Indigenous GPU-Accelerated Optimization Solver (Sovereign Alternative to Express / CEPLEX)
“We are building a 'mathematical engine' that helps industries like oil refineries make the best possible decisions, such as how to blend chemicals or schedule production. Currently, India relies on expensive foreign software for this, so we are creating a homegrown, high-performance alternative that is faster, cheaper, and fully under our control.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
A high-performance mathematical optimization solver core (a computational engine) capable of solving complex Linear, Mixed-Integer, and Quadratic programming problems.
WHO IS IT FOR?
Primary users are industrial engineers, data scientists, and operations researchers in sectors like MRPL, power grids, logistics, and manufacturing.
WHY DOES IT EXIST?
India is currently dependent on expensive, proprietary foreign software (like CPLEX/Gurobi). This creates a 'black box' risk where we cannot modify the code for national strategic needs, and we face high recurring licensing costs.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Mathematical models in standard formats (MPS, LP files) representing industrial constraints and objective functions.
Implementation of Simplex/Interior-point methods, Branch-and-Cut algorithms, sparse matrix factorization, and GPU-accelerated parallel processing.
Optimal variable values (the solution), objective function value, and solver status (optimal, infeasible, or unbounded).
CONSTRAINTS
- Must be built from scratch (no wrapping existing open-source solvers like CBC).
- Must handle large-scale, sparse, and ill-conditioned industrial matrices.
- Must demonstrate numerical stability and convergence on standard benchmarks (MIPLIB).
- Must support multi-core and GPU acceleration.
JURY METRICS
- Computational time (latency) compared to commercial solvers.
- Solution quality (optimality gap) on standard benchmarks.
- Numerical stability on ill-conditioned matrices.
- Memory efficiency for large-scale datasets.
CORE BOTTLENECK
Numerical stability. Floating-point errors in large-scale matrix inversions and the 'curse of dimensionality' in branch-and-bound trees will cause most teams' solvers to crash or fail to converge on real-world industrial problems.
Why You Should Choose This PS (Pros)
- High strategic national importance (Sovereignty).
- Clear, objective performance metrics (benchmarks).
- Massive potential for industry adoption if successful.
- Strong 'wow' factor for judges due to the depth of mathematical engineering required.
Challenges & Risks to Watch (Cons)
- Extremely high barrier to entry regarding mathematical knowledge.
- Debugging optimization algorithms is notoriously difficult.
- Performance optimization requires deep knowledge of hardware architecture (CPU/GPU cache lines).
Recommended Tech Stack & Grand Finale Features
Killer Features to Impress the SIH Jury
View Official SIH 2026 Portal Raw Text▼
The scope is to solve optimization problems arising from refinery scheduling, crude blending, process optimization, production planning, logistics, power system dispatch, transportation and supply chain management. The benchmark is that the solver should consistently deliver optimal or near-optimal solutions for industrial-scale problems involving thousands to millions of variables and constraints, including highly degenerate models, ill-conditioned matrices and difficult mixed-integer formulations where weaker implementations exhibit excessive computation times or fail to converge.