Organizations: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada · Huawei Technologies Canada, Burnaby, BC, Canada · Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada
Integer programming (IP) is central to many combinatorial optimization tasks but remains challenging due to its NP-hard nature. A practical way to improve IP solvers is to manually design inequality families that exploit problem structure. However, this creative process requires deep expertise and has therefore been difficult to automate. Our proposed framework, Symbolic Acceleration via Generative Evolution (SAGE), automates symbolic discovery of acceleration inequality families at the modeling level. It reasons over a symbolic IP formulation and a natural language description of the problem to discover a reusable set of acceleration inequality families that can be instantiated for each problem instance. These acceleration inequality families are selected through empirical admissibility and impact screening rather than mathematical proofs. SAGE (i) initializes a population of candidate acceleration inequality families via an initializer agent that uses an LLM; (ii) it screens candidates for admissibility on a small development admissibility set for solution feasibility and the separation of the relaxation solution; and (iii) it iteratively refines the population of inequalities through evolutionary crossover and mutation agents. Across seven MILP benchmarks, SAGE reduces optimality gaps by up to 62.1% relative to baseline MILP formulations solved with a fixed time budget and reaches target gaps up to 5.51× faster according to shifted geometric mean speedup. Comprehensive ablations show its robustness across different LLM backends and across solvers and internal cut settings. Code: https://github.com/milad1378yz/sage
Figures & tables
Figure 1: View of SAGE in two stages: during development, SAGE learns a reusable set of acceleration inequality families for a symbolic MILP formulation using a natural language description. During deployment, the families are instantiated for each problem instance and appended to its instantiated MILP before solving.
Mechanism
Stage
Problem-specific structure
Operates on
Derivation
Output
SAGE
Model design, before the solver is called
Yes, for a problem class
Symbolic MILP formulation
Automated, by LLM agents with evolutionary search
A reusable acceleration inequality family, instantiated for each instance
Solver cuts and learned runtime policies ( Turner et al., 2023 ; Gasse et al., 2019 ; Li et al., 2023 )
Inside the solver, during branch-and-bound
No, generic cuts or learned policies
Instantiated MILP
Automated, by solver routines or trained policies
Cuts and search decisions during the solve
Table 1: SAGE compared with other mechanisms that augment or transform a MILP: the stage at which each acts, whether it uses problem-specific structure, what it operates on, whether it is manual or automated, and what it returns.
Figure 2: SAGE workflow: an initializer agent proposes a population of candidate acceleration inequality families. Each candidate passes through the Inequality Admissibility Screen for code validity, OSS on the admissibility set Dadm , and an LP-separation check, then through the Inequality Impact Scorer on Dimp to obtain an inequality impact score based on the optimality gap. Tournament/elite selection and crossover/mutation driven by LLM agents generate the next population.
Figure 3: SAGE Inequality Admissibility Screen: candidate family code must compile (code check), pass OSS check on the admissibility set Dadm (i.e., not exclude the recorded reference solution), and separate at least one stored fractional LP optimum (LP-separation check). Failures trigger feedback and regeneration.
Problem
LLM
5 s
10 s
50 s
150 s
300 s
1000 s
2000 s
Average
TSP
DeepSeek-R1
8.2 (± 26.0)
9.1 (± 26.9)
11.9 (± 32.2)
18.1 (± 30.9)
18.5 (± 33.8)
12.9 (± 31.3)
20.7 (± 30.7)
14.2
Gemini 3 Pro
13.1 (± 27.5)
15.0 (± 27.9)
22.6 (± 31.3)
34.2 (± 30.3)
27.6 (± 33.1)
26.4 (± 33.3)
37.0 (± 31.1)
25.1
GPT-5.1
12.1 (± 28.4)
8.8 (± 29.7)
15.6 (± 33.2)
17.4 (± 34.1)
11.3 (± 35.0)
16.7 (± 33.8)
18.2 (± 31.6)
14.3
MCND
DeepSeek-R1
3.3 (± 17.8)
4.3 (± 19.9)
11.7 (± 27.0)
13.0 (± 28.2)
14.7 (± 28.9)
18.9 (± 32.4)
16.7 (± 29.7)
11.8
Gemini 3 Pro
6.8 (± 17.7)
8.2 (± 19.9)
15.1 (± 25.8)
17.4 (± 26.9)
17.8 (± 25.5)
23.2 (± 27.9)
15.8 (± 28.6)
14.9
GPT-5.1
25.5 (± 30.1)
22.3 (± 29.6)
16.5 (± 27.7)
6.9 (± 30.0)
15.6 (± 28.9)
18.1 (± 29.8)
20.4 (± 25.2)
17.9
Table 2: Test set mean stabilized relative gap improvement Δˉg (%) from adding the selected SAGE acceleration inequality family (Gurobi, three LLMs). Mean ±σ is reported at each checkpoint. Positive values correspond to gaug<gref at that checkpoint. Average reports the row average of checkpoint means.
