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xquad is a toolkit for defining optimization problems once and running them on different solvers. A high-level programming interface describes constraints declaratively, and those constraints compile into a shared intermediate form and a human-readable assembly format that a compact stack-based virtual machine can execute. The same problem definition can then be lowered to quantum annealers or to classical CPU and GPU solvers without being rewritten.
The main features of quipnetwork/xquad are: Quantum Annealing Tools, Solver-Agnostic Optimization Interfaces, Optimization Constraint Modeling Interfaces, Embedded Virtual Machines, Stack-Based Virtual Machines, Cross-Solver Problem Compilers, Problem Modeling, Solver Dispatchers.
Projects with overlapping indexed features include: cvxpy/cvxpy — CVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using… robotlocomotion/drake — Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and… julianlsolvers/optim.jl — Optim.jl is a numerical optimization library for the Julia programming language, providing a comprehensive framework… morluto/rea — Rea is a reverse engineering toolkit that lets AI agents analyze software through a local Model Context Protocol… hyperdbg/hyperdbg — HyperDbg is a hardware-assisted kernel-mode debugging platform that leverages virtualization to monitor and control… munificent/craftinginterpreters — Crafting Interpreters is a comprehensive resource for building a complete programming language from scratch. It…
CVXPY is a Python-embedded domain-specific language for modeling and solving convex optimization problems using natural mathematical syntax. It is built on a disciplined convex programming framework that automatically enforces convexity rules, ensuring that problems formulated by the user are valid for convex solvers. The project also functions as a multi-solver optimization interface, abstracting away backend details and dispatching problems to specialized solvers like ECOS, SCS, and Gurobi without manual configuration. Beyond standard convex optimization, CVXPY extends its reach to geometri
Drake is a robotics simulation framework and control system modeling tool used for designing, simulating, and verifying the dynamics of complex robotic systems. It functions as a multibody dynamics simulator and a mathematical optimization library, providing a suite of algorithms for trajectory optimization and the simulation of articulated robots. The framework is distinguished by its block-diagram system for composing dynamical subsystems and its ability to formulate and solve diverse mathematical programs, including linear, quadratic, and nonconvex nonlinear problems. It supports specializ
Optim.jl is a numerical optimization library for the Julia programming language, providing a comprehensive framework for minimizing or maximizing univariate and multivariate functions. It offers a suite of tools for solving both constrained and unconstrained mathematical problems, utilizing a variety of gradient-based, derivative-free, and stochastic search methods. The library distinguishes itself through a modular architecture that leverages language-level multiple dispatch to automatically select efficient solvers based on input data types and objective function properties. It supports com
Rea is a reverse engineering toolkit that lets AI agents analyze software through a local Model Context Protocol server. An agent can investigate how an application works, from observed behavior and web traffic down to native binaries, and return findings with supporting evidence and stated limitations. All analysis runs on the user's own machine, so the inspected software is not uploaded and only tool results are passed to the agent. For native code, Rea drives an installed disassembler or decompiler such as Hopper, Ghidra, IDA, or Kuna to produce pseudocode, assembly, strings, symbols, and