n c e s i n L e a r n i n g a n d C o n t r o l ( t r i b u t e t o M . V i d y a s a g a r ) , V . B l o n d e l , S . B o y d , a n d H . K i m u r a , e d i t o r s , S p r i n g e r , , p p . 9 5 - 1 1 0 . 2 0 0 8 1 4 / 1 4

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