(how) does machine learning impact or? - lnmb · learning (about) optimal good solutions i learn...
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(How) does Machine Learning impact OR?
Marco LubbeckeLehrstuhl fur Operations Research
RWTH Aachen University
@mluebbecke
NGB/LNMB Seminar · Lunteren, NL · January 16, 2019
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1994
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1994
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2019
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2019
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Example: Supervised Learning: Classification
I data X
, d features, labels Y
(xi
, yi
)
(φ(xi), yi)φ : X → Rd
algorithm
f : Rd → Y
s.t. errorxi∈X
(f(φ(xi)), yi) “small”
“learns”
an optimization problem
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Example: Supervised Learning: Classification
I data X
, d features,
labels Y
(xi, yi)
(φ(xi), yi)φ : X → Rd
algorithm
f : Rd → Y
s.t. errorxi∈X
(f(φ(xi)), yi) “small”
“learns”
an optimization problem
@mluebbecke · How does ML impact OR? · 4/33
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Example: Supervised Learning: Classification
I data X , d features, labels Y
(xi, yi) (φ(xi), yi)φ : X → Rd
algorithm
f : Rd → Y
s.t. errorxi∈X
(f(φ(xi)), yi) “small”
“learns”
an optimization problem
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Example: Supervised Learning: Classification
I data X , d features, labels Y
(xi, yi) (φ(xi), yi)φ : X → Rd
algorithm
f : Rd → Y
s.t. errorxi∈X
(f(φ(xi)), yi) “small”
“learns”
an optimization problem
@mluebbecke · How does ML impact OR? · 4/33
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Example: Supervised Learning: Classification
I data X , d features, labels Y
(xi, yi) (φ(xi), yi)φ : X → Rd
algorithm
f : Rd → Y
s.t. errorxi∈X
(f(φ(xi)), yi) “small”
“learns”
an optimization problem
@mluebbecke · How does ML impact OR? · 4/33
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Example: Supervised Learning: Classification
I data X , d features, labels Y
(xi, yi) (φ(xi), yi)φ : X → Rd
algorithm
f : Rd → Y
s.t. errorxi∈X
(f(φ(xi)), yi) “small”
“learns”
an optimization problem
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Optimization naturally appears in ML
“Optimization lies at the heart of ML. Most MLproblems reduce to optimization problems.
— Bennett, Parrado-Hernandez (2006) ”I minimize e.g., prediction error
I continuous, convex optimization
I discrete, integer optimization
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Example: Optimal Classification Trees
I several mixed-integer programs e.g., Bertsimas & Dunn (2017)
I can impose additional structural constraints! Bastert (2018)
I “path-based” integer program Fırat et al. (2018)
source: www.edureka.co/blog/decision-trees
I goals? use few nodes, shallow depth, . . .
I improves accuracy over classical CART method
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Example: Robustifying against adverserial Inputs
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Many Opportunities for Discrete Optimization in ML
I capture combinatorial explosion
I feature selection
I outlier detection
I parameter tuning
I . . .
→ check out what the Montreal people are up to
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How about the converse Direction?
I “emulating the expert” Barmann, Martin, Pokutta & Schneider (2017)
I observe a decision maker max cTtruex : x ∈ X(p)
I learn their objective function given (pt, x∗t )t=1,...,T
I online learning
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ML may help improving Optimization Algorithms
I e.g., branching in B&B
I full strong branching gives locally perfect information
I predict the strong branching score of a variable
I features describe state of a variable
I supervised learning: regression
→ promising proof-of-conceptMarcos Alvarez, Louveaux & Wehenkel (2017)
I survey on ML in branching/searching Lodi & Zarpellon (2017)
→ a lot more research in 2018
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More ML for algorithmic Ingegredients of B&B
I search strategy / node selection He et al. (2014)
I which primal heuristic to run when? Khalil et al. (2017)
I challenge: cut selection
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A Progress Bar for Branch-and-Bound?
I predict the runtime of branch-and-bound algorithmsHutter, Xu, Hoos & Leyton-Brown (2014)
I CPLEX 12.1 on 1510 publicly available MIPs
pred
icte
d
actual runtime
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Learning the Optimum (for specific Problems)
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Learning when to solve a MIP by Branch-and-Price
I our MIP solver GCG detects many potential DW reformulations
MIP+
CPU time
...
...
100+ features#conss, #vars, %constype,%vartype, #blocks, . . .
φk-NN
binary classifier f
“run SCIP or GCG?”
learnsfaster
SCIP GCG
fSCIP 69.5% 9.9%GCG 6.9% 13.7%
Kruber, L, Parmentier (2017)
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Learning when to solve a MIP by Branch-and-Price
I our MIP solver GCG detects many potential DW reformulations
MIP+
CPU time
...
...
100+ features#conss, #vars, %constype,%vartype, #blocks, . . .