g=10−5
g=10−4
g=10−3
g=10−2
g=10−1
Average
Problem
B/A
SGM
B/A
SGM
B/A
SGM
B/A
SGM
B/A
SGM
B/A
SGM
TSP
26/35
3.21
27/37
3.13
27/37
3.16
31/44
3.00
44/47
1.83
31.0/40.0
2.87
MCND
56/57
1.04
62/62
1.04
62/62
1.03
69/69
1.04
76/74
0.92
65.0/64.8
1.01
CWLP
26/23
2.44
35/36
4.24
35/36
4.42
35/36
5.19
35/36
3.83
33.2/33.4
4.02
JSSP
7/8
2.18
7/8
2.31
7/8
2.42
7/8
2.55
10/19
7.20
7.6/10.2
3.33
PDPTW
0/5
5.18
0/5
5.18
0/5
5.19
0/5
5.25
0/20
6.77
0.0/8.0
5.51
Table 3: Time to reach target gaps g : baseline vs. SAGE . B/A: instances reaching g within 2000 s (Baseline/ SAGE ). SGM: shifted geometric mean speedup ( >1 better). Average reports the row average over all target gaps.
SAGE
Solver-layer policy
Avg SGM speedup
Off
SCIP default
1.00
Off
Turner et al.
2.12
Off
Lawless et al. LLM(0)
2.58
Off
Lawless et al. LLM(5)
2.60
On
SCIP default
3.13
On
Turner et al.
3.45
Table 4: CWLP factorial with learned runtime policies, held-out OR-Library test set, SCIP 10.0, 2000 s time limit, single seed. The reported value is the average over the five target gaps g∈{10−5,10−4,10−3,10−2,10−1} of the time-to-target SGM speedup vs the reference cell ( SAGE off, plain SCIP defaults), following the protocol of Table 3 . Values >1 are faster. The reference cell is 1.00 × by construction.
Problem
Solver
5 s
10 s
50 s
150 s
300 s
1000 s
2000 s
Average
TSP
Gurobi
13.1 (± 27.5)
15.0 (± 27.9)
22.6 (± 31.3)
34.2 (± 30.3)
27.6 (± 33.1)
26.4 (± 33.3)
37.0 (± 31.1)
25.1
HiGHS
2.5 (± 13.8)
2.9 (± 14.9)
11.3 (± 23.3)
18.6 (± 26.5)
18.5 (± 27.2)
21.4 (± 28.1)
21.9 (± 28.7)
13.9
MCND
Gurobi
6.8 (± 17.7)
8.2 (± 19.9)
15.1 (± 25.8)
17.4 (± 26.9)
17.8 (± 25.5)
23.2 (± 27.9)
15.8 (± 28.6)
14.9
HiGHS
6.0 (± 15.1)
7.0 (± 16.9)
6.6 (± 22.5)
1.4 (± 27.7)
5.5 (± 28.4)
2.0 (± 29.3)
5.7 (± 29.0)
4.9
CWLP
Gurobi
43.8 (± 20.7)
49.4 (± 22.3)
68.8 (± 28.6)
76.3 (± 23.8)
64.8 (± 23.0)
35.2 (± 39.3)
30.1 (± 38.9)
52.6
HiGHS
25.5 (± 24.6)
25.3 (± 24.6)
23.0 (± 24.8)
28.1 (± 29.8)
39.1 (± 36.2)
62.3 (± 32.3)
64.2 (± 31.9)
38.2
Table 5: Gemini 3 Pro selected acceleration inequality family: test stabilized Δˉg (%) with Gurobi vs. HiGHS. Mean ±σ is reported at each checkpoint. Average reports the row average of checkpoint means.
