angeris

/*
 * I threw together this set of classes to work on Hackerrank's stuff at a fairly low level. Of 
 * course, you are provided with several libraries to do this for you, such as Weka, which makes
 * life quite a bit easier than setting up your own feature matrix. But for those who want to
 * have the simple stuff such as naive QR using Gram-Schmidt, back-substitution, and school-book
 * matrix multiplication, this will work. Even for fairly large datasets within Hackerrank
 * the performance is fast enough to give a very wide margin of extra time. Enjoy!
 * 
 * Guille (@Guillean)
 */

angeris

from numpy import *

N, L = 1e8, 10

print("Here's your crappy estimation of pi: {}".format((sum(exp(-(random.rand(N)*2*L-L)**2))*2*L/N)**2))

angeris

# Author: Guillermo Angeris (@guillean)
# Simple UnionFind based on https://www.cs.princeton.edu/~rs/AlgsDS07/01UnionFind.pdf

class UnionFind:
    def __init__(self):
        self.rank = {}
        self.parent = {}

    def add(self, elem):
        self.rank[elem] = 1

angeris

Initial positions

Player A:
[0-5] [1-3] [1-4] [2-2] [3-4] [4-4] [5-6]
Player B:
[0-0] [0-1] [0-6] [1-1] [1-6] [2-3] [2-6]
Player C:
[0-2] [2-4] [2-5] [3-3] [3-6] [4-5] [4-6]
Player D:
[0-3] [0-4] [1-2] [1-5] [3-5] [5-5] [6-6]

angeris

import numpy as np

X = np.array([
                [1, 4],
                [20, 35],
                [8, 15]
             ])
y = np.array([3, 15, 7])

opt_theta = np.linalg.lstsq(X, y)

angeris

using JuMP
using ProgressMeter
using LinearAlgebra
using SparseArrays
import Gurobi

# Formulates ‖x‖² ≤ y as an SOC
function quad_cons(m, x, y)
    @constraint(m, sum(x.^2) ≤ y )
end

angeris

using JuMP
using LinearAlgebra
using SparseArrays

# Formulates ‖x‖² ≤ y as an SOC
function quad_cons(m, x, y)
    @constraint(m, sum(x.^2) ≤ y )
end

const t_min = 1

angeris

using JuMP
using LinearAlgebra
using BenchmarkTools
using ProgressMeter
import Random
import SCS

Random.seed!(1234)

function randn_symm(n)

angeris

Keybase proof

I hereby claim:

• I am angeris on github.
• I am angeris (https://keybase.io/angeris) on keybase.
• I have a public key ASDT_GHDJohjVhunI0yCpEHqZH_yvzT0KEkNhjkOXA1-LQo

To claim this, I am signing this object:

angeris

import numpy as np
import cvxpy as cp
import matplotlib.pyplot as plt

# The idea is that we have two circles, in opposite corners
# that need to move past each other during optimization (in order to minimize
# some simple objective function). The important thing to guarantee
# is that they do not intersect at any point during any iteration.

# Initial/desired positions of circles, radius = 1/4.