133 citations · 204 across the 12 of their papers we have counts for
15 papers
A novel portfolio construction strategy based on the core-periphery profile of stocks
Imran Ansari, Charu Sharma, Akshay Agrawal +1
This paper highlights the significance of mesoscale structures, particularly the core-periphery structure, in financial networks for portfolio optimization. We build portfolios of…
Joint Graph Learning and Model Fitting in Laplacian Regularized Stratified Models
Ziheng Cheng, Junzi Zhang, Akshay Agrawal +1
Laplacian regularized stratified models (LRSM) are models that utilize the explicit or implicit network structure of the sub-problems as defined by the categorical features called…
Embedded Code Generation with CVXPY
Maximilian Schaller, Goran Banjac, Steven Diamond +3
We introduce CVXPYgen, a tool for generating custom C code, suitable for embedded applications, that solves a parametrized class of convex optimization problems. CVXPYgen is based…
Computing Tighter Bounds on the -Queens Constant via Newton's Method
Parth Nobel, Akshay Agrawal, Stephen Boyd
In recent work Simkin shows that bounds on an exponent occurring in the famous -queens problem can be evaluated by solving convex optimization problems, allowing him to find bou…
Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP
Deepak Narayanan, Fiodar Kazhamiaka, Firas Abuzaid +5
Resource allocation problems in many computer systems can be formulated as mathematical optimization problems. However, finding exact solutions to these problems using off-the-shel…
Constant Function Market Makers: Multi-Asset Trades via Convex Optimization
Guillermo Angeris, Akshay Agrawal, Alex Evans +2
The rise of Ethereum and other blockchains that support smart contracts has led to the creation of decentralized exchanges (DEXs), such as Uniswap, Balancer, Curve, mStable, and Su…
Learning Convex Optimization Models
Akshay Agrawal, Shane Barratt, Stephen Boyd
A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cas…
Differentiating through Log-Log Convex Programs
Akshay Agrawal, Stephen Boyd
We show how to efficiently compute the derivative (when it exists) of the solution map of log-log convex programs (LLCPs). These are nonconvex, nonsmooth optimization problems with…
Learning Convex Optimization Control Policies
Akshay Agrawal, Shane Barratt, Stephen Boyd +1
Many control policies used in various applications determine the input or action by solving a convex optimization problem that depends on the current state and some parameters. Com…
Differentiable Convex Optimization Layers
Akshay Agrawal, Brandon Amos, Shane Barratt +3
Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectur…
Disciplined Quasiconvex Programming
Akshay Agrawal, Stephen Boyd
We present a composition rule involving quasiconvex functions that generalizes the classical composition rule for convex functions. This rule complements well-known rules for the c…
Differentiating Through a Cone Program
Akshay Agrawal, Shane Barratt, Stephen Boyd +2
We consider the problem of efficiently computing the derivative of the solution map of a convex cone program, when it exists. We do this by implicitly differentiating the residual…
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
Akshay Agrawal, Akshay Naresh Modi, Alexandre Passos +8
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. Tensor…
Disciplined Geometric Programming
Akshay Agrawal, Steven Diamond, Stephen Boyd
We introduce log-log convex programs, which are optimization problems with positive variables that become convex when the variables, objective functions, and constraint functions a…
A Rewriting System for Convex Optimization Problems
Akshay Agrawal, Robin Verschueren, Steven Diamond +1
We describe a modular rewriting system for translating optimization problems written in a domain-specific language to forms compatible with low-level solver interfaces. Translation…