Problem
Solver
5 s
10 s
50 s
150 s
300 s
1000 s
2000 s
Average
TSP
No internal cuts
34.9 (± 25.1)
37.8 (± 25.5)
53.5 (± 23.3)
61.6 (± 18.1)
64.2 (± 16.5)
64.0 (± 16.8)
62.0 (± 20.2)
54.0
Default internal cuts
13.1 (± 27.5)
15.0 (± 27.9)
22.6 (± 31.3)
34.2 (± 30.3)
27.6 (± 33.1)
26.4 (± 33.3)
37.0 (± 31.1)
25.1
MCND
No internal cuts
2.5 (± 10.0)
2.9 (± 11.0)
4.3 (± 14.8)
3.4 (± 15.3)
1.7 (± 17.1)
4.1 (± 17.2)
5.1 (± 13.6)
3.4
Default internal cuts
6.8 (± 17.7)
8.2 (± 19.9)
15.1 (± 25.8)
17.4 (± 26.9)
17.8 (± 25.5)
23.2 (± 27.9)
15.8 (± 28.6)
14.9
CWLP
No internal cuts
53.6 (± 19.8)
60.6 (± 21.2)
86.7 (± 19.8)
84.3 (± 28.3)
94.8 (± 8.8)
94.5 (± 8.9)
93.9 (± 9.2)
81.2
Default internal cuts
43.8 (± 20.7)
49.4 (± 22.3)
68.8 (± 28.6)
76.3 (± 23.8)
64.8 (± 23.0)
35.2 (± 39.3)
30.1 (± 38.9)
52.6
Table 6: Gemini 3 Pro selected acceleration inequality family: test stabilized Δˉg (%) with Gurobi internal cuts disabled vs. default. Mean ±σ is reported at each checkpoint. Average reports the row average of checkpoint means.
(∣Dadm∣,∣Dimp∣)
(2,10)
(5,10)
(10,10)
(10,5)
(10,2)
Δˉg±σ(%)
37.3 ± 22.0
37.3 ± 22.0
40.4 ± 22.3
37.3 ± 22.0
7.1 ± 16.4
Table 7: JSSP: test stabilized Δˉg±σ(%) at the 2000 s checkpoint for the selected SAGE acceleration inequality family vs. (∣Dadm∣,∣Dimp∣) .
Agent
LLM
Code Fail.%
OSS Fail.%
Not Useful %
Success Rate %
Δˉimp±σ (%)
Initializer
Main
DeepSeek
38.1
11.1
25.4
25.4
12.3 ± 20.8
Gemini 3 Pro
5.6
12.8
45.1
36.5
14.8 ± 19.9
GPT-5.1
8.9
14.7
46.8
29.6
10.4 ± 19.5
Mutation
General
DeepSeek
17.6
13.2
2.9
66.2
15.5 ± 16.6
Table 8: SAGE agent outcomes across three LLMs: code fail, OSS fail, LP-separation rejection, success rate, and mean inequality-impact-score gain Δˉimp±σ (%) (successful offspring only).
Run
Tokens
Best simp
−dˉ (%)
SAGE (5 generations)
761,451
19.07
64.6
Initializer with feedback
765,308
17.60
56.5
Table 9: JSSP with DeepSeek-R1: development comparison between five generations of SAGE and the initializer with feedback, with token usage matched to within 0.51% after the shared initialization. Appendix C.3 gives the protocol and the deployment results.
Component
What it does
Source status
Binary product rows Eq. ( 3 )
Encode bg,t=vg,twg,t+1 , i.e., whether unit g starts at t and shuts down at t+1 .
Standard MILP linearization of binary products, not SHUC-specific ( Glover, 1975 ) .
Upper-bound reachable thermal capacity when the unit is starting up or shutting down.
Closely aligned with startup/shutdown ramping and strong UC production upper bounds ( Morales-España et al., 2012 ; Knueven et al., 2020 ) .
Lifted adjacent start/shutdown envelope Eq. ( 6 )
Combines the two derating envelopes and uses bg,t to avoid double-counting when both transition indicators are active.
Built from known lifting and UC derating ideas. Closest references are strong UC upper-bound and ramping formulations ( Knueven et al., 2018 ; Knueven et al., 2020 ) .