φ
k-NN
binary classifier f
“run SCIP or GCG?”
learnsfaster
SCIP GCG
fSCIP 69.5% 9.9%GCG 6.9% 13.7%
Kruber, L, Parmentier (2017)
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Learning when to solve a MIP by Branch-and-Price
I our MIP solver GCG detects many potential DW reformulations
MIP+
CPU time
...
...
100+ features#conss, #vars, %constype,%vartype, #blocks, . . .
φk-NN
binary classifier f
“run SCIP or GCG?”
learns
fasterSCIP GCG
fSCIP 69.5% 9.9%GCG 6.9% 13.7%
Kruber, L, Parmentier (2017)
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Learning when to solve a MIP by Branch-and-Price
I our MIP solver GCG detects many potential DW reformulations
MIP+
CPU time
...
...
100+ features#conss, #vars, %constype,%vartype, #blocks, . . .
φk-NN
binary classifier f
“run SCIP or GCG?”
learnsfaster
SCIP GCG
fSCIP 69.5% 9.9%GCG 6.9% 13.7%
Kruber, L, Parmentier (2017)
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Learning how to attack MIQPs
I linearize quadratic part or not Bonami et al. (2018)
I “ML to parametrize optimization algorithm”
+ try to derive structural explanations/insights
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What ML Answers can we (Optimizers) expect?
I we get statistical answers → not what we are used to see
I we have domain/expert knowledge: e.g., pseudo-costs
I ML may give a better predictor, but no explanation
→ more precisely: no structural explanation
I some info can be extracted from most influential features
→ interpretability is a huge theoretical and practical topic
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Decision Making: Machine Learning
“Machine Learning and Artificial Intelligencedelivers the most value when you need tomake lots of similar decisions quickly.
— Ingo Mierswa, Rapidminer ”I simple decisions: e.g., auto correct current word
I solution: often a single score → greedy
I keep/learn habits: extrapolate from the past (!)
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Typical Example: Predictive Maintenance
source: blog.capterra.com/should-you-invest-in-a-predictive-maintenance-strategy/
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Exploit all Options: Prescriptive Maintenance
source: www.siemens.com/press/pool/de/pressebilder/photonews/pn200826/300dpi/pn200826-12 300dpi.jpg
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Decision Making: Optimization
“How often can the result of an optimizationmodel be captured in a single variable?
— Ed Rothberg, Gurobi ”I solution: not only the objective value!
I complex decisions/plans: e.g., timetables, crew schedules, . . .
I global scope: models all (reasonable) interdependencies
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Perfect Partners
“Current Standard:” Predictive then Prescriptive Analytics
ML harnesses the bigness of data (the past and present);Optimization captures the bigness of options (the future).
I e.g., predict arc travel times Gmira et al. (2017)
I e.g., predict availability of charging stations L., (ongoing)
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Learning (about) optimal good Solutions
I learn how good (partial) solutions look like
I this may help finding good solutions faster
I reinforcement learning to predict trajectories Le, Liu, Lau (2016)
I predict partial optimal job-shop schedules Shylo, Shams (2018)
→ guide a tabu search
I predict (aggr.) solutions to stochastic ILPs Larsen et al. (2018)
→ high-speed, high-accuracy for particular problems
I deep-learning / container pre-marshalling Tierney et al. (2018)
→ guide a tree search
...
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MLers think ML can do Optimization
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MLers think ML can do Optimization
I open source library github.com/deepmind/graph_nets
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MLers think ML can do Optimization
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MLers think ML can do Optimization
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MLers think ML can do Optimization
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Maybe they are right?
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Maybe they are right?
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Maybe they are right?
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We need to (and do) talk to each other
I classification ML4Opt: end-to-end, offline, online
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We need to (and do) talk to each other
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What’s next?
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Robust Optimization
I data-driven design of uncertainty sets Bertsimas et al. (2018)
I a practical notion of robustness?
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Learning (about) Optimization Models?
I a business school student asked me why we need models
I ML can make sense of data
I optimization models are also “data”
→ (how) can ML help us make sense of optimization models?
I can ML learn (+ help automate) good modeling?
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Learning (about) Optimization Problems?
potential first step:
I can ML learn the semantics of a MIP model (“the problem”)?
⇒ e.g., help the modeler find a better formulation
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The AI Umbrella?
I OR vs. analytics discussion X
I OR vs. AI discussion ?
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Why is this relevant?
source: blogs.worldbank.org/category/tags/artificial-intelligence
I if the fourth industrial revolution is about AI,OR should be part of it
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Why is this relevant?
I besides (perceived) relevance
I and besides the enormous public reception:
I job openings in industry
I position titles in academia
I calls by funding bodies
I journal mission statements
I recognition by peers
→ all AI/ML “framed”. . .
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(How) does Machine Learning impact OR?
Marco LubbeckeLehrstuhl fur Operations Research
RWTH Aachen University
@mluebbecke
NGB/LNMB Seminar · Lunteren, NL · January 16, 2019
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