no code implementations • 7 Sep 2022 • Guillermo Angeris, Tarun Chitra, Alex Evans, Matthew Lorig
When asset prices can jump and the volatility process is independent of the underlying risky assets, we derive an explicit replication strategy for the short side of a perpetual contract.
no code implementations • 26 Nov 2021 • Guillermo Angeris, Alex Evans, Tarun Chitra
In this paper, we show that any monotonic payoff can be replicated using only liquidity provider shares in constant function market makers (CFMMs), without the need for additional collateral or oracles.
no code implementations • 1 Apr 2021 • Alex Evans, Guillermo Angeris, Tarun Chitra
Trading fees have been proposed as a mechanism for compensating LPs for arbitrage losses.
no code implementations • 26 Mar 2021 • Guillermo Angeris, Alex Evans, Tarun Chitra
We present a method for constructing Constant Function Market Makers (CFMMs) whose portfolio value functions match a desired payoff.
no code implementations • 15 Dec 2020 • Guillermo Angeris, Alex Evans, Tarun Chitra
We show that this definition is tightly related to the curvature of a CFMM's trading function and can be used to explain a number of heuristic results.
1 code implementation • 18 May 2020 • Shane Barratt, Guillermo Angeris, Stephen Boyd
We consider the problem of assigning weights to a set of samples or data records, with the goal of achieving a representative weighting, which happens when certain sample averages of the data are close to prescribed values.
no code implementations • 22 Mar 2020 • Guillermo Angeris, Tarun Chitra
Automated market makers, first popularized by Hanson's logarithmic market scoring rule (or LMSR) for prediction markets, have become important building blocks, called 'primitives,' for decentralized finance.
1 code implementation • 1 Mar 2020 • Rahul Trivedi, Guillermo Angeris, Logan Su, Stephen Boyd, Shanhui Fan, Jelena Vuckovic
We illustrate our bounding procedure by studying limits on the scattering cross-sections of dielectric and metallic particles in the absence of material losses.
Optics
1 code implementation • 13 Feb 2020 • Guillermo Angeris, Jelena Vučković, Stephen Boyd
In a physical design problem, the designer chooses values of some physical parameters, within limits, to optimize the resulting field.
Optimization and Control Computational Physics Optics
1 code implementation • 29 Jan 2020 • Shane Barratt, Guillermo Angeris, Stephen Boyd
Given an infeasible, unbounded, or pathological convex optimization problem, a natural question to ask is: what is the smallest change we can make to the problem's parameters such that the problem becomes solvable?
Optimization and Control
no code implementations • 8 Nov 2019 • Guillermo Angeris, Hsien-Tang Kao, Rei Chiang, Charlie Noyes, Tarun Chitra
Uniswap -- and other constant product markets -- appear to work well in practice despite their simplicity.
1 code implementation • 27 Oct 2019 • Shane Barratt, Guillermo Angeris, Stephen Boyd
We consider the problem of minimizing a sum of clipped convex functions; applications include clipped empirical risk minimization and clipped control.
1 code implementation • 30 May 2019 • Guillermo Angeris, Kunal Shah, Mac Schwager
We present a fully distributed collision avoidance algorithm based on convex optimization for a team of mobile robots.
Optimization and Control Robotics
1 code implementation • 30 Nov 2018 • Guillermo Angeris, Jelena Vuckovic, Stephen Boyd
Physical design problems, such as photonic inverse design, are typically solved using local optimization methods.
Optics Optimization and Control Computational Physics