Projects unit-level startup, shutdown, and ramp-reachability envelopes into one system-level condition: net load plus reserve must be coverable by reachable thermal capacity.
In our targeted review, close analogs exist for the unit-level components in the UC formulation literature, including reserve and ramp-capability formulations ( Morales-España et al., 2013 ; Morales-España et al., 2015 ) , but we did not identify Eq. ( 10 ) as this same static aggregate inequality added before solving for the SHUC formulation in Appendix F.7 .
Table 10: Post-hoc source attribution for the selected SHUC acceleration inequality family. The table categorizes each component by whether it has a direct literature analog, follows from the base formulation, or appears to be a formulation-specific aggregate component.
Appendix figures & tables19 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 4: Example JSSP evolutionary trajectory. The green panels show candidate families with annotated inequality impact scores, and the yellow panels show the agent instructions that produced each offspring.
Figure 5: Best (blue, dashed) and mean (orange, dash-dotted) inequality impact scores across 20 generations for the TSP, MCND, CWLP, JSSP, IMO6, PDPTW, and SHUC benchmarks. Shaded regions show one standard deviation.
t (s)
SAGE
Initializer
Difference
95% CI
W/L/T
5
33.8 (± 22.6)
17.9 (± 28.1)
15.9
[11.7,20.1]
44 / 9 / 27
10
34.7 (± 23.1)
25.1 (± 27.8)
9.6
[6.0,13.2]
41 / 11 / 28
50
26.8 (± 26.4)
22.8 (± 31.2)
4.0
[0.9,7.1]
35 / 15 / 30
150
37.2 (± 28.3)
34.9 (± 33.4)
2.3
[−0.7,5.3]
34 / 23 / 23
300
28.4 (± 27.1)
23.5 (± 34.5)
4.9
[1.1,8.7]
38 / 17 / 25
1000
38.6 (± 26.8)
34.5 (± 35.2)
4.1
[−0.4,8.6]
35 / 22 / 23
Appendix
Table 11: JSSP deployment results for the families selected in Table 9 . Δˉg is reported as mean with standard deviation (%). Difference and CI give the mean paired difference and its pointwise confidence interval in percentage points. W/L/T gives win, loss, and tie counts for SAGE against the initializer.
Figure 6: Relationship between the integer feasible set S , its LP relaxation P , the set conv(S) , a valid inequality that removes the fractional LP optimum (useful), and an optimality-preserving inequality that may remove some feasible points while keeping the optimal value.
Figure 7: Change over time (up to 2000 s) of the mean stabilized relative gap improvement Δˉg(t) after adding the selected SAGE acceleration inequality family to the baseline MILP for TSP, MCND, CWLP, JSSP, PDPTW, IMO6, and SHUC. The solid line is the mean across test instances at each wall-clock time and the shaded band indicates one standard deviation ( σ ). Positive values mean the augmented model has a smaller optimality gap at that wall-clock time.
Problem
Separated / Total
Rate (%)
TSP
54/54
100.0
MCND
72/81
88.9
CWLP
36/36
100.0
JSSP
80/80
100.0
PDPTW
165/165
100.0
IMO6
34/34
100.0
Appendix
Table 12: Held-out test-set LP separation rate of the selected SAGE acceleration inequality family.
Problem
Setup (s)
Baseline (s)
SAGE (s)
Solves
Solves with setup
TSP
0.94
1063.8
760.3
238
238
MCND
0.006
640.7
627.1
5276
5278
CWLP
0.97
220.5
11.4
345
347
JSSP
0.46
1886.5
1846.1
1782
1803
PDPTW
0.08
2000.0
1962.7
1933
1935
IMO6
10.55
1594.5
1555.1
1828
2496
Appendix
Table 13: Discovery payback at g≤10−3 on reserved test instances. Setup is the mean added deployment setup time. Solves is the number of deployments whose solver time savings recover the search budget. The last column charges the added setup time to SAGE as well.
Method
Mean stabilized relative gap improvement (%)
Automatic ( −1 )
34.3±29.3
Deterministic concurrent (4)
27.6±33.1
Appendix
Table 14: TSP sensitivity to the Gurobi continuous solution method.
Problem
5 s
10 s
50 s
150 s
300 s
1000 s
2000 s
Average
RNTD
18±35
−50±197
24±17
24±19
25±20
28±20
23±19
13
Appendix
Table 15: RNTD test mean stabilized relative gap improvement Δˉg(%) . Mean ±σ is reported at each checkpoint.
Target gap
B/A
SGM speedup
10−5
9/12
4.2×
10−4
9/12
4.2×
10−3
9/13
4.2×
10−2
44/44
2.7×
10−1
44/44
1.0×
Average
23.0/25.0
3.3×
Appendix
Table 16: RNTD time to target results. B/A counts the baseline and augmented runs that reach the target.
Problem
Static literature valid inequalities
SAGE families
CWLP
3.01×
4.02×
JSSP
1.91×
3.33×
MCND
1.01×
1.01×
TSP
2.57×
2.87×
Appendix
Table 18: Mean SGM speedup for static literature valid inequalities and SAGE acceleration inequality families. Each value is averaged over the same five target gaps used in Table 3 .
Solver
Parameter
Default
Role
Gurobi
MIPGapAbs
1e-10
Absolute optimality gap tolerance.
Gurobi
Presolve
-1
Automatic presolve control.
Gurobi
Cuts
-1
Automatic global cut generation control.
Gurobi
Heuristics
0.05
Target fraction of runtime for primal heuristics.
Gurobi
MIPFocus
0
Balanced MIP search focus.
Gurobi
Symmetry
-1
Automatic symmetry detection.
Appendix
Table 19: Important default solver parameters not otherwise listed in the experimental setup.
Figure 8: Gap trajectories over time for four representative TSP instances ( gr202 , kroB150 , kroA200 , and kroD100 ) from Reinelt (1991) . Orange: baseline MILP augmented with the selected SAGE acceleration inequality family. Blue: baseline MILP.
Figure 9: Evolution of the solver’s best bound for four representative TSP instances. Blue lines: baseline MILP (reference). Orange lines: MILP with the selected SAGE acceleration inequality family.
Figure 10: PDI trajectories for the same four TSPLIB instances used in Figs. 8 - 9 . Lower curves are better. Orange: baseline + selected SAGE acceleration inequality family. Blue: baseline MILP.
Figure 11: Gap trajectories for four MCND instances where the selected acceleration inequality family slightly hurts the gap ( R-r18.9.dow , R-r15.6.dow , R-r15.3.dow , and R-r13.9.dow ). Orange: baseline MILP augmented with the selected SAGE acceleration inequality family. Blue: baseline MILP.
Benchmark
# Instances
Variables
Constraints
TSP
54
15821.65±15593.23
15931.00±15655.59
MCND
81
24991.89±17962.83
2485.22±1330.64
CWLP
36
34398.00±47121.26
420.22±487.64
JSSP
80
21382.25±31271.97
42046.88±62013.04
PDPTW
165
11090.00±0.00
4565.00±0.00
IMO6
34
20759.50±22078.88
7524.50±7842.23
Appendix
Table 20: Scale of held out benchmarks before solver presolve. Variable and constraint counts are reported as mean ± sample standard deviation for the baseline formulations and exclude the SAGE families.
Synthetic source for Dimp / Dadm
Variables
Constraints
TSP synthetic
16737.80±15413.50
16852.61±15473.23
MCND synthetic
24988.19±17868.66
2485.22±1322.35
CWLP synthetic
19690.59±31363.09
420.22±449.19
JSSP synthetic
21382.25±31075.91
42046.88±61624.24
PDPTW synthetic
8645.00±1038.60
3627.50±548.92
IMO6 synthetic
41740.75±44521.34
14863.25±15606.55
Appendix
Table 21: Synthetic development source scale for Dimp and Dadm . Variable and constraint counts are reported as mean ± population standard deviation over the size pool of each generator.
Parameter
DeepSeek
Gemini 3 Pro
GPT-5.1
Model
deepseek-reasoner
google/gemini-3-pro-preview
gpt-5.1
Maximum output tokens
10,000
10,000
10,000
Temperature
1.0
1.0
1.0
Frequency penalty
0.0
0.0
0.0
Presence penalty
0.0
0.0
0.0
Total input tokens
∼ 26 M
∼ 19 M
∼ 28 M
Appendix
Table 22: LLM API settings and estimated token costs used for all SAGE experiments. Costs use Jan 2026 OpenRouter list prices. The Gemini 3 Pro API identifier and pricing follow OpenRouter OpenRouter (2025) .
Large language models (LLMs) can generate syntactically valid optimization programs, yet often struggle to reliably choose an effective modeling strategy, leading to incorrect formulations and inefficient solver behavior. We propose SAGE, a strategy-aware framework that makes Modeling Strategy explicit in both data construction and post-training. SAGE builds a solver-verified multi-strategy dataset and trains a student model with supervised fine-tuning followed by Segment-Weighted GRPO using a composite reward over format compliance, correctness, and solver efficiency. Across eight benchmarks spanning synthetic and real-world settings, SAGE improves average pass@1 from 72.7 to 80.3 over the strongest open-source baseline. With multiple generations, SAGE discovers more distinct correct formulations and improves component-level diversity at pass@16 by 19-29%. At the largest scale, SAGE produces more compact constraint systems with 14.2% fewer constraints than the baseline, consistent with solver-efficient modeling. Overall, these results show that making Modeling Strategy explicit improves automated optimization modeling. Code is available at https://github.com/rachhhhing/SAGE.
Ruiqing Zhao, Fengzhi Li, Yuan Zuo +5
School of Computer Science and Engineering, Beihang University, Beijing, China · JIUTIAN Research, Beijing, China · MIIT Key Laboratory of Data and Decision Intelligence, Beihang University, Beijing, China
Mixed-integer programming (MIP) lies at the core of operations research and industrial optimization. While large language models (LLMs) have recently shown promise in automated MIP modeling from natural language, they prioritize semantic correctness but overlook formulation strength, severely bottlenecking the efficiency of downstream solvers. We propose FormuEvo, an LLM-guided evolutionary framework for automated discovery of solver-efficient MIP formulations. FormuEvo frames MIP formulation design as evolutionary optimization over the symbolic space of MIP formulations, represented as executable modeling programs, by iteratively generating, evaluating, and selecting stronger candidates via LLM-driven crossover, mutation, and repair operations. To move beyond blind exploration, FormuEvo introduces a solver-informed diagnosis mechanism that exploits fine-grained solver statistics as verbal gradients for targeted refinement. Additionally, a structured memory abstracts prior experience into reusable modeling strategies, avoiding redundant exploration while enabling zero-shot transfer to unseen problems and bootstrapping smaller LLMs. Experiments across diverse linear and non-linear problems demonstrate that FormuEvo discovers formulations that significantly outperform both expert-designed formulations and existing LLM-based approaches, accelerating solvers by up to 5.5×, with distilled knowledge transferring effectively across problems and model scales.
Haofeng Yuan, Jianing Peng, Jieyi Bi +3
Department of Automation, BNRist, Tsinghua University · College of Computing and Data Science, Nanyang Technological University · School of Computing and Information Systems, Singapore Management University
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making. Owing to their NP-hardness, however, modern solvers may struggle to find high-quality solutions for challenging MILP instances within practical time limits. Recent learning-based approaches seek to accelerate MILP solving by directly predicting high-quality solutions from static instance-level features, such as variable-constraint bipartite graphs. Yet accurate solution prediction from instance features alone is difficult, and these methods largely overlook the information revealed during the solver's search process. In this paper, we find that solutions produced at the early search stage of MILP solvers, which are computationally cheap to obtain, are often structurally close to the solutions found after full-budget search. Motivated by this observation, we propose a new solver-informed paradigm that shifts the learning target from variable assignment to early-to-final consistency: for each variable, we predict whether its early-stage assignment should persist in full-budget solutions. The predicted consistency naturally guides downstream search, for instance by fixing the assignments deemed consistent. At inference time, we further ensemble consistency predictions across multiple early-stage solutions to improve robustness. Experiments across four MILP benchmarks show our method improves prediction-guided search across diverse downstream pipelines. With Gurobi, our proposed method reduces the primal gap by 56.9% on average and closes it completely on combinatorial auction instances. Besides, we transferred the Gurobi-trained model zero-shot to SCIP without adaptation, achieving a 36.4% average gap reduction across benchmarks.
Guanlin Li, Chengrui Gao, Chenguang Wang +6
State Key Laboratory of Novel SoftwareTechnology, Nanjing University · School of Artificial Intelligence,Nanjing University · State Key Laboratory of Technology and Equipment for Defense Against Power System Operational Risks, Nari Technology Co., Ltd.